social group hypothesis

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Social Groups

Learning objectives.

• Describe how a social group differs from a social category or social aggregate.
• Distinguish a primary group from a secondary group.
• Define a reference group and provide one example of such a group.
• Explain the importance of networks in a modern society.

Most of us feel comfortable using the word “group” without giving it much thought. In everyday use, it can be a generic term, although it carries important clinical and scientific meanings. Moreover, the concept of a group is central to much of how we think about society and human interaction. Often, we might mean different things by using that word. We might say that a group of kids all saw the dog, and it could mean 250 students in a lecture hall or four siblings playing on a front lawn. In everyday conversation, there isn’t a clear distinguishing use. So how can we hone the meaning more precisely for sociological purposes?

Defining a Group

The term  group   is an amorphous one and can refer to a wide variety of gatherings, from just two people (think about a “group project” in school when you partner with another student), a club, a regular gathering of friends, or people who work together or share a hobby. In short, the term refers to any collection of at least two people who interact with some frequency and who share a sense that their identity is somehow aligned with the group. Of course, every time people are gathered it is not necessarily a group. A rally is usually a one-time event, for instance, and belonging to a political party doesn’t imply interaction with others. People who exist in the same place at the same time but who do not interact or share a sense of identity—such as a bunch of people standing in line at Starbucks—are considered an  aggregate , or a crowd. Another example of a nongroup is people who share similar characteristics but are not tied to one another in any way. These people are considered a  category , and as an example all children born from approximately 1980–2000 are referred to as “Millennials.” Why are Millennials a category and not a group? Because while some of them may share a sense of identity, they do not, as a whole, interact frequently with each other.

Interestingly, people within an aggregate or category can become a group. During disasters, people in a neighborhood (an aggregate) who did not know each other might become friendly and depend on each other at the local shelter. After the disaster ends and the people go back to simply living near each other, the feeling of cohesiveness may last since they have all shared an experience. They might remain a group, practicing emergency readiness, coordinating supplies for next time, or taking turns caring for neighbors who need extra help. Similarly, there may be many groups within a single category. Consider teachers, for example. Within this category, groups may exist like teachers’ unions, teachers who coach, or staff members who are involved with the PTA.

Types of Groups

Sociologist Charles Horton Cooley (1864–1929) suggested that groups can broadly be divided into two categories:  primary groups and  secondary groups  (Cooley 1909). According to Cooley, primary groups play the most critical role in our lives. The primary group is usually fairly small and is made up of individuals who generally engage face-to-face in long-term emotional ways. This group serves emotional needs:  expressive functions  rather than pragmatic ones. The primary group is usually made up of significant others, those individuals who have the most impact on our socialization. The best example of a primary group is the family.

Secondary groups are often larger and impersonal. They may also be task-focused and time-limited. These groups serve an  instrumental function  rather than an expressive one, meaning that their role is more goal- or task-oriented than emotional. A classroom or office can be an example of a secondary group. Neither primary nor secondary groups are bound by strict definitions or set limits. In fact, people can move from one group to another. A graduate seminar, for example, can start as a secondary group focused on the class at hand, but as the students work together throughout their program, they may find common interests and strong ties that transform them into a primary group.

SOCIOLOGY IN THE REAL WORLD

Best friends she’s never met.

Writer Allison Levy worked alone. While she liked the freedom and flexibility of working from home, she sometimes missed having a community of coworkers, both for the practical purpose of brainstorming and the more social “water cooler” aspect. Levy did what many do in the Internet age: she found a group of other writers online through a web forum. Over time, a group of approximately twenty writers, who all wrote for a similar audience, broke off from the larger forum and started a private invitation-only forum. While writers in general represent all genders, ages, and interests, it ended up being a collection of twenty- and thirty-something women who comprised the new forum; they all wrote fiction for children and young adults.

At first, the writers’ forum was clearly a secondary group united by the members’ professions and work situations. As Levy explained, “On the Internet, you can be present or absent as often as you want. No one is expecting you to show up.” It was a useful place to research information about different publishers and about who had recently sold what and to track industry trends. But as time passed, Levy found it served a different purpose. Since the group shared other characteristics beyond their writing (such as age and gender), the online conversation naturally turned to matters such as child-rearing, aging parents, health, and exercise. Levy found it was a sympathetic place to talk about any number of subjects, not just writing. Further, when people didn’t post for several days, others expressed concern, asking whether anyone had heard from the missing writers. It reached a point where most members would tell the group if they were traveling or needed to be offline for awhile.

The group continued to share. One member on the site who was going through a difficult family illness wrote, “I don’t know where I’d be without you women. It is so great to have a place to vent that I know isn’t hurting anyone.” Others shared similar sentiments.

So is this a primary group? Most of these people have never met each other. They live in Hawaii, Australia, Minnesota, and across the world. They may never meet. Levy wrote recently to the group, saying, “Most of my ‘real-life’ friends and even my husband don’t really get the writing thing. I don’t know what I’d do without you.” Despite the distance and the lack of physical contact, the group clearly fills an expressive need.

In-Groups and Out-Groups

One of the ways that groups can be powerful is through inclusion, and its inverse, exclusion. The feeling that we belong in an elite or select group is a heady one, while the feeling of not being allowed in, or of being in competition with a group, can be motivating in a different way. Sociologist William Sumner (1840–1910) developed the concepts of in-group  and  out-group to explain this phenomenon (Sumner 1906). In short, an in-group is the group that an individual feels she belongs to, and she believes it to be an integral part of who she is. An out-group , conversely, is a group someone doesn’t belong to; often we may feel disdain or competition in relationship to an out-group. Sports teams, unions, and sororities are examples of in-groups and out-groups; people may belong to, or be an outsider to, any of these. Primary groups consist of both in-groups and out-groups, as do secondary groups.

While group affiliations can be neutral or even positive, such as the case of a team sport competition, the concept of in-groups and out-groups can also explain some negative human behavior, such as white supremacist movements like the Ku Klux Klan, or the bullying of gay or lesbian students. By defining others as “not like us” and inferior, in-groups can end up practicing ethnocentrism, racism, sexism, ageism, and heterosexism—manners of judging others negatively based on their culture, race, sex, age, or sexuality. Often, in-groups can form within a secondary group. For instance, a workplace can have cliques of people, from senior executives who play golf together, to engineers who write code together, to young singles who socialize after hours. While these in-groups might show favoritism and affinity for other in-group members, the overall organization may be unable or unwilling to acknowledge it. Therefore, it pays to be wary of the politics of in-groups, since members may exclude others as a form of gaining status within the group.

BIG PICTURE

Bullying and cyberbullying: how technology has changed the game.

Most of us know that the old rhyme “sticks and stones may break my bones, but words will never hurt me” is inaccurate. Words can hurt, and never is that more apparent than in instances of bullying. Bullying has always existed and has often reached extreme levels of cruelty in children and young adults. People at these stages of life are especially vulnerable to others’ opinions of them, and they’re deeply invested in their peer groups. Today, technology has ushered in a new era of this dynamic. Cyberbullying is the use of interactive media by one person to torment another, and it is on the rise. Cyberbullying can mean sending threatening texts, harassing someone in a public forum (such as Facebook), hacking someone’s account and pretending to be him or her, posting embarrassing images online, and so on. A study by the Cyberbullying Research Center found that 20 percent of middle school students admitted to “seriously thinking about committing suicide” as a result of online bullying (Hinduja and Patchin 2010). Whereas bullying face-to-face requires willingness to interact with your victim, cyberbullying allows bullies to harass others from the privacy of their homes without witnessing the damage firsthand. This form of bullying is particularly dangerous because it’s widely accessible and therefore easier to accomplish.

Cyberbullying, and bullying in general, made international headlines in 2010 when a fifteen-year-old girl, Phoebe Prince, in South Hadley, Massachusetts, committed suicide after being relentlessly bullied by girls at her school. In the aftermath of her death, the bullies were prosecuted in the legal system and the state passed anti-bullying legislation. This marked a significant change in how bullying, including cyberbullying, is viewed in the United States. Now there are numerous resources for schools, families, and communities to provide education and prevention on this issue. The White House hosted a Bullying Prevention summit in March 2011, and President and First Lady Obama have used Facebook and other social media sites to discuss the importance of the issue.

According to a report released in 2013 by the National Center for Educational Statistics, close to 1 in every 3 (27.8 percent) students report being bullied by their school peers. Seventeen percent of students reported being the victims of cyberbullying.

Will legislation change the behavior of would-be cyberbullies? That remains to be seen. But we can hope communities will work to protect victims before they feel they must resort to extreme measures.

Reference Groups

A  reference group is a group that people compare themselves to—it provides a standard of measurement. In U.S. society, peer groups are common reference groups. Kids and adults pay attention to what their peers wear, what music they like, what they do with their free time—and they compare themselves to what they see. Most people have more than one reference group, so a middle school boy might look not just at his classmates but also at his older brother’s friends and see a different set of norms. And he might observe the antics of his favorite athletes for yet another set of behaviors.

Some other examples of reference groups can be one’s cultural center, workplace, family gathering, and even parents. Often, reference groups convey competing messages. For instance, on television and in movies, young adults often have wonderful apartments and cars and lively social lives despite not holding a job. In music videos, young women might dance and sing in a sexually aggressive way that suggests experience beyond their years. At all ages, we use reference groups to help guide our behavior and show us social norms. So how important is it to surround yourself with positive reference groups? You may not recognize a reference group, but it still influences the way you act. Identifying your reference groups can help you understand the source of the social identities you aspire to or want to distance yourself from.

College: A World of In-Groups, Out-Groups, and Reference Groups

For a student entering college, the sociological study of groups takes on an immediate and practical meaning. After all, when we arrive someplace new, most of us glance around to see how well we fit in or stand out in the ways we want. This is a natural response to a reference group, and on a large campus, there can be many competing groups. Say you are a strong athlete who wants to play intramural sports, and your favorite musicians are a local punk band. You may find yourself engaged with two very different reference groups.

These reference groups can also become your in-groups or out-groups. For instance, different groups on campus might solicit you to join. Are there fraternities and sororities at your school? If so, chances are they will try to convince students—that is, students they deem worthy—to join them. And if you love playing soccer and want to play on a campus team, but you’re wearing shredded jeans, combat boots, and a local band T-shirt, you might have a hard time convincing the soccer team to give you a chance. While most campus groups refrain from insulting competing groups, there is a definite sense of an in-group versus an out-group. “Them?” a member might say. “They’re all right, but their parties are nowhere near as cool as ours.” Or, “Only serious engineering geeks join that group.” This immediate categorization into in-groups and out-groups means that students must choose carefully, since whatever group they associate with won’t just define their friends—it may also define their enemies.

Social Networks

These days in the job world we often hear of “networking,” or taking advantage of your connections with people who have connections to other people who can help you land a job. You do not necessarily know these “other people” who ultimately can help you, but you do know the people who know them. Your ties to the other people are weak or nonexistent, but your involvement in this network may nonetheless help you find a job.

Modern life is increasingly characterized by such social networks , or the totality of relationships that link us to other people and groups and through them to still other people and groups. Some of these relationships involve strong bonds, while other relationships involve weak bonds (Granovetter, 1983). Facebook and other Web sites have made possible networks of a size unimaginable just a decade ago. Social networks are important for many things, including getting advice, borrowing small amounts of money, and finding a job. When you need advice or want to borrow $5 or $10, to whom do you turn? The answer is undoubtedly certain members of your social networks—your friends, family, and so forth.

The indirect links you have to people through your social networks can help you find a job or even receive better medical care. For example, if you come down with a serious condition such as cancer, you would probably first talk with your primary care physician, who would refer you to one or more specialists whom you do not know and who have no connections to you through other people you know. That is, they are not part of your social network. Because the specialists do not know you and do not know anyone else who knows you, they are likely to treat you very professionally, which means, for better or worse, impersonally.

Gavin Llewellyn – My social networks – CC BY 2.0.

Now suppose you have some nearby friends or relatives who are physicians. Because of their connections with other nearby physicians, they can recommend certain specialists to you and perhaps even get you an earlier appointment than your primary physician could. Because these specialists realize you know physicians they know, they may treat you more personally than otherwise. In the long run, you may well get better medical care from your network through the physicians you know. People lucky enough to have such connections may thus be better off medically than people who do not.

But let’s look at this last sentence. What kinds of people have such connections? What kinds of people have friends or relatives who are physicians? All other things being equal, if you had two people standing before you, one employed as a vice president in a large corporation and the other working part time at a fast-food restaurant, which person do you think would be more likely to know a physician or two personally? Your answer is probably the corporate vice president. The point is that factors such as our social class and occupational status, our race and ethnicity, and our gender affect how likely we are to have social networks that can help us get jobs, good medical care, and other advantages. As just one example, a study of three working-class neighborhoods in New York City—one white, one African American, and one Latino—found that white youths were more involved through their parents and peers in job-referral networks than youths in the other two neighborhoods and thus were better able to find jobs, even if they had been arrested for delinquency (Sullivan, 1989). This study suggests that even if we look at people of different races and ethnicities in roughly the same social class, whites have an advantage over people of color in the employment world.

Gender also matters in the employment world. In many businesses, there still exists an “old boys’ network,” in which male executives with job openings hear about male applicants from male colleagues and friends. Male employees already on the job tend to spend more social time with their male bosses than do their female counterparts. These related processes make it more difficult for females than for males to be hired and promoted (Barreto, Ryan, & Schmitt, 2009). To counter these effects and to help support each other, some women form networks where they meet, talk about mutual problems, and discuss ways of dealing with these problems. An example of such a network is The Links, Inc., a community service group of 12,000 professional African American women whose name underscores the importance of networking ( http://www.linksinc.org/index.shtml ). Its members participate in 270 chapters in 42 states; Washington, DC; and the Bahamas. Every two years, more than 2,000 Links members convene for a national assembly at which they network, discuss the problems they face as professional women of color, and consider fund-raising strategies for the causes they support.

Key Takeaways

• Groups are a key building block of social life but can also have negative consequences.
• Primary groups are generally small and include intimate relationships, while secondary groups are larger and more impersonal.
• Reference groups provide a standard for guiding and evaluating our attitudes and behaviors.
• Social networks are increasingly important in modern life, and involvement in such networks may have favorable consequences for many aspects of one’s life.

Barreto, M., Ryan, M. K., & Schmitt, M. T. (Eds.). (2009). The glass ceiling in the 21st century: Understanding barriers to gender equality . Washington, DC: American Psychological Association.

Elsesser, K., & Peplau L. A. (2006). The glass partition: Obstacles to cross-sex friendships at work. Human Relations, 59 , 1077–1100.

Gosselin, D. K. (2010). Heavy hands: An introduction to the crimes of family violence (4th ed.). Upper Saddle River, NJ: Prentice Hall.

Granovetter, M. (1983). The strength of weak ties: A network theory revisited. Sociological Theory, 1, 201–233.

Maimon, D., & Kuhl, D. C. (2008). Social control and youth suicidality: Situating Durkheim’s ideas in a multilevel framework. American Sociological Review, 73, 921–943.

Marks, S. R. (1994). Intimacy in the public realm: The case of co-workers. Social Forces, 72, 843–858.

Olzak, S. (1992). The dynamics of ethnic competition and conflict . Stanford, CA: Stanford University Press.

Stouffer, S. A., Suchman, E. A., DeVinney, L. C., Star, S. A., & Williams, R. M., Jr. (1949). The American soldier: Adjustment during army life (Studies in Social Psychology in World War II, Vol. 1). Princeton, NJ: Princeton University Press.

Sullivan, M. (1989). Getting paid: Youth crime and work in the inner city . Ithaca, NY: Cornell University Press.

Introduction to Sociology: Understanding and Changing the Social World Copyright © 2016 by University of Minnesota is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Allport’s Intergroup Contact Hypothesis: Its History and Influence

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, Ph.D., is a qualified psychology teacher with over 18 years experience of working in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

On This Page:

Contact hypothesis was proposed by Gordon Allport (1897-1967) and states that social contact between social groups is sufficient to reduce intergroup prejudice.

However, empirical evidence suggests that this is only in certain circumstances.

Key Takeaways:

• The contact hypothesis fundamentally rests on the idea that ingroups who have more interactions with a certain outgroup tend to develop more positive perceptions and fewer negative perceptions of that outgroup.
• Theorists have long been interested in intergroup conflict . However, Robin Williams and Gordon Allport proposed a number of conditions for ameliorating intergroup conflict that has formed the basis of empirical research for several decades.
• Allport suggests four “positive factors” leading to better intergroup relations; however, recent research suggests that these factors can facilitate but are not necessary for reducing intergroup prejudice.
• Although originally studied in the context of race and ethnic relations, the contact hypothesis has applicability between ingroup-outgroup relations across religion, age, sexuality, disease status, economic circumstances, and so on.

Contact Hypothesis

The Contact Hypothesis is a psychological theory that suggests that direct contact between members of different social or cultural groups can reduce prejudice, improve intergroup relations, and promote mutual understanding.

According to this hypothesis, interpersonal contact can lead to positive attitudes, decreased stereotypes, and increased acceptance between individuals from different groups under certain conditions.

The Contact Hypothesis was first proposed by Gordon W. Allport in 1954 and has since been supported by numerous studies in the field of social psychology. T

The contact theory suggests that contact between groups is more likely to be effective in reducing prejudice and improving relations if it meets specific criteria:

1. Equal Status Between Groups

Members of the contact situation should not have an unequal, hierarchical relationship (e.g., teacher/student, employer/employee).

Both groups perceive the other to be of equal status in the situation (Cohen, 1982; Riordan and Ruggiero, 1980; Pettigrew and Tropp, 2005).

Although some scholars emphasize that groups should be of equal status both prior to (Brewer and Kramer, 1985) and during (Foster and Finchilescu, 1986) a contact situation, research demonstrated that equal status could promote positive intergroup attitudes even when the groups initially differ in status (Patchen, 1982; Schofield and Rich-Fulcher, 2001).

2. Common Goals

Members must rely on each other to achieve their shared desired goal. To have effective contact, typically, groups need to be making an active effort toward a goal that the groups share.

For example, a national football team (Chu and Griffey, 1985; Patchen, 1982) could draw from many people of different races and ethnic origins — people from different groups — in working together and replying to each other to achieve their shared goals of winning. This tends to lead to Allport’s third characteristic of intergroup contact; intergroup cooperation (Pettigrew and Tropp, 2005).

3. Intergroup Cooperation

Members should work together in a non-competitive environment.

According to Allport (1954), the attainment of these common goals must be based on cooperation over competition. For example, in Sheriff et al. ‘s (1961) Robbers’ Cave field study , researchers devised barriers to common goals, such as a planned picnic that could only be resolved with cooperation between both groups.

This intergroup cooperation encourages positive relations between the groups. Another instance of intergroup cooperation has been studied in schools (e.g., Brewer and Miller, 1984; Johnson, Johnson, and Maruyama, 1984; Schofield, 1986).

For example, Elliot Aronson developed a “jigsaw” approach such that students from diverse backgrounds work toward common goals, fostering positive relationships among children worldwide (Aronson, 2002).

4. The Support of Authorities, Law, or Custom

The support of authorities, law, and customs also tend to lead to more positive intergroup contact effects because authorities can establish norms of acceptance and guidelines for how group members should interact with each other.

There should not be official laws enforcing segregation. This importance has been demonstrated in such wide-ranging circumstances as the military (Landis, Hope, and Day, 1983), business (Morrison and Herlihy, 1992), and religion (Parker, 1968).

Legislation, such as the civil-rights acts in American society, can also be instrumental in establishing anti-prejudicial norms (Pettigrew and Tropp, 2005).

5. Positive Contact Norms

The belief in positive contact norms refers to the understanding that positive interactions between individuals from different groups are the norm and valued by society.

When individuals perceive that positive contact is socially accepted and encouraged, it can increase the effectiveness of intergroup contact.

6. Personal Accountability

A belief in personal accountability for one’s actions and attitudes is important for effective intergroup contact.

Taking responsibility for one’s biases, stereotypes, and prejudices and actively working towards changing them, can contribute to positive intergroup relationships.

7. Empathy and Perspective-Taking

The belief in the importance of empathy and perspective-taking can enhance intergroup contact.

When individuals try to understand and empathize with the experiences and perspectives of members from other groups, it can lead to greater mutual understanding and positive relationships.

Why Does Contact Reduce Prejudice?

Brewer and Miller (1996) and Brewer and Brown (1998) suggest that these conditions can be viewed as an application of dissonance theory (Festinger, 1957).

Specifically, when individuals with negative attitudes toward specific groups find themselves in situations in which they engage in positive social interactions with members of those groups, their behavior is inconsistent with their attitudes.

This dissonance, it is theorized, may result in a change of attitude to justify the new behavior if the situation is structured so as to satisfy the above four conditions.

In contrast, Forbes (1997) asserts that most social scientists implicitly assume that increased interracial/ethnic contact reduces tension between groups by giving each information about the other.

Those who write, adopt, participate in or evaluate prejudice reduction programs are likely to have explicit or implicit informal theories about how prejudice reduction programs work.

Examples of Contact Hypothesis

Homelessness.

Historically, in contact hypothesis research, racial and ethnic minorities have been the out-group of choice; however, the hypothesis can extend to out-groups created by a number of factors. One such alienating situation is homelessness.

Like many out-groups, homeless people are more visible than they once were because of their growth in number as well as extensive media and policy coverage.

This has elicited a large amount of stigmatization and associations between homelessness and poor physical and mental health, substance abuse, and criminality, and ethnographic studies have revealed that homeless people are regularly degraded, avoided, or treated as non-persons by passersby (Anderson, Snow, and Cress, 1994).

Lee, Farrell, and Link (2004) used data from a national survey of public attitudes toward homeless people to evaluate the applicability of the contact hypothesis to relationships between homeless and housed people, even in the absence of Allport’s four positive factors.

The researchers found that even taking selection and social desirability biases into account, general exposure to homeless people tended to affect public attitudes toward homeless people favorably (Lee, Farrell, and Link, 2004).

Contact Between Age Groups

In the 1980s, there was a trend of pervasive age segregation in American society, with children and adults tending to pursue their own separate and independent lives (Caspi, 1984).

This had consequences such as a lack of transmission of work skills and culture, poor preparation for parenthood, and generally inaccurate stereotypes and unfavorable attitudes toward other age groups.

Caspi (1984) assessed the effects of cross-age contact on the attitudes of children toward older adults by comparing children attending an age-integrated preschool to children attending a traditional preschool.

Those in the age-integrated preschool (having daily contact with older adults) tended to hold positive attitudes toward older adults, while those without such contact tended to hold vague or indifferent attitudes.

In addition, children placed in the age-integrated preschool show better differentiation between adult age groups than those not in that preschool.

These findings were among the first to suggest that Allport’s contact hypothesis held relevance in intergroup contact beyond race relations (Caspi, 1984).

Contact Between Religious Groups in Indonesia and the Philippines

Following a resurgence of religion-related conflict and religiously motivated intolerance and violence and the 1999-2002 outbreak of sectarian violence in Ambon, Indonesia, between Christians and Muslims, researchers have become motivated to find ways to reduce acts of religiously motivated intolerance.

Kanas, Scheepers, and Sterkens (2015) examined the relationship between interreligious contact and negative attitudes toward religious out-groups by conducting surveys of Christian and Muslim students in Indonesia and the Philippines.

They attempted to answer the following questions (Kanas, Sccheeepers, and Sterkens, 2015):

• Does positive interreligious contact reduce, while negative interreligious contact induces negative attitudes towards the religious out-group?
• Does the perception of group threat provide a valid mechanism for both the positive and negative effects of interreligious contact?
• Does positive interreligious contact reduce negative out-group attitudes when intergroup relations are tense and both groups experience extreme conflict and violence?

The researchers focused on four ethnically and religiously diverse regions of Indonesia and the Philippines: Maluku and Yogyakarta, the Autonomous Region in Muslim Mindanao, and Metro Manila, with Maluku and the Autonomous Region in Muslim Mindanao having more substantial religious conflicts than the other two regions.

Kanas, Scheepers, and Sterkens found that even accounting for the effects of self-selection, interreligious friendships reduced negative attitudes toward the religious out-group, while casual interreligious contact tended to increase negative out-group attitudes.

In regions experiencing more interreligious violence, there was no effect on interreligious friendships but a further deterioration in effect between casual interreligious contact and negative out-group attitudes.

Kanas, Scheepers, and Sterrkens believed that this effect could be explained by perceived group threat.

Evaluating the Contact Hypothesis

Allport’s testable formulation of the Contact Hypothesis has spawned research using a wide range of approaches, such as field studies, laboratory experiments, surveys, and archival research.

Pettigrew and Tropp (2005) conducted a 5-year meta-analysis on 515 studies (a method where researchers gather data from every possible study and statistically pool results to examine overall patterns) to uncover the overall effects of intergroup contact on prejudice and assess the specific factors that Allport identified as important for successful intergroup contact.

These studies ranged from the 1940s to the year 2000 and represented responses from 250,493 individuals across 38 countries.

The researchers found that, in general, greater levels of intergroup contact were associated with lower levels of prejudice and that more rigorous research studies actually revealed stronger relationships between contact and lowered prejudice (Pettigrew and Tropp, 2005).

The meta-analysis showed that the positive effects of contact on group relations vary dramatically between the nature of the groups, such as age, sexual orientation, disability, and mental illness, with the largest contact effects emerging for contact between heterosexuals and non-heterosexuals.

The smallest contact effects happened between those with and without mental and physical disabilities (Pettigrew and Tropp, 2005).

Although meta-analyses, such as Pettigrew and Tropp’s (2005) show that there is a strong association between intergroup contact and decreased prejudice, whether or not Allport’s four conditions hold is more widely contested.

Some researchers have suggested that the inverse relationship between contact and prejudice still persists in situations that do not match Allport’s key conditions, albeit not as strong as when they are present (Pettigrew and Tropp, 2005).

Gordon Allport taught sociology as a young man in Turkey (Nicholson, 2003) but emphasized proximal and immediate causes and disregarded larger-level, societal causes of intergroup effects.

As a result, both Allport and Williams (1947) doubted whether contact in itself reduced intergroup prejudice and thus attempted to specify a set of “positive conditions” where intergroup contact did.

Researchers have criticized Allport’s “positive factors” approach because it invites the addition of different situational conditions thought to be crucial that actually are not.

As a result, a number of researchers have proposed a host of additional conditions needed to achieve positive contact outcomes (e.g., Foster and Finchilescu, 1986) to the extent that it is unlikely that any contact situation would actually meet all of the conditions specified by the body of contact hypothesis researchers (Pettigrew and Tropp, 2005).

Researchers have also criticized Allport for not specifying the processes involved in intergroup contact’s effects or how these apply to other situations, the entire outgroup, or outgroups not involved in the contact (Pettigrew, 1998).

For example, Allport’s contact conditions leave open the question of whether contact with one group could lead to less prejudicial opinions of other outgroups.

All in all, Allport’s hypothesis neither reveals the processes behind the factors leading to the intergroup contact effect nor its effects on outgroups not involved in contact (Pettigrew, 1998).

Theorists have since pivoted their stance on the intergroup contact hypothesis to believing that intergroup contact generally diminishes prejudice but that a large number of facilitating factors can increase or decrease the magnitude of the effect.

In fact, according to newer theoretical approaches, there are negative factors that can even subvert the way that contact normally reduces prejudice (Pettigrew and Tropp, 2005).

For example, groups that tend to feel anxiety and threat toward others tend to have less decreased prejudice when put in contact with other groups (Blair, Park, and Bachelor, 2003; Stephan et al., 2002).

Indeed, more recent research into the contact hypothesis has suggested that the underlying mechanism of the phenomenon is not increased knowledge about the out-group in itself but empathy with the out-group and a reduction in intergroup threat and anxiety (Pettigrew and Tropp, 2008; Kanas, Scheepers, and Sterkens, 2015).

Social Darwinists such as William Graham Sumner (1906) believed that intergroup contact almost inevitably leads to conflict. Sumner believed that because most groups believed themselves to be superior, intergroup hostility and conflict were natural and inevitable outcomes of contact.

Perspectives such as those in Jackson (1983) and Levine and Campbell (1972) make similar predictions. In the twentieth century, perspectives began to diversify.

While some theorists believed that contact between in groups, such as between races, bred “suspicion, fear, resentment, disturbance, and at times open conflict” (Baker, 1934), others, such as Lett (1945), believed that interracial contact led to “mutual understanding and regard.”

Nonetheless, these early investigations were speculative rather than empirical (Pettigrew and Tropp, 2005). The emerging field of social psychology emphasized theories of intergroup contact.

The University of Alabama researchers Sims and Patrick (1936) were among the first to conduct a study on intergroup contact but found, discouragingly, that the anti-black attitudes of northern white students increased when immersed in the then all-white University of Alabama.

Aligning more with the later work of Allport, Brophy (1946) studied race relations between blacks and whites in the nearly-desegregated Merchant Marine. The researchers found that the more voyages that white seamen took with black seamen, the more positive their racial attitudes became.

In a similar direction, white police in Philadelphia with black colleagues showed fewer objections to working with black partners, having black people join previously all-white police districts, and taking orders from qualified black police officers (Kephart, 1957; Pettigrew and Tropp, 2005).

Following these studies, Cornell University sociologist Robin Williams Jr. offered 102 propositions on intergroup relations that constituted an initial formulation of intergroup contact theory.

These propositions generally stressed that intergroup contact reduces prejudice when (Williams, 1947):

• The two groups share similar statuses, interests, and tasks;
• the situation fosters personal, intimate intergroup contact;
• participants do not fit stereotypical conceptions of their group members;
• the activities cut across group lines.

Stouffer et al. offered the first extensive field study of the effects of intergroup contact (1949).

Stouffer et al. demonstrated that white soldiers who fought alongside black soldiers in the 1944-1945 Battle of the Bulge tended to have far more positive attitudes toward their black colleagues (Pettigrew and Tropp, 2005), regardless of status or place of origin.

Researchers such as Deutsch and Collins (1951); Wilner, Walkley, and Cook (1955); and Works (1961) supported mounting evidence that contact diminished racial prejudice among both blacks and whites through their studies of racially desegregated housing projects.

Allport, G. W. (1955). The nature of prejudice. In: JSTOR.

Anderson, L., Snow, D. A., & Cress, D. (1994). Negotiating the public realm: Stigma management and collective action among the homeless. Research in Community Sociology, 1(1), 121-143.

Aronson, E. (2002). Building empathy, compassion, and achievement in the jigsaw classroom. In Improving academic achievement (pp. 209-225): Elsevier.

Baker, P. E. (1934). Negro-white adjustment in America. Journal of Negro Education, 194-204.

Blair, I. V., Park, B., & Bachelor, J. (2003). Understanding Intergroup Anxiety: Are Some People More Anxious than Others? Group Processes & Intergroup Relations, 6(2), 151-169. doi:10.1177/1368430203006002002

Brewer, M. B., & Kramer, R. M. (1985). The psychology of intergroup attitudes and behavior. Annual review of psychology, 36(1), 219-243.

Brophy, I. N. (1945). The luxury of anti-Negro prejudice. Public opinion quarterly, 9(4), 456-466.

Caspi, A. (1984). Contact Hypothesis and Inter-Age Attitudes: A Field Study of Cross-Age Contact. Social Psychology Quarterly, 47(1), 74-80. doi:10.2307/3033890

Chu, D., & Griffey, D. (1985). The contact theory of racial integration: The case of sport. Sociology of Sport Journal, 2(4), 323-333.

Cohen, E. G. (1982). Expectation states and interracial interaction in school settings. Annual review of sociology, 8(1), 209-235.

Deutsch, M., & Collins, M. E. (1951). Interracial housing: A psychological evaluation of a social experiment: U of Minnesota Press.

Foster, D., & Finchilescu, G. (1986). Contact in a”non-contact”society: The case of South Africa.

Jackson, J. W. (1993). Contact theory of intergroup hostility: A review and evaluation of the theoretical and empirical literature. International Journal of Group Tensions, 23(1), 43-65.

Jackson, P. (1985). Social geography: race and racism. Progress in Human Geography, 9(1), 99-108.

Johnson, D., Johnson, R., & Maruyama, G. (1984). Goal Interdependence and Interpersonal-personal Attraction in Heterogeneous Classrooms: a meta analysis, chapter in Miller N & Brewer MB Groups in Contact: The Psychology of Desegregation. In: New York: Academic Press.

Kanas, A., Scheepers, P., & Sterkens, C. (2015). Interreligious Contact, Perceived Group Threat, and Perceived Discrimination:Predicting Negative Attitudes among Religious Minorities and Majorities in Indonesia. Social Psychology Quarterly, 78(2), 102-126. doi:10.1177/0190272514564790

Kephart, W. M. (2018). Racial factors and urban law enforcement: University of Pennsylvania Press.

Kramer, B. M. (1950). Residential contact as a determinant of attitudes toward Negroes. Harvard University.

Lee, B. A., Farrell, C. R., & Link, B. G. (2004). Revisiting the Contact Hypothesis: The Case of Public Exposure to Homelessness. American Sociological Review, 69(1), 40-63. doi:10.1177/000312240406900104

Lee, F. F. (1956). Human Relations in Interracial Housing: A Study of the Contact Hypothesis. In: JSTOR.

Lett, H. A. (1945). Techniques for achieving interracial cooperation. Harvard Educational Review, 15(1), 62-71.

LeVine, R. A., & Campbell, D. T. (1972). Ethnocentrism: Theories of conflict, ethnic attitudes, and group behavior.

MacKenzie, B. K. (1948). The importance of contact in determining attitudes toward Negroes. The Journal of Abnormal and Social Psychology, 43(4), 417.

Morrison, E. W., & Herlihy, J. M. (1992). Becoming the best place to work: Managing diversity at American Express Travel related services. Diversity in the Workplace, 203-226.

Nicholson, I. A. M. (2003). Inventing personality: Gordon Allport and the science of selfhood. Washington, DC, US: American Psychological Association.

Parker, J. H. (1968). The Interaction of Negroes and Whites in an Integrated Church Setting. Social Forces, 46(3), 359-366. doi:10.2307/2574883

Pettigrew, T. F. (1998). INTERGROUP CONTACT THEORY. Annual review of psychology, 49(1), 65-85. doi:10.1146/annurev.psych.49.1.65

Pettigrew, T. F., & Tropp, L. R. (2005). Allport’s intergroup contact hypothesis: Its history and influence. On the nature of prejudice: Fifty years after Allport, 262-277.

Riordan, C., & Ruggiero, J. (1980). Producing equal-status interracial interaction: A replication. Social Psychology Quarterly, 131-136. Schofield, J. W. (1986). Black-White contact in desegregated schools.

Schofield, J. W., & Eurich-Fulcer, R. (2004). When and How School Desegregation Improves Intergroup Relations.

Sims, V. M., & Patrick, J. R. (1936). Attitude toward the Negro of northern and southern college students. The Journal of Social Psychology, 7(2), 192-204.

Stephan, W. G., Boniecki, K. A., Ybarra, O., Bettencourt, A., Ervin, K. S., Jackson, L. A., . . . Renfro, C. L. (2002). The Role of Threats in the Racial Attitudes of Blacks and Whites. Personality and Social Psychology Bulletin, 28(9), 1242-1254. doi:10.1177/01461672022812009

Stouffer, S. A., Suchman, E. A., DeVinney, L. C., Star, S. A., & Williams Jr, R. M. (1949). The american soldier: Adjustment during army life.(studies in social psychology in world war ii), vol. 1.

Sumner, W. G. (1906). Folkways: The Sociological Importance of Usages. Manners, Customs, Mores, and Morals, Ginn & Co., New York, NY.

Williams Jr, R. M. (1947). The reduction of intergroup tensions: a survey of research on problems of ethnic, racial, and religious group relations. Social Science Research Council Bulletin.

Works, E. (1961). The prejudice-interaction hypothesis from the point of view of the Negro minority group. American Journal of Sociology, 67(1), 47-52.

Further Information

Dovidio, J. F., Love, A., Schellhaas, F. M., & Hewstone, M. (2017). Reducing intergroup bias through intergroup contact: Twenty years of progress and future directions. Group Processes & Intergroup Relations, 20(5), 606-620.

Pettigrew, T. F., Tropp, L. R., Wagner, U., & Christ, O. (2011). Recent advances in intergroup contact theory. International journal of intercultural relations, 35(3), 271-280.

Pettigrew, T. F., & Tropp, L. R. (2006). A meta-analytic test of intergroup contact theory. Journal of personality and social psychology, 90(5), 751.

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13 The Psychology of Groups

This module assumes that a thorough understanding of people requires a thorough understanding of groups. Each of us is an autonomous individual seeking our own objectives, yet we are also members of groups—groups that constrain us, guide us, and sustain us. Just as each of us influences the group and the people in the group, so, too, do groups change each one of us. Joining groups satisfies our need to belong, gain information and understanding through social comparison, define our sense of self and social identity, and achieve goals that might elude us if we worked alone. Groups are also practically significant, for much of the world’s work is done by groups rather than by individuals. Success sometimes eludes our groups, but when group members learn to work together as a cohesive team their success becomes more certain. People also turn to groups when important decisions must be made, and this choice is justified as long as groups avoid such problems as group polarization and groupthink.

Learning Objectives

• Review the evidence that suggests humans have a fundamental need to belong to groups.
• Compare the sociometer model of self-esteem to a more traditional view of self-esteem.
• Use theories of social facilitation to predict when a group will perform tasks slowly or quickly (e.g., students eating a meal as a group, workers on an assembly line, or a study group).
• Summarize the methods used by Latané, Williams, and Harkins to identify the relative impact of social loafing and coordination problems on group performance.
• Describe how groups change over time.
• Apply the theory of groupthink to a well-known decision-making group, such as the group of advisors responsible for planning the Bay of Pigs operation.
• List and discuss the factors that facilitate and impede group performance and decision making.
• Develop a list of recommendations that, if followed, would minimize the possibility of groupthink developing in a group.

The Psychology of Groups

Psychologists study groups because nearly all human activities—working, learning, worshiping, relaxing, playing, and even sleeping—occur in groups. The lone individual who is cut off from all groups is a rarity. Most of us live out our lives in groups, and these groups have a profound impact on our thoughts, feelings, and actions. Many psychologists focus their attention on single individuals, but social psychologists expand their analysis to include groups, organizations, communities, and even cultures.

This module examines the psychology of groups and group membership. It begins with a basic question: What is the psychological significance of groups? People are, undeniably, more often in groups rather than alone. What accounts for this marked gregariousness and what does it say about our psychological makeup? The module then reviews some of the key findings from studies of groups. Researchers have asked many questions about people and groups: Do people work as hard as they can when they are in groups? Are groups more cautious than individuals? Do groups make wiser decisions than single individuals? In many cases the answers are not what common sense and folk wisdom might suggest.

The Psychological Significance of Groups

Many people loudly proclaim their autonomy and independence. Like Ralph Waldo Emerson, they avow, “I must be myself. I will not hide my tastes or aversions . . . . I will seek my own” ( 1903/2004 , p. 127). Even though people are capable of living separate and apart from others, they join with others because groups meet their psychological and social needs.

The Need to Belong

Across individuals, societies, and even eras, humans consistently seek inclusion over exclusion, membership over isolation, and acceptance over rejection. As Roy Baumeister and Mark Leary conclude, humans have a need to belong : “a pervasive drive to form and maintain at least a minimum quantity of lasting, positive, and impactful interpersonal relationships” ( 1995 , p. 497). And most of us satisfy this need by joining groups. When surveyed, 87.3% of Americans reported that they lived with other people, including family members, partners, and roommates ( Davis & Smith, 2007 ). The majority, ranging from 50% to 80%, reported regularly doing things in groups, such as attending a sports event together, visiting one another for the evening, sharing a meal together, or going out as a group to see a movie ( Putnam, 2000 ).

People respond negatively when their need to belong is unfulfilled. For example, college students often feel homesick and lonely when they first start college, but not if they belong to a cohesive, socially satisfying group ( Buote et al., 2007 ). People who are accepted members of a group tend to feel happier and more satisfied. But should they be rejected by a group, they feel unhappy, helpless, and depressed. Studies of ostracism —the deliberate exclusion from groups—indicate this experience is highly stressful and can lead to depression, confused thinking, and even aggression ( Williams, 2007 ). When researchers used a functional magnetic resonance imaging scanner to track neural responses to exclusion, they found that people who were left out of a group activity displayed heightened cortical activity in two specific areas of the brain—the dorsal anterior cingulate cortex and the anterior insula. These areas of the brain are associated with the experience of physical pain sensations ( Eisenberger, Lieberman, & Williams, 2003 ). It hurts, quite literally, to be left out of a group.

Affiliation in Groups

Groups not only satisfy the need to belong, they also provide members with information, assistance, and social support. Leon Festinger’s theory of social comparison ( 1950 , 1954 ) suggested that in many cases people join with others to evaluate the accuracy of their personal beliefs and attitudes. Stanley Schachter ( 1959 ) explored this process by putting individuals in ambiguous, stressful situations and asking them if they wished to wait alone or with others. He found that people affiliate in such situations—they seek the company of others.

Although any kind of companionship is appreciated, we prefer those who provide us with reassurance and support as well as accurate information. In some cases, we also prefer to join with others who are even worse off than we are. Imagine, for example, how you would respond when the teacher hands back the test and yours is marked 85%. Do you want to affiliate with a friend who got a 95% or a friend who got a 78%? To maintain a sense of self-worth, people seek out and compare themselves to the less fortunate. This process is known as downward social comparison .

Identity and Membership

Groups are not only founts of information during times of ambiguity, they also help us answer the existentially significant question, “Who am I?” Common sense tells us that our sense of self is our private definition of who we are, a kind of archival record of our experiences, qualities, and capabilities. Yet, the self also includes all those qualities that spring from memberships in groups. People are defined not only by their traits, preferences, interests, likes, and dislikes, but also by their friendships, social roles, family connections, and group memberships. The self is not just a “me,” but also a “we.”

Even demographic qualities such as sex or age can influence us if we categorize ourselves based on these qualities. Social identity theory , for example, assumes that we don’t just classify other people into such social categories as man, woman, Anglo, elderly, or college student, but we also categorize ourselves. Moreover, if we strongly identify with these categories, then we will ascribe the characteristics of the typical member of these groups to ourselves, and so stereotype ourselves. If, for example, we believe that college students are intellectual, then we will assume we, too, are intellectual if we identify with that group ( Hogg, 2001 ).

Groups also provide a variety of means for maintaining and enhancing a sense of self-worth, as our assessment of the quality of groups we belong to influences our collective self-esteem ( Crocker & Luhtanen, 1990 ). If our self-esteem is shaken by a personal setback, we can focus on our group’s success and prestige. In addition, by comparing our group to other groups, we frequently discover that we are members of the better group, and so can take pride in our superiority. By denigrating other groups, we elevate both our personal and our collective self-esteem ( Crocker & Major, 1989 ).

Mark Leary’s sociometer model goes so far as to suggest that “self-esteem is part of a sociometer that monitors peoples’ relational value in other people’s eyes” ( 2007 , p. 328). He maintains self-esteem is not just an index of one’s sense of personal value, but also an indicator of acceptance into groups. Like a gauge that indicates how much fuel is left in the tank, a dip in self-esteem indicates exclusion from our group is likely. Disquieting feelings of self-worth, then, prompt us to search for and correct characteristics and qualities that put us at risk of social exclusion. Self-esteem is not just high self-regard, but the self-approbation that we feel when included in groups ( Leary & Baumeister, 2000 ).

Evolutionary Advantages of Group Living

Groups may be humans’ most useful invention, for they provide us with the means to reach goals that would elude us if we remained alone. Individuals in groups can secure advantages and avoid disadvantages that would plague the lone individuals. In his theory of social integration, Moreland concludes that groups tend to form whenever “people become dependent on one another for the satisfaction of their needs” ( 1987 , p. 104). The advantages of group life may be so great that humans are biologically prepared to seek membership and avoid isolation. From an evolutionary psychology perspective, because groups have increased humans’ overall fitness for countless generations, individuals who carried genes that promoted solitude-seeking were less likely to survive and procreate compared to those with genes that prompted them to join groups ( Darwin, 1859/1963 ). This process of natural selection culminated in the creation of a modern human who seeks out membership in groups instinctively, for most of us are descendants of “joiners” rather than “loners.”

Motivation and Performance

Groups usually exist for a reason. In groups, we solve problems, create products, create standards, communicate knowledge, have fun, perform arts, create institutions, and even ensure our safety from attacks by other groups. But do groups always outperform individuals?

Social Facilitation in Groups

Do people perform more effectively when alone or when part of a group? Norman Triplett ( 1898 ) examined this issue in one of the first empirical studies in psychology. While watching bicycle races, Triplett noticed that cyclists were faster when they competed against other racers than when they raced alone against the clock. To determine if the presence of others leads to the psychological stimulation that enhances performance, he arranged for 40 children to play a game that involved turning a small reel as quickly as possible (see Figure 1). When he measured how quickly they turned the reel, he confirmed that children performed slightly better when they played the game in pairs compared to when they played alone (see Stroebe, 2012 ; Strube, 2005 ).

Triplett succeeded in sparking interest in a phenomenon now known as social facilitation : the enhancement of an individual’s performance when that person works in the presence of other people. However, it remained for Robert Zajonc ( 1965 ) to specify when social facilitation does and does not occur. After reviewing prior research, Zajonc noted that the facilitating effects of an audience usually only occur when the task requires the person to perform dominant responses, i.e., ones that are well-learned or based on instinctive behaviors. If the task requires nondominant responses, i.e., novel, complicated, or untried behaviors that the organism has never performed before or has performed only infrequently, then the presence of others inhibits performance. Hence, students write poorer quality essays on complex philosophical questions when they labor in a group rather than alone ( Allport, 1924 ), but they make fewer mistakes in solving simple, low-level multiplication problems with an audience or a coactor than when they work in isolation ( Dashiell, 1930 ).

Social facilitation, then, depends on the task: other people facilitate performance when the task is so simple that it requires only dominant responses, but others interfere when the task requires nondominant responses. However, a number of psychological processes combine to influence when social facilitation, not social interference, occurs. Studies of the challenge-threat response and brain imaging, for example, confirm that we respond physiologically and neurologically to the presence of others ( Blascovich, Mendes, Hunter, & Salomon, 1999 ). Other people also can trigger evaluation apprehension, particularly when we feel that our individual performance will be known to others, and those others might judge it negatively ( Bond, Atoum, & VanLeeuwen, 1996 ). The presence of other people can also cause perturbations in our capacity to concentrate on and process information ( Harkins, 2006 ). Distractions due to the presence of other people have been shown to improve performance on certain tasks, such as the Stroop task , but undermine performance on more cognitively demanding tasks ( Huguet, Galvaing, Monteil, & Dumas, 1999 ).

Social Loafing

Groups usually outperform individuals. A single student, working alone on a paper, will get less done in an hour than will four students working on a group project. One person playing a tug-of-war game against a group will lose. A crew of movers can pack up and transport your household belongings faster than you can by yourself. As the saying goes, “Many hands make light the work” ( Littlepage, 1991 ; Steiner, 1972 ).

Groups, though, tend to be underachievers. Studies of social facilitation confirmed the positive motivational benefits of working with other people on well-practiced tasks in which each member’s contribution to the collective enterprise can be identified and evaluated. But what happens when tasks require a truly collective effort? First, when people work together they must coordinate their individual activities and contributions to reach the maximum level of efficiency—but they rarely do ( Diehl & Stroebe, 1987 ). Three people in a tug-of-war competition, for example, invariably pull and pause at slightly different times, so their efforts are uncoordinated. The result is coordination loss : the three-person group is stronger than a single person, but not three times as strong. Second, people just don’t exert as much effort when working on a collective endeavor, nor do they expend as much cognitive effort trying to solve problems, as they do when working alone. They display social loafing ( Latané, 1981 ).

Bibb Latané, Kip Williams, and Stephen Harkins ( 1979 ) examined both coordination losses and social loafing by arranging for students to cheer or clap either alone or in groups of varying sizes. The students cheered alone or in 2- or 6-person groups, or they were lead to believe they were in 2- or 6-person groups (those in the “pseudo-groups” wore blindfolds and headsets that played masking sound). As Figure 2 indicates, groups generated more noise than solitary subjects, but the productivity dropped as the groups became larger in size. In dyads, each subject worked at only 66% of capacity, and in 6-person groups at 36%. Productivity also dropped when subjects merely believed they were in groups. If subjects thought that one other person was shouting with them, they shouted 82% as intensely, and if they thought five other people were shouting, they reached only 74% of their capacity. These loses in productivity were not due to coordination problems; this decline in production could be attributed only to a reduction in effort—to social loafing (Latané et al., 1979, Experiment 2).

Social loafing is no rare phenomenon. When sales personnel work in groups with shared goals, they tend to “take it easy” if another salesperson is nearby who can do their work ( George, 1992 ). People who are trying to generate new, creative ideas in group brainstorming sessions usually put in less effort and are thus less productive than people who are generating new ideas individually ( Paulus & Brown, 2007 ). Students assigned group projects often complain of inequity in the quality and quantity of each member’s contributions: Some people just don’t work as much as they should to help the group reach its learning goals ( Neu, 2012 ). People carrying out all sorts of physical and mental tasks expend less effort when working in groups, and the larger the group, the more they loaf ( Karau & Williams, 1993 ).

Groups can, however, overcome this impediment to performance through teamwork . A group may include many talented individuals, but they must learn how to pool their individual abilities and energies to maximize the team’s performance. Team goals must be set, work patterns structured, and a sense of group identity developed. Individual members must learn how to coordinate their actions, and any strains and stresses in interpersonal relations need to be identified and resolved ( Salas, Rosen, Burke, & Goodwin, 2009 ).

Researchers have identified two key ingredients to effective teamwork: a shared mental representation of the task and group unity. Teams improve their performance over time as they develop a shared understanding of the team and the tasks they are attempting. Some semblance of this shared mental model is present nearly from its inception, but as the team practices, differences among the members in terms of their understanding of their situation and their team diminish as a consensus becomes implicitly accepted ( Tindale, Stawiski, & Jacobs, 2008 ).

Effective teams are also, in most cases, cohesive groups ( Dion, 2000 ). Group cohesion is the integrity, solidarity, social integration, or unity of a group. In most cases, members of cohesive groups like each other and the group and they also are united in their pursuit of collective, group-level goals. Members tend to enjoy their groups more when they are cohesive, and cohesive groups usually outperform ones that lack cohesion.

This cohesion-performance relationship, however, is a complex one. Meta-analytic studies suggest that cohesion improves teamwork among members, but that performance quality influences cohesion more than cohesion influences performance ( Mullen & Copper, 1994 ; Mullen, Driskell, & Salas, 1998 ; see Figure 3). Cohesive groups also can be spectacularly unproductive if the group’s norms stress low productivity rather than high productivity ( Seashore, 1954 ).

Group Development

In most cases groups do not become smooth-functioning teams overnight. As Bruce Tuckman’s ( 1965 ) theory of group development suggests, groups usually pass through several stages of development as they change from a newly formed group into an effective team. As noted in Focus Topic 1, in the forming phase, the members become oriented toward one another. In the storming phase, the group members find themselves in conflict, and some solution is sought to improve the group environment. In the norming, phase standards for behavior and roles develop that regulate behavior. In the performing, phase the group has reached a point where it can work as a unit to achieve desired goals, and the adjourning phase ends the sequence of development; the group disbands. Throughout these stages groups tend to oscillate between the task-oriented issues and the relationship issues, with members sometimes working hard but at other times strengthening their interpersonal bonds ( Tuckman & Jensen, 1977 ).

Focus Topic 1: Group Development Stages and Characteristics

Stage 1 – “Forming”. Members expose information about themselves in polite but tentative interactions. They explore the purposes of the group and gather information about each other’s interests, skills, and personal tendencies.

Stage 2 – “Storming”. Disagreements about procedures and purposes surface, so criticism and conflict increase. Much of the conflict stems from challenges between members who are seeking to increase their status and control in the group.

Stage 3 – “Norming”. Once the group agrees on its goals, procedures, and leadership, norms, roles, and social relationships develop that increase the group’s stability and cohesiveness.

Stage 4 – “Performing”. The group focuses its energies and attention on its goals, displaying higher rates of task-orientation, decision-making, and problem-solving.

Stage 5 – “Adjourning”. The group prepares to disband by completing its tasks, reduces levels of dependency among members, and dealing with any unresolved issues.

Sources based on Tuckman (1965) and Tuckman & Jensen (1977)

We also experience change as we pass through a group, for we don’t become full-fledged members of a group in an instant. Instead, we gradually become a part of the group and remain in the group until we leave it. Richard Moreland and John Levine’s ( 1982 ) model of group socialization describes this process, beginning with initial entry into the group and ending when the member exits it. For example, when you are thinking of joining a new group—a social club, a professional society, a fraternity or sorority, or a sports team—you investigate what the group has to offer, but the group also investigates you. During this investigation stage you are still an outsider: interested in joining the group, but not yet committed to it in any way. But once the group accepts you and you accept the group, socialization begins: you learn the group’s norms and take on different responsibilities depending on your role. On a sports team, for example, you may initially hope to be a star who starts every game or plays a particular position, but the team may need something else from you. In time, though, the group will accept you as a full-fledged member and both sides in the process—you and the group itself—increase their commitment to one another. When that commitment wanes, however, your membership may come to an end as well.

Making Decisions in Groups

Groups are particularly useful when it comes to making a decision, for groups can draw on more resources than can a lone individual. A single individual may know a great deal about a problem and possible solutions, but his or her information is far surpassed by the combined knowledge of a group. Groups not only generate more ideas and possible solutions by discussing the problem, but they can also more objectively evaluate the options that they generate during discussion. Before accepting a solution, a group may require that a certain number of people favor it, or that it meets some other standard of acceptability. People generally feel that a group’s decision will be superior to an individual’s decision.

Groups, however, do not always make good decisions. Juries sometimes render verdicts that run counter to the evidence presented. Community groups take radical stances on issues before thinking through all the ramifications. Military strategists concoct plans that seem, in retrospect, ill-conceived and short-sighted. Why do groups sometimes make poor decisions?

Group Polarization

Let’s say you are part of a group assigned to make a presentation. One of the group members suggests showing a short video that, although amusing, includes some provocative images. Even though initially you think the clip is inappropriate, you begin to change your mind as the group discusses the idea. The group decides, eventually, to throw caution to the wind and show the clip—and your instructor is horrified by your choice.

This hypothetical example is consistent with studies of groups making decisions that involve risk. Common sense notions suggest that groups exert a moderating, subduing effect on their members. However, when researchers looked at groups closely, they discovered many groups shift toward more extreme decisions rather than less extreme decisions after group interaction. Discussion, it turns out, doesn’t moderate people’s judgments after all. Instead, it leads to group polarization : judgments made after group discussion will be more extreme in the same direction as the average of individual judgments made prior to discussion ( Myers & Lamm, 1976 ). If a majority of members feel that taking risks is more acceptable than exercising caution, then the group will become riskier after a discussion. For example, in France, where people generally like their government but dislike Americans, group discussion improved their attitude toward their government but exacerbated their negative opinions of Americans ( Moscovici & Zavalloni, 1969 ). Similarly, prejudiced people who discussed racial issues with other prejudiced individuals became even more negative, but those who were relatively unprejudiced exhibited even more acceptance of diversity when in groups ( Myers & Bishop, 1970 ).

Common Knowledge Effect

One of the advantages of making decisions in groups is the group’s greater access to information. When seeking a solution to a problem, group members can put their ideas on the table and share their knowledge and judgments with each other through discussions. But all too often groups spend much of their discussion time examining common knowledge—information that two or more group members know in common—rather than unshared information. This common knowledge effect will result in a bad outcome if something known by only one or two group members is very important.

Researchers have studied this bias using the hidden profile task . On such tasks, information known to many of the group members suggests that one alternative, say Option A, is best. However, Option B is definitely the better choice, but all the facts that support Option B are only known to individual groups members—they are not common knowledge in the group. As a result, the group will likely spend most of its time reviewing the factors that favor Option A, and never discover any of its drawbacks. In consequence, groups often perform poorly when working on problems with nonobvious solutions that can only be identified by extensive information sharing ( Stasser & Titus, 1987 ).

Groups sometimes make spectacularly bad decisions. In 1961, a special advisory committee to President John F. Kennedy planned and implemented a covert invasion of Cuba at the Bay of Pigs that ended in total disaster. In 1986, NASA carefully, and incorrectly, decided to launch the Challenger space shuttle in temperatures that were too cold.

Irving Janis ( 1982 ), intrigued by these kinds of blundering groups, carried out a number of case studies of such groups: the military experts that planned the defense of Pearl Harbor; Kennedy’s Bay of Pigs planning group; the presidential team that escalated the war in Vietnam. Each group, he concluded, fell prey to a distorted style of thinking that rendered the group members incapable of making a rational decision. Janis labeled this syndrome groupthink : “a mode of thinking that people engage in when they are deeply involved in a cohesive in-group, when the members’ strivings for unanimity override their motivation to realistically appraise alternative courses of action” (p. 9).

Janis identified both the telltale symptoms that signal the group is experiencing groupthink and the interpersonal factors that combine to cause groupthink. To Janis, groupthink is a disease that infects healthy groups, rendering them inefficient and unproductive. And like the physician who searches for symptoms that distinguish one disease from another, Janis identified a number of symptoms that should serve to warn members that they may be falling prey to groupthink. These symptoms include overestimating the group’s skills and wisdom, biased perceptions and evaluations of other groups and people who are outside of the group, strong conformity pressures within the group, and poor decision-making methods.

Janis also singled out four group-level factors that combine to cause groupthink: cohesion, isolation, biased leadership, and decisional stress.

• Cohesion : Groupthink only occurs in cohesive groups. Such groups have many advantages over groups that lack unity. People enjoy their membership much more in cohesive groups, they are less likely to abandon the group, and they work harder in pursuit of the group’s goals. But extreme cohesiveness can be dangerous. When cohesiveness intensifies, members become more likely to accept the goals, decisions, and norms of the group without reservation. Conformity pressures also rise as members become reluctant to say or do anything that goes against the grain of the group, and the number of internal disagreements—necessary for good decision making—decreases.
• Isolation. Groupthink groups too often work behind closed doors, keeping out of the limelight. They isolate themselves from outsiders and refuse to modify their beliefs to bring them into line with society’s beliefs. They avoid leaks by maintaining strict confidentiality and working only with people who are members of their group.
• Biased leadership . A biased leader who exerts too much authority over group members can increase conformity pressures and railroad decisions. In groupthink groups, the leader determines the agenda for each meeting, sets limits on discussion, and can even decide who will be heard.
• Decisional stress. Groupthink becomes more likely when the group is stressed, particularly by time pressures. When groups are stressed they minimize their discomfort by quickly choosing a plan of action with little argument or dissension. Then, through collective discussion, the group members can rationalize their choice by exaggerating the positive consequences, minimizing the possibility of negative outcomes, concentrating on minor details, and overlooking larger issues.

You and Your Groups

Most of us belong to at least one group that must make decisions from time to time: a community group that needs to choose a fund-raising project; a union or employee group that must ratify a new contract; a family that must discuss your college plans; or the staff of a high school discussing ways to deal with the potential for violence during football games. Could these kinds of groups experience groupthink? Yes they could, if the symptoms of groupthink discussed above are present, combined with other contributing causal factors, such as cohesiveness, isolation, biased leadership, and stress. To avoid polarization, the common knowledge effect, and groupthink, groups should strive to emphasize open inquiry of all sides of the issue while admitting the possibility of failure. The leaders of the group can also do much to limit groupthink by requiring full discussion of pros and cons, appointing devil’s advocates, and breaking the group up into small discussion groups.

If these precautions are taken, your group has a much greater chance of making an informed, rational decision. Furthermore, although your group should review its goals, teamwork, and decision-making strategies, the human side of groups—the strong friendships and bonds that make group activity so enjoyable—shouldn’t be overlooked. Groups have instrumental, practical value, but also emotional, psychological value. In groups we find others who appreciate and value us. In groups we gain the support we need in difficult times, but also have the opportunity to influence others. In groups we find evidence of our self-worth, and secure ourselves from the threat of loneliness and despair. For most of us, groups are the secret source of well-being.

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Excluding one or more individuals from a group by reducing or eliminating contact with the person, usually by ignoring, shunning, or explicitly banishing them.

The process by which people understand their own ability or condition by mentally comparing themselves to others.

Social identity theory notes that people categorize each other into groups, favoring their own group.

Feelings of self-worth that are based on evaluation of relationships with others and membership in social groups.

A conceptual analysis of self-evaluation processes that theorizes self-esteem functions to psychologically monitor of one’s degree of inclusion and exclusion in social groups.

When performance on simple or well-rehearsed tasks is enhanced when we are in the presence of others.

The reduction of individual effort exerted when people work in groups compared with when they work alone.

The process by which members of the team combine their knowledge, skills, abilities, and other resources through a coordinated series of actions to produce an outcome.

Knowledge, expectations, conceptualizations, and other cognitive representations that members of a group have in common pertaining to the group and its members, tasks, procedures, and resources.

The solidarity or unity of a group resulting from the development of strong and mutual interpersonal bonds among members and group-level forces that unify the group, such as shared commitment to group goals.

The tendency for members of a deliberating group to move to a more extreme position, with the direction of the shift determined by the majority or average of the members’ predeliberation preferences.

The tendency for groups to spend more time discussing information that all members know (shared information) and less time examining information that only a few members know (unshared).

A set of negative group-level processes, including illusions of invulnerability, self-censorship, and pressures to conform, that occur when highly cohesive groups seek concurrence when making a decision.

An Introduction to Social Psychology Copyright © 2022 by Thomas Edison State University is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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• Published: 01 September 2021

Creation, evolution, and dissolution of social groups

• James Flamino 1 , 2   na1 ,
• Boleslaw K. Szymanski 1 , 2 , 3 , 4   na1 ,
• Ashwin Bahulkar 1 , 3   na1 ,
• Kevin Chan 5 &
• Omar Lizardo 6  

Scientific Reports volume  11 , Article number:  17470 ( 2021 ) Cite this article

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• Complex networks
• Computational science
• Computer science
• Human behaviour

Understanding why people join, stay, or leave social groups is a central question in the social sciences, including computational social systems, while modeling these processes is a challenge in complex networks. Yet, the current empirical studies rarely focus on group dynamics for lack of data relating opinions to group membership. In the NetSense data, we find hundreds of face-to-face groups whose members make thousands of changes of memberships and opinions. We also observe two trends: opinion homogeneity grows over time, and individuals holding unpopular opinions frequently change groups. These observations and data provide us with the basis on which we model the underlying dynamics of human behavior. We formally define the utility that members gain from ingroup interactions as a function of the levels of homophily of opinions of group members with opinions of a given individual in this group. We demonstrate that so-defined utility applied to our empirical data increases after each observed change. We then introduce an analytical model and show that it accurately recreates the trends observed in the NetSense data.

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Introduction

Most social interactions among humans occur in the context of a set of relatively small face-to-face groups, and not in isolated dyads 1 , 2 , 3 , 4 , 5 , 6 . In addition, these groups are not static. Instead, their membership changes over time with individuals constantly deciding whether to stay in their current groups and perhaps adjust some opinions, or leave to join other groups 7 . To date, however, the dynamics of human group formation, evolution, and dissolution in real social groups remain poorly understood 8 , 9 . Among humans, groups represent a level of association mediating between the individual and whole societies, producing a variety of personal benefits, including companionship and support 1 , while also allowing people to come together to pursue instrumental goals or work together toward common purposes 10 . Despite the introduction of telecommunication as an entirely different medium of interaction between individuals, the importance of offline groups as the primary nexus of social life has prevailed. Indeed, new technologically mediated interactions such as texting, online chatting, and social media complement rather than replace face-to-face interaction in small groups, and a great majority of online ties ultimately relies on (or emerges from) offline face-to-face interactions 11 . Extended to social connections within face-to-face groups, computational and empirical literature have found that these groups are continually evolving rather than being static 5 , 8 , 9 , 12 , 13 . Such evolution has been aided by the tendency of the contemporary societies to relax social and institutional restrictions on group joining and leaving behavior compared to the past or to the societies that are more traditional 10 , 14 .

The previous theoretical works identified homophilic interactions within groups and communities as the primary component of the membership benefits 4 , 7 . Recent papers 15 , 16 , 17 separately consider a role of homophily in popularity, ranking of minorities, and structural change in social networks. Here, we formalize a notion of utility of group membership as a function of compatibility of opinions between a member and all others in its group. The utility enables us to integrate in a single model the three aforementioned phenomena and to account for the two dynamic patterns observed in empirical data. We find that changes of opinions or group memberships that increase members’ utility recreate the group dynamic patterns observed in empirical data and the theoretically postulated role of homophily in these dynamics. This agreement validates our utility maximization hypothesis. Subsequently, we introduce a predictive model based on utility maximization. This model performs well on forecasting opinions and group membership dynamics within the data, which further validates our approach.

In this paper we assume a broad definition of “groups” since within the social sciences, there are a variety of ways of defining groups 1 , 3 , 18 . Some definitions emphasize enduring affective ties among members, while others point to a unique sociometric signature (e.g., fully connected cliques). However, all approaches point to three features that seem to be characteristic of all human groups 18 . First, groups are located in specific settings allowing for face-to-face interaction within relatively small spatial distances (dormitories, homes, workplaces), happening at specific times (mornings, evenings) 3 , 7 . Second, groups are relatively small 1 . After a human gathering reaches a limit size, it loses its capacity to function as a coordinated group, and becomes a “crowd” or a “mass” instead 10 . Finally, groups can overlap. This means that persons are not constrained to belong to only one group. Instead, groups share members, and people need to make decisions (given a limited budget) as to how much time and energy they will dedicate to each group.

These three features, namely, specific location in time and space, relatively small size, and possibility of overlap, inform our approach to group detection and to analysis of group composition dynamics presented here. We are agnostic as to whether “strong” ties exist among group members as we define them; instead, we allow for people to be either weakly or strongly attached to the groups to which they belong as that becomes an endogenous factor driving the dynamics of change in group membership we observe. By definition, since, as in previous work 8 , 19 , 20 , 21 , we use data sensitive to spatial co-location to define groups, every group we observe is a sociometric clique 3 .

In this paper, we use the NetSense study that follows 196 randomly selected students from the incoming freshman class of fall 2011 at the University of Notre Dame, until the spring semester of 2013. Reference 22 discusses the study’s design and its approval by the university’s Institutional Review Board for studies involving human subjects. The collected dataset includes study participants’ answers to survey questions about their stances on a variety of topics and the Bluetooth proximity records collected continuously by the student’s cell phones. Every phone of a NetSense study member makes a record of every Bluetooth interaction between a pair of participants when this interaction meets the threshold of physical proximity. To extract stable groups formed in each semester from this proximity data, we use the hierarchical clustering method that we proposed in 23 as discussed in the first subsection of “ Methods ” section.

Similar data collection strategies and the use of Bluetooth proximity for finding real-world group gatherings were used and validated by others in 8 , 24 , 25 , 26 , 27 , 28 . Table 1 lists the details about the groups identified from the Bluetooth data. Our approach discovers 436 groups, with their periods of existence ranging from one to four semesters, and identifies 149,771 meetings. Since the average fraction of group meetings attended by a member ranges from 0.89 to 0.91, members stay with their groups for 90% of the 14 week semester. Accordingly, we assume that changes happen at the three boundaries between the semesters. By design, this is where we analyze any changes in the data. Having so reconstructed groups, we are able to detect changes in these groups and their group members between semesters (see “ Methods ”). We use these changes as a ground truth for our analytical model, which we design to predict group membership dynamics and changes in individual opinions.

To study the dynamics of student opinions, specifically, we use demographic surveys that the NetSense study participants completed at the beginning of each semester. These surveys contain 38 questions pertaining to family background, activities on campus, hobbies, and stances on various social and political issues, favorite types of music. The possible answers to these questions also came in a variety of forms, ranging from “yes” and “no” modalities, to closed lists, to open-ended text boxes. In our study, we include the five survey questions that were found to be the most predictive on the formation of new social relationships 29 . These questions are related to general political orientation, as well as stances on the legality of abortion, marijuana usage, gay marriage rights, and alcoholic drinking habits. For political stances, the possible answers rely on a seven-point scale, with one being extremely liberal, four being neutral, and seven being extremely conservative. The other questions use a similar point scale, ranging from strong support to strong opposition to the question, with a neutral stance in the middle. Drinking habits answers describe a range of drinking frequencies, ranging from no drinking to very frequent drinking. For all questions (including drinking habits), we cluster the range of responses into three modalities; joining extreme and moderate stances into two opposing stances while the third stance represents neutral answers. To track a change in opinion across a semester for our ground truth, we simply compare the stances of a student before and after the boundary.

In contrast to other studies that rely on routinely and massively collected Call Record Data, our study relies also on Bluetooth and survey data. This approach allows us to track actual face-to-face groups, rather than hypothetical or ersatz groups purely based on remote communications. While work based on remote telecommunications may have the advantage of scale 30 , it has the disadvantage of not being able to account for face-to-face group dynamics, which is the core scientific question we are after. In this respect, the sample size of human subjects in our study is consistent with that used in the very few other studies that have been able to track on-the-ground human groups over time.

For instance, 21 tracked 100 participants in an executive MBA program at an elite business school using infrared wearable badges on a single occasion, while 31 also tracked 100 subjects (using a similar bluetooth technology approach as NetSense) for 9 months. In the same manner, 20 tracked 24 graduate student participants for a whole year, also using wearable badges. A study that tracked 200 students for a whole year using motion sensors comes close to the sample size in NetSense 19 . All this previous work identifying groups using wearable technology or bluetooth proximity detection is able to identify face-to-face groups using non-obtrusive methods. However, the data used in this previous work lacks measures of people’s opinions, attitudes, and beliefs, and thus is unable to link the observed group dynamics with the opinion distribution across people and groups and related dynamics of opinion change and distribution, as we do in this paper.

The most ambitious effort to study face-to-face groups using computational social science techniques we know of is that of 8 which, in similar design as NetSense, tracked about 1000 freshmen students from an undisclosed European University for several months determining the existence of groups via bluetooth. However, like the other studies mentioned earlier, 8 lacks information on individual opinions and beliefs (and thus cannot link group evolution dynamics to opinion change). While smaller than 8 in terms of sample size, the NetSense data used here has the advantage of tracking participants for almost 2 years, allowing for richer temporal dynamics to be detected. In addition, while 8 discovers a number of descriptive sociometric dynamics of groups similar to the ones we observe, the authors do not develop an underlying formal model capable of predicting the observed patterns, and thus cannot shed light on the individual micro-mechanisms underlying the observed patterns 32 .

The initial number of participants in the NetSense study is 196. However, its retention rate is \(71 \%\) over all four semesters. This is comparable to published studies with similar sample sizes 33 and avoids the pitfalls of low retention rates which have been shown to seriously affect results 34 . Like previous work, which has studied group dynamics using data collected from “captive” populations (e.g., MBA executives, graduate and undergraduate students, workers at scientific labs and so forth 8 , 19 , 20 , 25 ), our study also uses a student population, namely, a sample of Freshmen who began their studies at the University of Notre Dame in 2011. In studying face-to-face groups “in the wild” 20 , it is important to note that the main scientific issue is not necessarily scale or “representativeness” in the probability sample sense. Yet, the University of Notre Dame has a strongly diverse undergraduate student body, both socio-demographically and geographically 22 with \(89\%\) of the students from out-of-state 35 . Thus, we consider these students a representative sample of the life experience for half of the U.S. population born around the year 1993. In that respect, our study is also similar to many other published studies focusing on social network dynamics among individuals undergoing other significant transitions, like going into retirement or having a child 33 , 36 , 37 .

Beyond this, studies like ours need to worry about when studying group dynamics in real-life settings is whether the human gatherings detected are in fact face-to-face groups in the sociological sense 1 , 3 , as the scientific objective is to determine the “life-cycle” of actual groups on the ground, not ersatz groups. Therefore, we need data with high ecological validity of the underlying data, study location, and participant population 38 and the NetSense data provides that feature. Absent high ecological validity, even the largest sample of participants will yield the wrong (or misleading) answers.

University of Notre Dame has a nearly entirely residential campus located on north outskirts of South Bend city, Indiana, in the Midwestern United States. Hence, student life there, especially during the first 2 years of study, revolves mostly around activities (studies, recreation, social and religious life) located exclusively on campus. In that sense, University of Notre Dame is a nearly perfect closed-system, a “social laboratory”, where generic behavioral processes applicable to all human groups can be observed with little exogenous disturbance. For the time of observation, almost all the groups to which individuals belong are located on campus. Additionally, for most of these students, this is their first long-time away from home and family, creating a transformative and shared experience in their social lives 39 , 40 . The social ties students form at this stage are both personally and sociologically significant as are the groups that they join 1 . Furthermore, at Notre Dame, freshmen students are required to take a specific set of foundational courses heavily oriented toward philosophy, theology, and humanities to delay disciplinary specialization until the second academic year when students can declare majors. This allows for friendships and groups to form invariant of major, as well as keep specialized courses from initially forcing connections between particular students. As such, in this case, given the scientific objective to study general behavioral processes of face-to-face group formation and evolution at fine-grained time-scales, restriction to a student population should pose no barrier to proper inference.

Sociological processes for determining social group dynamics

Although there is little empirical research on face-to-face group membership dynamics 20 , theoretical work relying on agent-based modeling and simulation in computational social sciences points to three basic processes determining social group dynamics: selective interaction, network-based recruitment, and value homophily. These intuitions can be used as the building blocks to construct a formal model that can shed light on the micro-mechanisms that account for patterns of group evolution observed in the data. First, people selectively concentrate their interactions on members of the groups to which they belong and not on outgroup members 4 , 41 . The reason is a limited budget of time and energy a person can devote to interactions with others. The more people interact in face-to-face settings, the more likely they are to form strong attachments with one another 1 , 42 and to stay with a group 4 . Still, some dyadic ties link members to individuals outside of their current groups 2 . These ties enable individuals to join new groups via the process of network-based recruitment 5 , 7 . The more time and energy people spend interacting with others outside the group, the more likely it is that they will leave their current groups and join new groups 12 . Hence, the extent to which groups attract an individual’s time and energy is an important determinant of whether individuals will stay with the group 5 , 7 .

The benefit that people gain from their current group memberships depends on the extent to which they share important attributes with others, referred to as homophily . It may include shared socio-demographic characteristics 41 , 42 , and shared beliefs and opinions, referred to as value homophily 41 . People are also likely to leave a group when others disagree with them. Because people share values, beliefs, and opinions via social interaction, selective interaction and homophily can be mutually reinforcing 4 .

Utility of group member interactions

To account for the aforementioned sociological processes that drive social group dynamics, we develop a formal model based on subjective utility, a common modeling approach in behavioral science 43 . In the model, member v gains utility from interactions with member m within group g . This utility is a function of the interacting members’ fractions of group meeting attendance and their stances on an attribute a . As reported in 44 , ruling parties tend to have an equally negative attitude toward members of opposition parties. Thus, we treat holders of neutral and extremist views equivalent in terms of the utility of their interactions with other members of a group. We also assume that the utility gain from interactions with members sharing the same stance is twice as large as the utility loss from interactions with members holding different stances. This assumption yields a simple yet effective model.

In the SM, we show results under an alternative assumption that extremists dislike neutrals less than the opposing extremists while neutrals equally less like interactions with other neutrals. Both models yield similar results. The essential property of both models is that individuals optimizing utility drive up polarization of groups. In the SM, we show that replacing the coefficient 2 in Eq. ( 1 ) with a parameter \(\alpha \ge 1\) and the coefficient 1/2 in Eq. ( S2 ) with a parameter \(\beta \le 1\) preserves this property.

We denote the liberal stance by \(-1\) , neutral by 0 and conservative by 1. To express relations of stances to each other, we can place them as vertices of the equilateral triangle, with the neutral stance at the top, the liberal stance at left bottom and the conservative stance at right bottom. Then, for each stance s , stance \(s^+\) denotes its clockwise neighbor, while \(s^-\) its counterclockwise neighbor. With this notation, \(0^+=1\) and \(0^-=-1\) , etc. Let for a node m , \(s_{m,a}\) denotes its stance for attribute a while for a member v of group g , let \(w_{v,g}\) denote a fraction of meetings of g that v attended. Then, utility of v from interactions within g is

where \(W^s_{g,a} = \sum _{m\in g\cap s_{m,a}=s} w_{m,g}\) . Thus, the utility of interactions of node v with each member of the group g , including v , is represented by the product of fractions of the meetings in which each of these two members participated. For each pair of members of the same stance the doubled result is added to the utility, while for each pair with different stances, the result is subtracted from the utility. We include v in the list of members of the same stance so it represents the expected vote for this stance. The total utility of node v from interactions with members of g is just the sum of utility over all attributes. The utility of an entire group is the sum of the total utility of all members of this group.

We hypothesize that the members attempting to maximize their utilities drive group evolution dynamics. Indeed, it is natural to expect that people will seek their own benefit, and that they will make changes in their group membership to seek an improvement to their utility. If this is the only acting criterion when an individual makes a change, this change is egocentric . However, participants might also consider the feelings of others when changing group membership or opinions. To account for this, we introduce another criterion based on the average benefits of all group members. This criterion is called strongly altruistic , since it is close to the traditional definition of altruism in the social sciences 45 . However, research in economic games also shows that people are more cooperative when they interact with others whom they believe share a group identity 46 or who share opinions with members of the group 47 . In the dictator game, players in the dictator role transfer more money when they believe the recipient shares their group identity 48 . Hence, it is reasonable to suppose that people making a decision about a change, will take into account how this change will affect members with whom they share a stance on the involved attribute. Accordingly, we refer to a change as weakly altruistic if the total utility change for all members who share the most of their stances, and thus, value homophily, with the person making the change, is positive.

Formally, all three types of changes use the same function computing a difference in utility for a certain subset of members of a group g on which node v , making a change, focuses on. This subset contains just v for egocentric change, the entire group for strongly altruistic change, and all nodes in g with the same stances as v for weakly altruistic change. Therefore for node v in a group g , we compute the utility change by subtracting v ’s utility before the change from this utility after the change. A change is accepted if, across all attributes, the sum of utility changes for all member in the focused subset is positive.

We computed the frequency of the three types of criteria in the empirical data. The left side of Table  2 shows the results. On average, \(93 \%\) of changes made by members are egocentric, \(85 \%\) of them are weakly altruistic, and \(81 \%\) are strongly altruistic. These results show that large majority of individuals make group changes benefiting not only themselves, but also others. It seems counter-intuitive that an average of \(7 \%\) of changes result in no utility benefits for the change-making individual in any way. Yet, these types of changes are likely made due to sudden and unexpected circumstances that cannot be traced by our indicators of value homophily. E.g., leaving a group due to the discovery of irrevocable differences of personalities with some members). Interestingly, \(81\%\) of changes also benefited those who did not even align their stances with the node making a change. This might well be an unintentional consequence of egocentric changes. For example, when a holder of minority stances in a group leaves motivated by the low utility this holder is gaining, all holders of majority stances gain utility, and their total gain may be higher than the utility lost by the peers of the leaving member.

Additionally, we found that the differences in utility resulting from the changes made versus not made in the data are always positive. Yet, their magnitude depends on the type of changes compared (see “ Methods ” for details). The biggest differences arise for egocentric changes. Yet, they vary significantly from 27 to \(45\%\) . The smallest are seen for the strongly altruistic changes, for which the differences vary the most from 2 to \(32\%\) .

In addition, the utility gains observed in each semester increase group value homophily. This increases stance polarization across groups. To quantify this change, we define a measure of polarization as a function of stance alignment. To measure polarization across a group, we sum the squares of differences between globally expected fractions of stances of all attributes and the actual fraction of these stances in each group. More formally, let \(W^s_{g,a}\) denote the sum of fractions of attendances for all group members in g with stance s , while \(W_{g,a}=\sum _{s={-1}}^1 W^s_{g,a}\) denote the sum of such fractions for all group members. Then, \(G^s_{g,a}=w_{g,a}\frac{\sum _g W^s_{g,a}}{\sum _g W_{g,a}}\) denotes the expected attendance of stance s for attribute a in group g . With this notation, the polarization of group g on attribute a can be expressed as

The total group polarization is just the sum of the values of this function for each attribute. Polarization is 0 when there is a full agreement between the global and local frequencies of stances among members. It grows when members increasingly align their stances with each other. This can be accomplished within each group by members either changing their stances or leaving groups in which their stance is the minority, and joining those where their stance is the majority. Figure  1 illustrates a group evolution in which in each step both utility and polarization increase for groups involved in a change.

Example of group dynamics with polarization and utility growth over time. ( a ) Evolution of group memberships, with three potential groups and nine different participants. These individuals hold three different stances (each marked by its own color, either blue, orange, or grey) for some arbitrary attribute. ( b ) The increase of group utility and group polarization in discrete steps as the members of the example groups change membership to maximize their utility. All steps made are egocentric and altruistic.

For each semester boundary, we use our polarization measure to compute the actual trend of value homophily within the NetSense data by subtracting polarization for each group before a change from this polarization after the change in membership. The right side of Table  2 shows the results that show polarization monotonically increasing over time.

Now, by examining Eq. ( 1 ) for utility and Eq. ( 2 ) for polarization, it is clear that increasing group homophily imposes stronger polarization and a sequence of maximal (i.e., increasing utility the most) strongly altruistic changes irreversibly push groups toward the full stance polarization 49 . Given this, we can conclude that for any state in a group not fully homogeneous, there is a sequence of strongly altruistic changes leading toward complete polarization. These steps increase consensus on stances among group members, showing that value homophily has strong effects on the society-wide distribution of stances. Since \(81\%\) of all changes in the NetSense data are actually strongly altruistic, the above trends are strong in this empirical dataset.

Analyzing the NetSense data even further, we also discover that students holding the campus majority stances change their group membership less frequently than do those holding less popular stances. The data shows that quantitatively, the majority stance holders moving to the next semester retain membership in about \(85\%\) of the groups while such fraction for students with the minority stance is \(75\%\) . Additionally, we find that the majority stance holders not only retain membership of groups for a greater amount of time, but they also enjoy a majority control of membership in a greater number of groups. We find that on average, the number of groups in which the majority of members hold the minority opinion is just 53%, barely over half of the number of groups with a majority of members holding the majority opinion. We also measure the dependence of utility gained on the popularity of stances of a group member on four attributes (we excluded the drinking habits attribute since its values represent ranges of frequency, not stances). The ratio of the average utility gained by members with the minority stance to such utility earned by members with the majority stance is the highest for the legalization of gay marriage (0.55), followed by political leaning (0.29), legalization of marijuana (0.25), and abortion (0.11). We also present the fractions of people with majority and minority stances for the entire population and for the top \(10\%\) of members, ordered by the utility they received. Table 3 presents these results.

We find that the nodes gaining high utility are more likely to hold majority stances than expected by chance based on their fraction in the entire population. Likewise, the high utility nodes are statistically less likely to hold a minority stance, as indicated by the fraction of minority stance holders in the entire population.

To summarize, this section demonstrates that our utility increases for \(93 \%\) of the changes made by members in the NetSense dataset and \(81 \%\) of all changes also increase utility across the entire group. Moreover, nodes observed to make frequent group membership changes have lower utility than other nodes.

This validates our hypothesis that members attempting to increase their utilities are driving social group dynamics.

Predicting group membership and opinion changes

To test this hypothesis further, we implement an analytical model to forecast future group affiliation and stance changes based on this hypothesis. We hypothesize that maximizing a person’s total utility from group memberships also requires accounting for the time and effort of attending group meetings. Therefore, the total utility for predictions is then the sum of two terms: the ingroup interaction utility and the utility of membership in the given number of groups. Our model analytically computes the ratio of these two terms that maximizes the prediction performance on training subset of our data (the first two semester boundaries), and then predicts membership and opinion changes that specifically improve utility on the remainder (the last semester boundary). To give some reference of the quality of performance of our model, we use a random baseline that randomly predicts changes across our data (see “ Methods ” for details on the analytical model, and SM for the baseline implementation).

Table 4 shows the results for the test data predictions of both the analytical model and the random baseline compared to our ground truth. The results indicate that group joining behavior is more predictable than both group leaving and stance change behavior. This is important, as the random model shows that, as a baseline, this task is very difficult to perform. This is because for group leaving and opinion changes, a person considering a change belongs only to a few groups to leave and holds a few opinions to change. On the other hand, there many groups that are open and available to join for such a person. Still, even for group leaving and opinion changing, prediction quality of the analytical model is substantial when compared to the baseline. Overall, the results demonstrate that the analytical model is very effective at predicting changes. These results also validate maximizing the group membership utility as a process driving group dynamics.

Previous theoretical and simulation-based works in computational social sciences and complex networks has pointed to a variety of processes involved in selecting group affiliation by humans. Those include network ties, rates of interaction and time investment, and shared knowledge and opinions 2 , 4 , 5 , 7 . Yet, empirical validation of these processes and their relevance to explaining and predicting group affiliation and opinion change behavior has been scant. The main reason is a paucity of naturalistic data in which there is a mapping from such behavior to data on beliefs and opinions of group members.

In this paper, we are using the NetSense study dataset, which is unique in containing longitudinal information about communications, locations, and opinions for randomly selected students from the University of Notre Dame geographically diverse student body. We detected in this data 436 groups and their evolution across four semesters of activities. The data captures a large representative sample of social group dynamics within the context of college life. We observe that groups slowly transition toward ingroup stance homogeneity. Moreover, we find that the frequencies of group changes by individuals strongly depend on popularity of their stances. We use these empirical observations to formulate and validate our hypothesis about the group evolution.

Theoretical works have postulated that the level of homophily with other members in groups and communities defines the benefit of membership 4 , 7 . Recent works have also analyzed the roles of homophily in members popularity 15 , ranking of minorities 16 , and structural change 17 , respectively.

In this paper, we formalize a notion of utility of group membership as a function of compatibility of opinions between a member and all others in its group. This formalization enables us to integrate in a single model the three aforementioned phenomena and to account for the two dynamic patterns observed in empirical data. Using this data, we simulate only those changes of opinions or group memberships, which increase members’ utility. We find that this process recreates the group dynamics observed in empirical data and confirms the theoretically postulated role of homophily in these dynamics.

The total member utility includes another term, which is a function of the number of groups to which this member belongs to account for commitment limit each person has. We demonstrate that when applied to our empirical data, this utility increases after each observed change. This leaves an interesting open question: whose average utility should a member maximize when considering a change of an opinion or group membership? To probe different answers, we introduce three maximization criteria. The first, called egocentric, requires that the change of utility of a decision-maker needs to be positive for the move to be accepted. We also consider a more considerate alternative in the form of the weakly altruistic criterion, which requires the positive average change of utility for group members sharing stances with the node making the change. Finally, we produce a strongly altruistic criterion, when all group members on average benefit from a change.

We find that, on average, \(93 \%\) of changes made by students in NetSense data are egocentric and \(85 \%\) are weakly altruistic. Additionally, over \(76 \%\) of these changes are also strongly altruistic at the first semester boundary, with this fraction growing to \(86 \%\) at the third semester boundary. These changes increase polarization, resulting in its observed growth. These results empirically confirm that it is important to allow for an altruistic component in agreement with previous work linking altruism, empathy, and group identity. Another observation from empirical data finds the differences in frequency of group membership changes between individuals espousing majority and minority stances. Our utility decays with the decreasing popularity of the member stances. This naturally increases such member motivation to attempt changes to maximize the utility. This agreement with empirical observations validates our hypothesis that utility maximization drives group dynamics.

Subsequently, we introduce a predictive analytical model based on utility maximization to forecast the evolution of groups. It balances the two terms of total utility at the value that globally maximizes model performance on training data. This model accurately predicts affiliation (group joining and group leaving) and stance change.

Overall, these results advance our understanding of group dynamics. They also have important implications for future work on this topic in social sciences, computational social systems and complex networks. In particular, our results show that core processes isolated in previous theoretical and simulation-based work are applicable to naturalistic settings, uncovering the motivations leading people to join or leave groups. We also identify two side effects of utility maximization. The first is that holders of unpopular stances gain lower utility from ingroup interactions and have increased frequency of group changes. The second reveals that desire to spend more time interacting with like-minded others contributes to the increased stance polarization across groups. This kind of polarization has been noted previously 50 , where seemingly innocuous stances and beliefs become highly influential markers determining who interacts with whom, generating small “echo chambers” characterized by opinion homogeneity. This is especially relevant in contemporary contexts featuring relatively low barriers to geographic mobility allowing persons to self-select into social environments and to find and affiliate with groups of their choice online or face-to-face.

Group detection within NetSense

The first step in analyzing group dynamics within the NetSense dataset is to extract groups from the Bluetooth proximity data. As mentioned in the main text, we extract groups from this proximity data using the hierarchical clustering method proposed in 23 . This method first finds persistent connected components in the dynamic network generated from NetSense’s dyadic Bluetooth interactions. Each detected component is a potential group meeting. The largest sequence of components such that each component includes at least a fraction \(f_i\) of the union of the members of all other components is considered a representation of a meeting of a single group. Members of this group are the nodes that attend at least a fraction \(f_m\) of the meetings of this group. Finally, each meeting that attracts less than a fraction \(f_{mi}\) of its group members is removed. The details of the algorithm to extract groups with the required properties and for finding the best values of parameters that are \(f_i=0.6, f_m=0.5, f_{mi}=0.3\) are presented in 23 . Since this reference uses the same NetSense data as our study does, we use these parameter values to extract groups.

Bluetooth interactions collected on the phones of participants include proximity data of all cell phones. Yet, we found that the ratio of non-participant meetings with participants (required to establish a group) to participant meetings with participants is 0.0489. Thus, no phone owned by a person not enrolled in the NetSense study passed the described above group membership requirements. As the result, the extracted groups include only participants of the NetSense study.

Since groups are not labeled in the data we work with, we must extract a mapping to identify a self-subsisting group across consecutive semesters. The mapping uses the Jaccard Similarity on the two sets of members of both groups. A threshold is set for similarity level for the latter group to be a continuation of the former. By tracking these mappings across semesters, we can identify new or missing members between subsequent group reincarnations. We consider so-identified new members as the true positive cases for joining the affected group and treat missing members as ground truth cases for leaving. However, when an entire group finds no succeeding group mapping in the following semester, we do not record the ingroup participants as a ground truth case of leaving the group en masse. Instead, we just dissolve the group by removing each node from this group for the new semester. The reasoning behind this decision is that the data collection process for NetSense could sometimes be noisy 22 causing the matches to be lost.

With these consistent initial group mappings, we observe in the ground truth data that the majority of people join groups with members with whom they share some communication links. This is in agreement with sociological work on network-based recruitment into groups 5 , 7 discussed previously. To account for this phenomenon, we introduce a lower bound on the number of connections that a given person has with current members of the new group over all meetings in which they participate. This bound is the product of the fraction of group’s members linked via communication contacts to that individual in the current semester and the number of meetings held by the group’s members, which measures group stability. We selected the bound value in such a way that a typical group with five members of which two are familiar to the person attempting to join would require holding 50 meetings to qualify. However, just ten meetings would have sufficed if the joining person had connections to all five members.

Utility difference for changes

Given group mappings between semesters, we can track for each participant all the changes made and not made in group memberships. For example, every group to which participant v does not belong in the subsequent semester is a group that v could have joined, but did not. Additionally, every group that v is a part of in the following semester is a group that v could have left, but abstained from doing so. Between the 196 NetSense participants, 889 group membership and opinion changes were made across all three semester boundaries, and 16,348 possible changes were not. To quantify the actual utility differences between changes made and changes not made, we subtract utility of node v in group g for some attribute a before the change is made from this utility after the change. For a node joining a group without an associated attendance history, the average frequency of attending meetings of its other groups is used, and the frequency is set to 1 if the former is not available. For opinion changes, the total utility change is computed by subtracting the sum of the utility for each group to which node v belongs before the change from that sum after the change.

The analytical predictive model

Our model aims to predict the set of changes maximizing the utility individuals derive from the groups to which they belong or will join with the opinions they hold. In this analytical model, the utility functions use a model parameter x . It defines an exchange rate between utility derived from ingroup interactions versus an adjustment representing the commitment of belonging to the current number of groups. Our analytical model maximizes this augmented version of utility. To find the optimal x , we define a penalty function for changes predicted by the model but not made in reality (false positive changes) and for changes not predicted by the model but made in reality (false negative). There is no penalty for changes predicted correctly (true positive) or changes not made and predicted as such (true negative). Using training data and this penalty system, we find the optimal value of the parameter x . The exact mechanism for analytically solving for the optimal x is shown in the next subsection. Overall, to predict change in group membership and opinions, the model finds the augmented utility differential by subtracting utility before a simulated change is made from this utility after the change.

Equation ( 1 ) formally defines the augmented utility for a node v in group g with a stance on attribute a . Summing over all attributes and over all groups to which v belongs defines the overall utility derived by node v from all ingroup interactions. However, belonging to a group requires the commitment of time, a resource limited for all humans, for attending meetings. Human interactions satisfy a genuine human need, but this part of utility decays from the utility with the optimal time commitment to groups for both under and over participation in groups. This decay grows non-linearly with the imbalance of the committed time. To account for this, we define a quadratic function for each node v , \(f^g(v)=W_v(2{\overline{W}} - W_v)\) , where \(W_v=\sum _{\{g|v\in G\}} w_{v,g}\) is the total time commitment of node v to all groups to which this node belongs and G denotes a set of all currently existing groups. \({\overline{W}} = \frac{1}{n} \sum _{v \in P} W_v\) is the average time commitment of all nodes to all groups where P denotes a set of all participants, and n stands for the total number of nodes. Using the data from the first two semester boundaries to establish an empirical value of average, we found that \({\overline{W}} = 3.95\) . The sum of the ingroup interaction and time commitment utility of a node represents its augmented utility.

Both terms of this augmented utility are function of time, but represent different units, therefore we introduce an exchange rate, x , between the two. Hence, a person v who belongs to group g and holds a set A of opinions (attribute values) in a given semester, gains the augmented utility defined as

where \(G_v\) denotes the set of groups to which node v belongs. When considering a membership change, our model evaluates Eq. ( 3 ) before and after the change is made, subtracting the former from the latter. If the difference is positive, the move is eligible for execution, otherwise it is not. For opinion change, the model subtracts the sum of augmented utilities for v in all the groups to which v belongs before a change from such utility after the change.

The non-linearity of the commitment function makes the eligibility of moves dependent on the order in which they are attempted. When the current number of groups to which a node belongs is below four, the new group joining proceeds to trial before any other change. Otherwise, the group leaving, if one exists, has the execution priority. Opinion changes proceed only after all other changes have finished their trials.

Analytically defining the optimal x

To find an optimal value of x , we define penalty functions for group affiliation and opinion changes. Our model minimizes the total penalty incurred for all individuals within our training data (the first two semester boundaries) to find the globally optimal value of x . The total penalty used is

where C represents the total penalty for all individuals considering a change, x denotes the exchange rate parameter, v is the change-making individual, t represents the semester, P is the set of all study participants, and S is the constant denoting the number of semesters. \(G^{-}_{v}\) denotes the groups of which v is currently a member, and \(G^+_v\) denotes the groups that v is eligible to join in semester t . Value \(p^l_{g,v,t}\) is the penalty for leaving or not leaving group g in semester \(t+1\) , and \(p^j_{g,v,t}\) is the penalty for joining or not joining the group g by v in semester \(t+1\) .

Leaving a group

We can construct the leaving group penalty function based on the utility function as follows:

where \(\Delta ^l_g f^u_v\) is computed by subtracting the augmented utility \(f^u_v\) before v leaves group g from that utility after the leave, while \(|\Delta ^l_g f^u_v|\) denotes the absolute value of this difference. Using the absolute value in the term containing the unknown parameter x makes the entire function non-linear with respect to x . The factor \(m^l_{v,g,t}\) represents if person v leaves group g in semester t . In particular, if this person indeed makes the predicted change in the ground truth data, then the value of \(m^l_{v,g,t}\) is 1, otherwise, it is \(-1\) . The intuition behind such a penalty function is that a person avoids the penalty for a change if and only if the resulting utility differential is positive.

For changes with a positive utility, the value of \(m^l_{v,t}\) is equal to 1, so no penalty is incurred when the change is positive. However, if the value of \(m^l_{v,t}\) is equal to \(-1\) , a penalty is incurred by the model for predicting a change not made in the ground truth data. Conversely, a change associated with a negative utility differential, but made in the ground truth data incurs a penalty since with \(m^l_{v,t}=1\) , the absolute value of the second term is added to the first. In opposite case, when the second term is equal to the first and when \(m^l_{v,t}=-1\) , the expression in Eq. ( 5 ) reduces to 0.

Joining a group

We construct the joining group penalty function by replacing the difference in the augmented utility for leaving a group in Eq. ( 5 ) with the utility differential of joining a group from. Hence, the penalty for joining a group, \(p^j_{g,v,t}\) , has properties analogous to properties of penalty for leaving a group. Naturally, the weight of node v is not known before the individual joins the group. Therefore, as before, we use either the average rate of all of v ’s other groups or set the attendance rate to 1.

Solving for the optimal x

The optimal value of x defined by Eq. ( 4 ) is efficiently solvable by replacing each derivative of an absolute value of a term with the product of the sign function of this term and the derivative of this term, yielding the expressions in the form

where \(A^l_{g,v,t}=w_{g,v}(2W_v-w_{g,v}-2\overline{W})\) and \(B^l_{v,t}=\sum _{a\in A}2W^{s_{v,a}}_{g,a}-W^{s^+_{v,a}}_{g,a}-W^{s^-_{v,a}}_{g,a}\) , where g is a group that node v is leaving. Each term defined by Eq. ( 6 ) changes its value only once for \(x=B^l_{v,t}/A^l_{g,v,t}\) . We will refer to these values as signum discontinuity points. The product of the number of nodes and the number of groups whose memberships are subject to change is the upper bound for the number of signum discontinuity points. After sorting these discontinuity points from smallest to largest, and processing them in this order, we can than find optimal value of x , by computing the derivative’s value at each discontinuity point to identity the smallest value of penalty and corresponding value of x in the current interval between the previous and the current discontinuity point. After processing all discontinuity points, we will have the minimum penalty and the value of x at which it is reached. For our data specifically, using the observations from the first two semester boundaries, we found the optimal x to be \(1.4095 \times 10^{-4}\) .

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Acknowledgements

We thank S. Levin for the comments. This work was partially supported by the Office of Naval Research (N00014-15-1-2640), the Army Research Office (W911NF-16-1-0524, W911NF-17-C-0099), and the Defense Advanced Research Projects Agency (W911NF-17-C-0099).

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These authors contributed equally: James Flamino, Boleslaw K. Szymanski and Ashwin Bahulkar.

Authors and Affiliations

Network Science and Technology Center, Rensselaer Polytechnic Institute, Troy, NY, 12180, USA

James Flamino, Boleslaw K. Szymanski & Ashwin Bahulkar

Department of Physics, Applied Physics, and Astronomy, Rensselaer Polytechnic Institute, Troy, NY, 12180, USA

James Flamino & Boleslaw K. Szymanski

Department of Computer Science, Rensselaer Polytechnic Institute, Troy, NY, 12180, USA

Boleslaw K. Szymanski & Ashwin Bahulkar

Społeczna Akademia Nauk, Łódź, Poland

Boleslaw K. Szymanski

U.S. Army Research Laboratory, Adelphi, MD, 20783, USA

Department of Sociology, University of California, Los Angeles, CA, 90095, USA

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A.B. and B.K.S. designed the study, B.K.S formalized the models, A.B. and J.F. implemented the models and conducted the computational experiments. J.F. generated the figures. O.L. and K.C. participated in study design and interpretation of data. A.B., J.F., O.L., and B.K.S, wrote the paper with input from all authors.

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Flamino, J., Szymanski, B.K., Bahulkar, A. et al. Creation, evolution, and dissolution of social groups. Sci Rep 11 , 17470 (2021). https://doi.org/10.1038/s41598-021-96805-7

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Chapter 10. Working Groups: Performance and Decision Making

10.1 Understanding Social Groups

Learning Objectives

• Define the factors that create social groups and perceptions of entitativity.
• Define the concept of social identity, and explain how it applies to social groups.
• Review the stages of group development and dissolution.

Although it might seem that we could easily recognize a social group when we come across one, it is actually not that easy to define what makes a group of people a social group. Imagine, for instance, a half dozen people waiting in a checkout line at a supermarket. You would probably agree that this set of individuals should not be considered a social group because the people are not meaningfully related to each other. And the individuals watching a movie at a theater or those attending a large lecture class might also be considered simply as individuals who are in the same place at the same time but who are not connected as a social group.

Of course, a group of individuals who are currently in the same place may nevertheless easily turn into a social group if something happens that brings them “together.” For instance, if a man in the checkout line of the supermarket suddenly collapsed on the floor, it is likely that the others around him would begin to work together to help him. Someone would call an ambulance, another might give CPR, and another might attempt to contact his family. Similarly, if the movie theater were to catch on fire, a group would form as the individuals attempted to leave the theater. And even the class of students might come to feel like a group if the instructor continually praised it for being the best (or worst) class that he or she has ever had. It has been a challenge to characterize what the “something” is that makes a group a group, but one term that has been used is entitativity (Campbell, 1958; Lickel et al., 2000). Entitativity refers to something like “groupiness”— the perception, either by the group members themselves or by others, that the people together are a group .

The concept of entitativity is an important one, both in relation to how we view our ingroups, and also in terms of our perceptions of and behavior toward our outgroups. For example, strong perceptions of ingroup entitativity can help people to retain their sense of collective self-esteem in the face of difficult circumstances (Bougie, Usborne, de la Sablonniere, & Taylor, 2011). Seeing our ingroups as more entitative can also help us to achieve our individual psychological needs (Crawford & Salaman, 2012). With our outgroups, our perceptions of their entitativity can influence both our prosocial and antisocial behaviors toward them. For instance, although in some situations individuals may feel more xenophobic toward outgroups that they perceive as more entitative  (Ommundsen, van der Veer, Yakushko, & Ulleberg, 2013), they may in other contexts choose to donate more money to help more entitative outgroups (Smith, Faro, & Burson, 2013).

One determinant of entitativity is a cognitive one—the perception of similarity. As we saw in our discussions of liking and loving, similarity is important across many dimensions, including beliefs, values, and traits. A group can only be a group to the extent that its members have something in common; at minimum, they are similar because they all belong to the group. If a collection of people are interested in the same things, share the same opinions and beliefs, or work together on the same task, then it seems they should be considered—by both themselves and others—to be a group. However, if there are a lot of differences among the individuals, particularly in their goals, values, beliefs, and behaviors, then they are less likely to be seen as a group.

Given the many differences that we have discussed in other chapters between members of individualistic and collectivistic cultures in terms of how they see their social worlds, it should come as no surprise that different types of similarity relate more strongly to perceptions of entitativity in each type of culture. For instance, similarity in terms of personal traits has been found to be more strongly associated with entiativity in American versus Japanese participants, with the opposite pattern found for similarity in terms of common goals and outcomes (Kurebayashi, Hoffman, Ryan, & Murayama, 2012).

People, then, generally get together to form groups precisely because they are similar. For example, perhaps they are all interested in playing poker, or follow the same soccer team, or like martial arts. And groups are more likely to fall apart when the group members become dissimilar and thus no longer have enough in common to keep them together (Crump, Hamilton, Sherman, Lickel, & Thakkar, 2010; Miles & Kivlighan, 2008).

Communication, Interdependence, and Group Structure

Although similarity is important, it is not the only factor that creates a group. Groups have more entitativity when the group members have frequent interaction and communication with each other (Johnson & Johnson, 2012). Although communication can occur in groups that meet together in a single place, it can also occur among individuals who are at great distances from each other. The members of a research team who communicate regularly via Skype, for instance, might have frequent interactions and feel as if they are a group even though they never or rarely meet in person.

Interaction is particularly important when it is accompanied by interdependence — the extent to which the group members are mutually dependent upon each other to reach a goal . In some cases, and particularly in working groups, interdependence involves the need to work together to successfully accomplish a task. Individuals playing baseball are dependent upon each other to be able to play the game and also to play well. Each individual must do his or her job in order for the group to function. We are also interdependent when we work together to write a research article or create a class project. When group members are interdependent, they report liking each other more, tend to cooperate and communicate with each other to a greater extent, and may be more productive (Deutsch, 1949).

Still another aspect of working groups whose members spend some time working together and that makes them seem “groupy” is that they develop group structure—the stable norms and roles that define the appropriate behaviors for the group as a whole and for each of the members. The relevant social norms for groups include customs, traditions, standards, and rules, as well as the general values of the group. Particularly important here are injunctive norms, which specify how group members are expected to behave . Some of these are prescriptive norms, which tell the group members what to do , whereas some are proscriptive norms, which tell   them what not to do . In general, the more clearly defined and the widely agreed upon the norms in a group are, the more  entitativity that the group members will feel.

Effective groups also develop and assign social roles (the expected behaviors) to group members. For instance, some groups may be structured such that they have a president, a secretary, and many different working committees. Different roles often come with different levels of status, or perceived power, and these hierarchies. In general, groups are more effective when the roles assigned to each member are clearly defined and appropriate to those individuals’ skills and goals. Also, if members have more than one role, for example, player and coach, it is important that these roles are compatible rather than contradictory. High-performing groups are thus able to avoid placing members under role stress . This   occurs when individuals experience incompatible demands and expectations within or between the roles that they occupy , which often negatively impacts their ability to be successful in those roles (Forsyth, 2010).

Social Identity

Although cognitive factors such as perceived similarity, communication, interdependence, and structure are often important parts of what we mean by being a group, they do not seem to always be necessary. In some situations, groups may be seen as groups even if they have little independence, communication, or structure. Partly because of this difficulty, an alternative approach to thinking about groups, and one that has been very important in social psychology, makes use of the affective feelings that we have toward the groups that we belong to. As we have read, social identity refers to the part of the self-concept that results from our membership in social groups (Hogg, 2003). Generally, because we prefer to remain in groups that we feel good about, the outcome of group membership is a positive social identity—our group memberships make us feel good about ourselves.

According to the social identity approach, a group is a group when the members experience social identity—when they define themselves in part by the group that they belong to and feel good about their group membership (Hogg, 2010). This identity might be seen as a tendency on the part of the individual to talk positively about the group to others, a general enjoyment of being part of the group, and a feeling of pride that comes from group membership. Because identity is such an important part of group membership, we may attempt to create it to make ourselves feel good, both about our group and about ourselves. Perhaps you know some people—maybe you are one—who wear the clothes of their sports team to highlight their identity with the group because they want to be part of, and accepted by, the other group members. Indeed, the more that we see our social identities as part of our membership of a group, the more likely we are to remain in them, even when attractive alternatives exist (Van Vugt & Hart, 2004).

The Stages of Group Development

Although many groups are basically static, performing the same types of tasks day in and day out, other groups are more dynamic. In fact, in almost all groups there is at least some change; members come and go, and the goals of the group may change. And even groups that have remained relatively stable for long periods of time may suddenly make dramatic changes; for instance, when they face a crisis, such as a change in task goals or the loss of a leader. Groups may also lose their meaning and identity as they successfully meet the goals they initially set out to accomplish.

One way to understand group development is to consider the potential stages that groups generally go through. One widely used approach here is the model developed by Tuckman and Jensen (1977). As you can see in Figure 10.3, “Stages of Group Development,” the different stages involve forming, storming, norming and performing, and adjourning .

The  forming stage  occurs  when the members of the group come together and begin their existence as a group . In some cases, when a new group, such as a courtroom jury, forms to accomplish a goal, the formation stage occurs relatively quickly and is appropriately considered the group’s first stage. In other cases, however, the process of group formation occurs continually over a long period of time, such as when factory workers leave their jobs and are replaced by new employees.

The forming stage is important for the new members, as well as for the group itself. During this time, the group and the individual will exchange knowledge about appropriate norms, including any existing group structures, procedures, and routines. Each individual will need to learn about the group and determine how he or she is going to fit in. And the group may be inspecting the individual’s characteristics and appropriateness as a group member. This initial investigation process may end up with the individual rejecting the group or the group rejecting the individual.

If the forming stage can be compared to childhood, there is no doubt that the next stage— storming —can be compared to adolescence. As the group members begin to get to know each other, they may find that they don’t always agree on everything. In the storming stage, members may attempt to make their own views known, expressing their independence and attempting to persuade the group to accept their ideas . Storming may occur as the group first gets started, and it may recur at any point during the group’s development, particularly if the group experiences stress caused by a negative event, such as a setback in progress toward the group goal. In some cases, the conflict may be so strong that the group members decide that the group is not working at all and they disband. In fact, field studies of real working groups have shown that a large percentage of new groups never get past the forming and storming stages before breaking up (Kuypers, Davies, & Hazewinkel, 1986).

Although storming can be harmful to group functioning and thus groups must work to keep it from escalating, some conflict among group members may in fact be helpful. Sometimes the most successful groups are those that have successfully passed through a storming stage, because conflict may increase the productivity of the group, unless the conflict becomes so extreme that the group disbands prematurely (Rispens & Jehn, 2011). Groups that experience no conflict at all may be unproductive because the members are bored, uninvolved, and unmotivated, and because they do not think creatively or openly about the topics of relevance to them (Tjosvold, 1991). In order to progress, the group needs to develop new ideas and approaches, and this requires that the members discuss their different opinions about the decisions that the group needs to make.

Assuming that the storming does not escalate too far, the group will move into the norming stage, which is when the appropriate norms and roles for the group are developed.  Once these norms have been developed, they allow the group to enter the performing stage, which is when group members establish a routine and effectively work together . At this stage, the individual group members may report great satisfaction and identification with the group, as well as strong group identity. Groups that have effectively reached this stage have the ability to meet goals and survive challenges. And at this point, the group becomes well tuned to its task and is able to perform the task efficiently.

In one interesting observational study of the group development process in real groups, Gersick (1988, 1989) observed a number of teams as they worked on different projects. The teams were selected so that they were all working within a specific time frame, but the time frame itself varied dramatically—from eight to 25 meetings held over periods ranging from 11 days to six months. Despite this variability, Gersick found that each of the teams followed a very similar pattern of norming and then performing. In each case, the team established well-defined norms regarding its method of attacking its task in its very first meeting. And each team stayed with this approach, with very little deviation, during the first half of the time it had been allotted. However, midway through the time it had been given to complete the project (and regardless of whether that was after four meetings or after 12), the group suddenly had a meeting in which it decided to change its approach. Then, each of the groups used this new method of performing the task during the rest of its allotted time. It was as if an alarm clock went off at the halfway point, which led each group to rethink its approach.

Most groups eventually come to the adjourning stage, where group members prepare for the group to end . In some cases, this is because the task for which the group was formed has been completed, whereas in other cases it occurs because the group members have developed new interests outside the group. In any case, because people who have worked in a group have likely developed a strong identification with the group and the other group members, the adjournment phase is frequently stressful, and participants may resist the breakup. Faced with these situations, individuals frequently plan to get together again in the future, exchanging addresses and phone numbers, even though they may well know that it is unlikely they will actually do so. Sometimes it is useful for the group to work ahead of time to prepare members for the breakup.

Keep in mind that this model represents only a general account of the phases of group development, beginning with forming and ending with adjourning, and will not apply equally well to all groups . For instance, the stages are not necessarily sequential: some groups may cycle back and forth between earlier and later stages in response to the situations they face. Also, not all groups will necessarily pass through all stages. Nevertheless, the model has been useful in describing the evolution of a wide range of groups (Johnson & Johnson, 2012).

Key Takeaways

• Social groups form the foundation of human society—without groups, there would be no human culture. Working together in groups, however, may lead to a variety of negative outcomes as well.
• Similarity, communication, interdependence, and group structure are variables that make a collection of individuals seem more like a group—the perception of group entitativity.
• Most groups that we belong to provide us with a positive social identity—the part of the self-concept that results from our membership in social groups.
• The more we feel that our identities are tied to the our group memberships, the less likely we are to leave the groups we belong to.
• One way to understand group development is to consider the potential stages that groups generally go through. The normal stages are forming, storming, norming and performing, and adjourning.

Exercises and Critical Thinking

• Compare some of the social groups that you belong to that you feel have high and low levels of entitativity. How do these groups differ in terms of their perceived similarity, communication, interdependence, and structure?
• Describe a situation where you experienced role stress. What were the causes of that stress and how did it affect your performance in that role?
• Think about a group that you belong to now, which is very important to you. Identify one prescriptive and one proscriptive norm for this group. How do you think that these norms help the group to function effectively? What do you think would happen if a group member violated those norms?
• Consider groups that provide a particularly strong social identity for their members. Why do you think social identity is so strong in these groups, and how do you think that the experience of identity influence the group members’ behavior?
• Think about a group that you have been a member of for a long time. Which of Tuckman and Jensen’s stages do you think that the group is currently in? Overall, how well do you think that their stage model helps to explain how this group has developed over time?

Bougie, E., Usborne, E., de la Sablonnière, R., & Taylor, D. M. (2011). The cultural narratives of Francophone and Anglophones Quebecers: Using a historical perspective to explore the relationships among collective relative deprivation, in‐group entitativity, and collective esteem.  British Journal Of Social Psychology ,50 (4), 726-746.

Campbell, D. T. (1958). Common fate, similarity, and other indices of the status of aggregate persons as social entities. Behavioral Science, 3,  14-25.

Crawford, M. T., & Salaman, L. (2012). Entitativity, identity, and the fulfilment of psychological needs.  Journal Of Experimental Social Psychology ,48 (3), 726-730.

Crump, S. A., Hamilton, D. L., Sherman, S. J., Lickel, B., & Thakkar, V. (2010). Group entitativity and similarity: Their differing patterns in perceptions of groups.  European Journal of Social Psychology, 40 (7), 1212–1230. doi: 10.1002/ejsp.716.

Deutsch, M. (1949). An experimental study of the effects of cooperation and competition upon group processes.  Human Relations, 2 , 199–231.

Forsyth, D. (2010). Group dynamics (5th ed.). Belmont, CA: Wadsworth.

Gersick, C. J. (1988). Time and transition in work teams: Toward a new model of group development.  Academy of Management Journal, 31 (1), 9–41.

Gersick, C. J. (1989). Marking time: Predictable transitions in task groups.  Academy of Management Journal, 32 , 274–309.

Hogg, M. A. (2003). Social identity. In M. R. Leary & J. P. Tangney (Eds.),  Handbook of self and identity  (pp. 462–479). New York, NY: Guilford Press.

Hogg, M. A. (2010). Human groups, social categories, and collective self: Social identity and the management of self-uncertainty. In R. M. Arkin, K. C. Oleson, & P. J. Carroll (Eds.),  Handbook of the uncertain self  (pp. 401–420). New York, NY: Psychology Press.

Johnson, D.W., & Johnson, F.P. (2012). Joining Together – Group Theory and Group Skills (11th ed). Boston: Allyn and Bacon.

Kurebayashi, K., Hoffman, L., Ryan, C. S., & Murayama, A. (2012). Japanese and American perceptions of group entitativity and autonomy: A multilevel analysis.  Journal Of Cross-Cultural Psychology ,43(2), 349-364.

Kuypers, B. C., Davies, D., & Hazewinkel, A. (1986). Developmental patterns in self-analytic groups.  Human Relations, 39 (9), 793–815.

Lickel, B., Hamilton, D. L., Wieczorkowska, G., Lewis, A., Sherman, S. J., & Uhles, A. N. (2000). Varieties of groups and the perception of group entitativity.  Journal of Personality and Social Psychology, 78 (2), 223–246.

Miles, J. R., & Kivlighan, D. M., Jr. (2008). Team cognition in group interventions: The relation between coleaders’ shared mental models and group climate.  Group Dynamics: Theory, Research, and Practice, 12 (3), 191–209. doi: 10.1037/1089–2699.12.3.191

Ommundsen R, van der Veer K, Yakushko O, Ulleberg P. Exploring the relationships between fear-related xenophobia, perceptions of out-group entitativity, and social contact in Norway.  Psychological Reports  [serial online]. February 2013;112(1):109-124.

Rispens, S., & Jehn, K. A. (2011). Conflict in workgroups: Constructive, destructive, and asymmetric conflict. In D. De Cremer, R. van Dick, & J. K. Murnighan (Eds.),  Social psychology and organizations  (pp. 185–209). New York, NY: Routledge/Taylor & Francis Group.

Smith, R. W., Faro, D., & Burson, K. A. (2013). More for the many: The influence of entitativity on charitable giving.  Journal Of Consumer Research , 39(5), 961-975.

Tjosvold, D. (1991). The conflict-positive organization. Reading, MA:   Addison-Wesley.

Tuckman, B., & Jenson, M. (1977). Stages of small group development revisited. Group and Organizational Studies, 2, 419-427.

Van Vugt, M., & Hart, C. M. (2004). Social identities as glue: The origins of group loyalty.  Journal of Personality and Social Psychology, 86,  585-598.

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The perception, either by the group members themselves or by others, that the people together are a group.

The extent to which the group members are mutually dependent upon each other to reach a goal.

How group members are expected to behave.

Tell the group members what to do.

Tell them what not to do.

When individuals experience incompatible demands and expectations within or between the roles that they occupy, which often negatively impacts their ability to be successful in those roles.

When the members of the group come together and begin their existence as a group.

Members may attempt to make their own views known, expressing their independence and attempting to persuade the group to accept their ideas.

When the appropriate norms and roles for the group are developed.

When group members establish a routine and effectively work together.

Group members prepare for the group to end.

Principles of Social Psychology - 1st International H5P Edition Copyright © 2022 by Dr. Rajiv Jhangiani and Dr. Hammond Tarry is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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6.2 Group Dynamics and Behavior

Learning objectives.

• Explain how and why group dynamics change as groups grow in size.
• Describe the different types of leaders and leadership styles.
• Be familiar with experimental evidence on group conformity.
• Explain how groupthink develops and why its development may lead to negative consequences.

Social scientists have studied how people behave in groups and how groups affect people’s behavior, attitudes, and perceptions (Gastil, 2009). Their research underscores the importance of groups for social life, but it also points to the dangerous influence groups can sometimes have on their members.

The Importance of Group Size

The distinction made earlier between small primary groups and larger secondary groups reflects the importance of group size for the functioning of a group, the nature of its members’ attachments, and the group’s stability. If you have ever taken a very small class, say fewer than 15 students, you probably noticed that the class atmosphere differed markedly from that of a large lecture class you may have been in. In the small class, you were able to know the professor better, and the students in the room were able to know each other better. Attendance in the small class was probably more regular than in the large lecture class.

Over the years, sociologists and other scholars have studied the effects of group size on group dynamics. One of the first to do so was German sociologist Georg Simmel (1858–1918), who discussed the effects of groups of different sizes. The smallest group, of course, is the two-person group, or dyad , such as a married couple or two people engaged to be married or at least dating steadily. In this smallest of groups, Simmel noted, relationships can be very intense emotionally (as you might know from personal experience) but also very unstable and short lived: if one person ends the relationship, the dyad ends as well.

The smallest group is the two-person group, or dyad. Dyad relationships can be very intense emotionally but also unstable and short lived. Why is this so?

erin m – 2 couples – CC BY-NC 2.0.

A triad , or three-person group, involves relationships that are still fairly intense, but it is also more stable than a dyad. A major reason for this, said Simmel, is that if two people in a triad have a dispute, the third member can help them reach some compromise that will satisfy all the triad members. The downside of a triad is that two of its members may become very close and increasingly disregard the third member, reflecting the old saying that “three’s a crowd.” As one example, some overcrowded college dorms are forced to house students in triples, or three to a room. In such a situation, suppose that two of the roommates are night owls and like to stay up very late, while the third wants lights out by 11:00 p.m. If majority rules, as well it might, the third roommate will feel very dissatisfied and may decide to try to find other roommates.

As groups become larger, the intensity of their interaction and bonding decreases, but their stability increases. The major reason for this is the sheer number of relationships that can exist in a larger group. For example, in a dyad only one relationship exists, that between the two members of the dyad. In a triad (say composed of members A, B, and C), three relationships exist: A-B, A-C, and B-C. In a four-person group, the number of relationships rises to six: A-B, A-C, A-D, B-C, B-D, and C-D. In a five-person group, 10 relationships exist, and in a seven-person group, 21 exist (see Figure 6.2 “Number of Two-Person Relationships in Groups of Different Sizes” ). As the number of possible relationships rises, the amount of time a group member can spend with any other group member must decline, and with this decline comes less intense interaction and weaker emotional bonds. But as group size increases, the group also becomes more stable because it is large enough to survive any one member’s departure from the group. When you graduate from your college or university, any clubs, organizations, or sports teams to which you belong will continue despite your exit, no matter how important you were to the group, as the remaining members of the group and new recruits will carry on in your absence.

Figure 6.2 Number of Two-Person Relationships in Groups of Different Sizes

Group Leadership and Decision Making

Most groups have leaders. In the family, of course, the parents are the leaders, as much as their children sometimes might not like that. Even some close friendship groups have a leader or two who emerge over time. Virtually all secondary groups have leaders. These groups often have a charter, operations manual, or similar document that stipulates how leaders are appointed or elected and what their duties are.

Sociologists commonly distinguish two types of leaders, instrumental and expressive. An instrumental leader is a leader whose main focus is to achieve group goals and accomplish group tasks. Often instrumental leaders try to carry out their role even if they alienate other members of the group. The second type is the expressive leader , whose main focus is to maintain and improve the quality of relationships among group members and more generally to ensure group harmony. Some groups may have both types of leaders.

Related to the leader types is leadership style . Three such styles are commonly distinguished. The first, authoritarian leadership , involves a primary focus on achieving group goals and on rigorous compliance with group rules and penalties for noncompliance. Authoritarian leaders typically make decisions on their own and tell other group members what to do and how to do it. The second style, democratic leadership , involves extensive consultation with group members on decisions and less emphasis on rule compliance. Democratic leaders still make the final decision but do so only after carefully considering what other group members have said, and usually their decision will agree with the views of a majority of the members. The final style is laissez-faire leadership . Here the leader more or less sits back and lets the group function on its own and really exerts no leadership role.

When a decision must be reached, laissez-faire leadership is less effective than the other two in helping a group get things done. Whether authoritarian or democratic leadership is better for a group depends on the group’s priorities. If the group values task accomplishment more than anything else, including how well group members get along and how much they like their leader, then authoritarian leadership is preferable to democratic leadership, as it is better able to achieve group goals quickly and efficiently. But if group members place their highest priority on their satisfaction with decisions and decision making in the group, then they would want to have a lot of input in decisions. In this case, democratic leadership is preferable to authoritarian leadership.

Some small groups shun leadership and instead try to operate by consensus . In this model of decision making popularized by Quakers (T. S. Brown, 2009), no decision is made unless all group members agree with it. If even one member disagrees, the group keeps discussing the issue until it reaches a compromise that satisfies everyone. If the person disagreeing does not feel very strongly about the issue or does not wish to prolong the discussion, she or he may agree to “stand aside” and let the group make the decision despite the lack of total consensus. But if this person refuses to stand aside, no decision may be possible.

Some small groups operate by consensus instead of having a leader guiding or mandating their decision making. This model of decision making was popularized by the Society of Friends (Quakers).

John – All Are Welcome – CC BY 2.0.

A major advantage of the consensus style of decision making is psychic. Because everyone has a chance to voice an opinion about a potential decision, and no decisions are reached unless everyone agrees with them, group members will ordinarily feel good about the eventual decision and also about being in the group. The major disadvantage has to do with time and efficiency. When groups operate by consensus, their discussions may become long and tedious, as no voting is allowed and discussion must continue until everyone is satisfied with the outcome. This means the group may well be unable to make decisions quickly and efficiently.

One final issue is how gender influences leadership styles. Although the evidence indicates that women and men are equally capable of being good leaders, their leadership styles do tend to differ. Women are more likely to be democratic leaders, while men are more likely to be authoritarian leaders (Eagly & Carli, 2007). Because of this difference, women leaders sometimes have trouble securing respect from their subordinates and are criticized for being too soft. Yet if they respond with a more masculine, or authoritarian, style, they may be charged with acting too much like a man and be criticized in ways a man would not be.

Groups, Roles, and Conformity

We have seen in this and previous chapters that groups are essential for social life, in large part because they play an important part in the socialization process and provide emotional and other support for their members. As sociologists have emphasized since the origins of the discipline during the 19th century, the influence of groups on individuals is essential for social stability. This influence operates through many mechanisms, including the roles that group members are expected to play. Secondary groups such as business organizations are also fundamental to complex industrial societies such as our own.

Social stability results because groups induce their members to conform to the norms, values, and attitudes of the groups themselves and of the larger society to which they belong. As the chapter-opening news story about teenage vandalism reminds us, however, conformity to the group, or peer pressure, has a downside if it means that people might adopt group norms, attitudes, or values that are bad for some reason to hold and may even result in harm to others. Conformity is thus a double-edged sword. Unfortunately, bad conformity happens all too often, as several social-psychological experiments, to which we now turn, remind us.

Solomon Asch and Perceptions of Line Lengths

Several decades ago Solomon Asch (1958) conducted one of the first of these experiments. Consider the pair of cards in Figure 6.3 “Examples of Cards Used in Asch’s Experiment” . One of the lines (A, B, or C) on the right card is identical in length to the single line in the left card. Which is it? If your vision is up to par, you undoubtedly answered Line B. Asch showed several students pairs of cards similar to the pair in Figure 6.3 “Examples of Cards Used in Asch’s Experiment” to confirm that it was very clear which of the three lines was the same length as the single line.

Figure 6.3 Examples of Cards Used in Asch’s Experiment

Next, he had students meet in groups of at least six members and told them he was testing their visual ability. One by one he asked each member of the group to identify which of the three lines was the same length as the single line. One by one each student gave a wrong answer. Finally, the last student had to answer, and about one-third of the time the final student in each group also gave the wrong answer that everyone else was giving.

Unknown to these final students, all the other students were confederates or accomplices, to use some experimental jargon, as Asch had told them to give a wrong answer on purpose. The final student in each group was thus a naive subject, and Asch’s purpose was to see how often the naive subjects in all the groups would give the wrong answer that everyone else was giving, even though it was very clear it was a wrong answer.

After each group ended its deliberations, Asch asked the naive subjects who gave the wrong answers why they did so. Some replied that they knew the answer was wrong but they did not want to look different from the other people in the group, even though they were strangers before the experiment began. But other naive subjects said they had begun to doubt their own visual perception : they decided that if everyone else was giving a different answer, then somehow they were seeing the cards incorrectly.

Asch’s experiment indicated that groups induce conformity for at least two reasons. First, members feel pressured to conform so as not to alienate other members. Second, members may decide their own perceptions or views are wrong because they see other group members perceiving things differently and begin to doubt their own perceptive abilities. For either or both reasons, then, groups can, for better or worse, affect our judgments and our actions.

Stanley Milgram and Electric Shock

Although the type of influence Asch’s experiment involved was benign, other experiments indicate that individuals can conform in a very harmful way. One such very famous experiment was conducted by Yale University psychologist Stanley Milgram (1974), who designed it to address an important question that arose after World War II and the revelation of the murders of millions of people during the Nazi Holocaust. This question was, “How was the Holocaust possible?” Many people blamed the authoritarian nature of German culture and the so-called authoritarian personality that it inspired among German residents, who, it was thought, would be quite ready to obey rules and demands from authority figures.

Milgram wanted to see whether Germans would indeed be more likely than Americans to obey unjust authority. He devised a series of experiments and found that his American subjects were quite likely to give potentially lethal electric shocks to other people. During the experiment, a subject, or “teacher,” would come into a laboratory and be told by a man wearing a white lab coat to sit down at a table housing a machine that sent electric shocks to a “learner.” Depending on the type of experiment, this was either a person whom the teacher never saw and heard only over a loudspeaker, a person sitting in an adjoining room whom the teacher could see through a window and hear over the loudspeaker, or a person sitting right next to the teacher.

The teacher was then told to read the learner a list of word pairs, such as mother-father, cat-dog, and sun-moon. At the end of the list, the teacher was then asked to read the first word of the first word pair—for example, “mother” in our list—and to read several possible matches. If the learner got the right answer (“father”), the teacher would move on to the next word pair, but if the learner gave the wrong answer, the teacher was to administer an electric shock to the learner. The initial shock was 15 volts (V), and each time a wrong answer was given, the shock would be increased, finally going up to 450 V, which was marked on the machine as “Danger: Severe Shock.” The learners often gave wrong answers and would cry out in pain as the voltage increased. In the 200-V range, they would scream, and in the 400-V range, they would say nothing at all. As far as the teachers knew, the learners had lapsed into unconsciousness from the electric shocks and even died. In reality, the learners were not actually being shocked. Instead, the voice and screams heard through the loudspeaker were from a tape recorder, and the learners that some teachers saw were only pretending to be in agony.

Before his study began, Milgram consulted several psychologists, who assured him that no sane person would be willing to administer lethal shock in his experiments. He thus was shocked (pun intended) to find that more than half the teachers went all the way to 450 V in the experiments, where they could only hear the learner over a loudspeaker and not see him. Even in the experiments where the learner was sitting next to the teacher, some teachers still went to 450 V by forcing a hand of the screaming, resisting, but tied-down learner onto a metal plate that completed the electric circuit.

Milgram concluded that people are quite willing, however reluctantly, to obey authority even if it means inflicting great harm on others. If that could happen in his artificial experiment situation, he thought, then perhaps the Holocaust was not so incomprehensible after all, and it would be too simplistic to blame the Holocaust just on the authoritarianism of German culture. Instead, perhaps its roots lay in the very conformity to roles and group norms that makes society possible in the first place. The same processes that make society possible may also make tragedies like the Holocaust possible.

The Third Wave

In 1969, concern about the Holocaust prompted Ron Jones, a high school teacher from Palo Alto, California, to conduct a real-life experiment that reinforced Milgram’s findings by creating a Nazi-like environment in the school in just a few short days (Jones, 1979). He began by telling his sophomore history class about the importance of discipline and self-control. He had his students sit at attention and repeatedly stand up and sit down in quiet unison and saw their pride as they accomplished this task efficiently. All of a sudden everyone in the class seemed to be paying rapt attention to what was going on.

The next day, Jones began his class by talking about the importance of community and of being a member of a team or a cause. He had his class say over and over, “Strength through discipline, strength through community.” Then he showed them a new class salute, made by bringing the right hand near the right shoulder in a curled position. He called it the Third Wave salute, because a hand in this position resembled a wave about to topple over. Jones then told the students they had to salute each other outside the classroom, which they did so during the next few days. As word of what was happening in Jones’s class spread, students from other classes asked if they could come into his classroom.

On the third day of the experiment, Jones gave membership cards to every student in his class, which had now gained several new members. He told them they had to turn in the name of any student who was disobeying the class’s rules. He then talked to them about the importance of action and hard work, both of which enhanced discipline and community. Jones told his students to recruit new members and to prevent any student who was not a Third Wave member from entering the classroom. During the rest of the day, students came to him with reports of other students not saluting the right way or of some students criticizing the experiment. Meanwhile, more than 200 students had joined the Third Wave.

On the fourth day of the experiment, more than 80 students squeezed into Jones’s classroom. Jones informed them that the Third Wave was in fact a new political movement in the United States that would bring discipline, order, and pride to the country and that his students were among the first in the movement. The next day, Jones said, the Third Wave’s national leader, whose identity was still not public, would be announcing a grand plan for action on national television at noon.

At noon the next day, more than 200 students crowded into the school auditorium to see the television speech. When Jones gave them the Third Wave salute, they saluted back. They chanted, “Strength through discipline, strength through community,” over and over, and then sat in silent anticipation as Jones turned on a large television in front of the auditorium. The television remained blank. Suddenly Jones turned on a movie projector and showed scenes from a Nazi rally and the Nazi death camps. As the crowd in the auditorium reacted with shocked silence, the teacher told them there was no Third Wave movement and that almost overnight they had developed a Nazi-like society by allowing their regard for discipline, community, and action to warp their better judgment. Many students in the auditorium sobbed as they heard his words.

The Third Wave experiment was designed to help high school students in Palo Alto, California, understand how the Nazi Holocaust (represented by this photo of the Auschwitz concentration camp) could have happened. The experiment illustrated that normal group processes that make social life possible can also lead people to conform to objectionable standards.

George Olcott – Auschwitz Fence – CC BY-NC 2.0.

The Third Wave experiment once again indicates that the normal group processes that make social life possible also can lead people to conform to standards—in this case fascism—that most of us would reject. It also helps us understand further how the Holocaust could have happened. As Jones (1979, pp. 509–10) told his students in the auditorium, “You thought that you were the elect. That you were better than those outside this room. You bargained your freedom for the comfort of discipline and superiority. You chose to accept the group’s will and the big lie over your own conviction.…Yes, we would all have made good Germans.”

Zimbardo’s Prison Experiment

In 1971, Stanford University psychologist Philip Zimbardo (1972) conducted an experiment to see what accounts for the extreme behaviors often seen in prisons: does this behavior stem from abnormal personalities of guards and prisoners or, instead, from the social structure of prisons, including the roles their members are expected to play? His experiment remains a compelling illustration of how roles and group processes can prompt extreme behavior.

Zimbardo advertised for male students to take part in a prison experiment and screened them out for histories of mental illness, violent behavior, and drug use. He then assigned them randomly to be either guards or prisoners in the experiment to ensure that any behavioral differences later seen between the two groups would have to stem from their different roles and not from any preexisting personality differences had they been allowed to volunteer.

The guards were told that they needed to keep order. They carried no weapons but did dress in khaki uniforms and wore reflector sunglasses to make eye contact impossible. On the first day of the experiment, the guards had the prisoners, who wore gowns and stocking caps to remove their individuality, stand in front of their cells (converted laboratory rooms) for the traditional prison “count.” They made the prisoners stand for hours on end and verbally abused those who complained. A day later the prisoners refused to come out for the count, prompting the guards to respond by forcibly removing them from their cells and sometimes spraying them with an ice-cold fire extinguisher to expedite the process. Some prisoners were put into solitary confinement. The guards also intensified their verbal abuse of the prisoners.

By the third day of the experiment, the prisoners had become very passive. The guards, several of whom indicated before the experiment that they would have trouble taking their role seriously, now were quite serious. They continued their verbal abuse of the prisoners and became quite hostile if their orders were not followed exactly. What had begun as somewhat of a lark for both guards and prisoners had now become, as far as they were concerned, a real prison.

Shortly thereafter, first one prisoner and then a few more came down with symptoms of a nervous breakdown. Zimbardo and his assistants could not believe this was possible, as they had planned for the experiment to last for two weeks, but they allowed the prisoners to quit the experiment. When the first one was being “released,” the guards had the prisoners chant over and over that this prisoner was a bad prisoner and that they would be punished for his weakness. When this prisoner heard the chants, he refused to leave the area because he felt so humiliated. The researchers had to remind him that this was only an experiment and that he was not a real prisoner. Zimbardo had to shut down the experiment after only six days.

Zimbardo (1972) later observed that if psychologists had viewed the behaviors just described in a real prison, they would likely have attributed them to preexisting personality problems in both guards and prisoners. As already noted, however, his random assignment procedure invalidated this possibility. Zimbardo thus concluded that the guards’ and prisoners’ behavioral problems must have stemmed from the social structure of the prison experience and the roles each group was expected to play. Zimbardo (2008) later wrote that these same processes help us understand “how good people turn evil,” to cite the subtitle of his book, and thus help explain the torture and abuse committed by American forces at the Abu Ghraib prison in Iraq after the United States invaded and occupied that country in 2003. Once again we see how two of the building blocks of social life—groups and roles—contain within them the seeds of regrettable behavior and attitudes.

Groupthink may prompt people to conform with the judgments or behavior of a group because they do not want to appear different. Because of pressures to reach a quick verdict, jurors may go along with the majority opinion even if they believe otherwise. Have you ever been in a situation where groupthink occurred?

Brian DeWitt – Wolf Law Courtroom – CC BY-NC-ND 2.0.

As these examples suggest, sometimes people go along with the desires and views of a group against their better judgments, either because they do not want to appear different or because they have come to believe that the group’s course of action may be the best one after all. Psychologist Irving Janis (1972) called this process groupthink and noted it has often affected national and foreign policy decisions in the United States and elsewhere. Group members often quickly agree on some course of action without thinking completely of alternatives. A well-known example here was the decision by President John F. Kennedy and his advisers in 1961 to aid the invasion of the Bay of Pigs in Cuba by Cuban exiles who hoped to overthrow the government of Fidel Castro. Although several advisers thought the plan ill advised, they kept quiet, and the invasion was an embarrassing failure (Hart, Stern, & Sundelius, 1997).

Groupthink is also seen in jury decision making. Because of the pressures to reach a verdict quickly, some jurors may go along with a verdict even if they believe otherwise. In juries and other small groups, groupthink is less likely to occur if at least one person expresses a dissenting view. Once that happens, other dissenters feel more comfortable voicing their own objections (Gastil, 2009).

Key Takeaways

• Leadership in groups and organizations involves instrumental and expressive leaders and several styles of leadership.
• Several social-psychological experiments illustrate how groups can influence the attitudes, behavior, and perceptions of their members. The Milgram and Zimbardo experiments showed that group processes can produce injurious behavior.

For Your Review

• Think of any two groups to which you now belong or to which you previously belonged. Now think of the leader(s) of each group. Were these leaders more instrumental or more expressive? Provide evidence to support your answer.
• Have you ever been in a group where you or another member was pressured to behave in a way that you considered improper? Explain what finally happened.

Asch, S. E. (1958). Effects of group pressure upon the modification and distortion of judgments. In E. E. Maccoby, T. M. Newcomb, & E. L. Hartley (Eds.), Readings in social psychology . New York, NY: Holt, Rinehart and Winston.

Brown, T. S. (2009). When friends attend to business . Philadelphia, PA: Philadelphia Yearly Meeting. Retrieved from http://www.pym.org/pm/comments.php?id=1121_0_178_0_C .

Eagly, A. H., & Carli, L. L. (2007). Through the labyrinth: The truth about how women become leaders . Boston, MA: Harvard Business School Press.

Gastil, J. (2009). The group in society . Thousand Oaks, CA: Sage.

Hart, P. T., Stern E. K., & Sundelius B., (Eds.). (1997). Beyond groupthink: Political group dynamics and foreign policy-making . Ann Arbor, MI: University of Michigan Press.

Janis, I. L. (1972). Victims of groupthink . Boston, MA: Houghton Mifflin.

Jones, R. (1979). The third wave: A classroom experiment in fascism. In J. J. Bonsignore, E. Karsh, P. d’Errico, R. M. Pipkin, S. Arons, & J. Rifkin (Eds.), Before the law: An introduction to the legal process (pp. 503–511). Dallas, TX: Houghton Mifflin.

Milgram, S. (1974). Obedience to authority . New York, NY: Harper and Row.

Zimbardo, P. G. (2008). The Lucifer effect: Understanding how good people turn evil . New York, NY: Random House Trade Paperbacks.

Zimbardo, P. G. (1972). Pathology of imprisonment. Society, 9 , 4–8.

Sociology Copyright © 2016 by University of Minnesota is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Creation, evolution, and dissolution of social groups

James flamino.

1 Network Science and Technology Center, Rensselaer Polytechnic Institute, Troy, NY 12180 USA

2 Department of Physics, Applied Physics, and Astronomy, Rensselaer Polytechnic Institute, Troy, NY 12180 USA

Boleslaw K. Szymanski

3 Department of Computer Science, Rensselaer Polytechnic Institute, Troy, NY 12180 USA

4 Społeczna Akademia Nauk, Łódź, Poland

Ashwin Bahulkar

5 U.S. Army Research Laboratory, Adelphi, MD 20783 USA

Omar Lizardo

6 Department of Sociology, University of California, Los Angeles, CA 90095 USA

Associated Data

Understanding why people join, stay, or leave social groups is a central question in the social sciences, including computational social systems, while modeling these processes is a challenge in complex networks. Yet, the current empirical studies rarely focus on group dynamics for lack of data relating opinions to group membership. In the NetSense data, we find hundreds of face-to-face groups whose members make thousands of changes of memberships and opinions. We also observe two trends: opinion homogeneity grows over time, and individuals holding unpopular opinions frequently change groups. These observations and data provide us with the basis on which we model the underlying dynamics of human behavior. We formally define the utility that members gain from ingroup interactions as a function of the levels of homophily of opinions of group members with opinions of a given individual in this group. We demonstrate that so-defined utility applied to our empirical data increases after each observed change. We then introduce an analytical model and show that it accurately recreates the trends observed in the NetSense data.

Introduction

Most social interactions among humans occur in the context of a set of relatively small face-to-face groups, and not in isolated dyads 1 – 6 . In addition, these groups are not static. Instead, their membership changes over time with individuals constantly deciding whether to stay in their current groups and perhaps adjust some opinions, or leave to join other groups 7 . To date, however, the dynamics of human group formation, evolution, and dissolution in real social groups remain poorly understood 8 , 9 . Among humans, groups represent a level of association mediating between the individual and whole societies, producing a variety of personal benefits, including companionship and support 1 , while also allowing people to come together to pursue instrumental goals or work together toward common purposes 10 . Despite the introduction of telecommunication as an entirely different medium of interaction between individuals, the importance of offline groups as the primary nexus of social life has prevailed. Indeed, new technologically mediated interactions such as texting, online chatting, and social media complement rather than replace face-to-face interaction in small groups, and a great majority of online ties ultimately relies on (or emerges from) offline face-to-face interactions 11 . Extended to social connections within face-to-face groups, computational and empirical literature have found that these groups are continually evolving rather than being static 5 , 8 , 9 , 12 , 13 . Such evolution has been aided by the tendency of the contemporary societies to relax social and institutional restrictions on group joining and leaving behavior compared to the past or to the societies that are more traditional 10 , 14 .

The previous theoretical works identified homophilic interactions within groups and communities as the primary component of the membership benefits 4 , 7 . Recent papers 15 – 17 separately consider a role of homophily in popularity, ranking of minorities, and structural change in social networks. Here, we formalize a notion of utility of group membership as a function of compatibility of opinions between a member and all others in its group. The utility enables us to integrate in a single model the three aforementioned phenomena and to account for the two dynamic patterns observed in empirical data. We find that changes of opinions or group memberships that increase members’ utility recreate the group dynamic patterns observed in empirical data and the theoretically postulated role of homophily in these dynamics. This agreement validates our utility maximization hypothesis. Subsequently, we introduce a predictive model based on utility maximization. This model performs well on forecasting opinions and group membership dynamics within the data, which further validates our approach.

In this paper we assume a broad definition of “groups” since within the social sciences, there are a variety of ways of defining groups 1 , 3 , 18 . Some definitions emphasize enduring affective ties among members, while others point to a unique sociometric signature (e.g., fully connected cliques). However, all approaches point to three features that seem to be characteristic of all human groups 18 . First, groups are located in specific settings allowing for face-to-face interaction within relatively small spatial distances (dormitories, homes, workplaces), happening at specific times (mornings, evenings) 3 , 7 . Second, groups are relatively small 1 . After a human gathering reaches a limit size, it loses its capacity to function as a coordinated group, and becomes a “crowd” or a “mass” instead 10 . Finally, groups can overlap. This means that persons are not constrained to belong to only one group. Instead, groups share members, and people need to make decisions (given a limited budget) as to how much time and energy they will dedicate to each group.

These three features, namely, specific location in time and space, relatively small size, and possibility of overlap, inform our approach to group detection and to analysis of group composition dynamics presented here. We are agnostic as to whether “strong” ties exist among group members as we define them; instead, we allow for people to be either weakly or strongly attached to the groups to which they belong as that becomes an endogenous factor driving the dynamics of change in group membership we observe. By definition, since, as in previous work 8 , 19 – 21 , we use data sensitive to spatial co-location to define groups, every group we observe is a sociometric clique 3 .

In this paper, we use the NetSense study that follows 196 randomly selected students from the incoming freshman class of fall 2011 at the University of Notre Dame, until the spring semester of 2013. Reference 22 discusses the study’s design and its approval by the university’s Institutional Review Board for studies involving human subjects. The collected dataset includes study participants’ answers to survey questions about their stances on a variety of topics and the Bluetooth proximity records collected continuously by the student’s cell phones. Every phone of a NetSense study member makes a record of every Bluetooth interaction between a pair of participants when this interaction meets the threshold of physical proximity. To extract stable groups formed in each semester from this proximity data, we use the hierarchical clustering method that we proposed in 23 as discussed in the first subsection of “ Methods ” section.

Similar data collection strategies and the use of Bluetooth proximity for finding real-world group gatherings were used and validated by others in 8 , 24 – 28 . Table ​ Table1 1 lists the details about the groups identified from the Bluetooth data. Our approach discovers 436 groups, with their periods of existence ranging from one to four semesters, and identifies 149,771 meetings. Since the average fraction of group meetings attended by a member ranges from 0.89 to 0.91, members stay with their groups for 90% of the 14 week semester. Accordingly, we assume that changes happen at the three boundaries between the semesters. By design, this is where we analyze any changes in the data. Having so reconstructed groups, we are able to detect changes in these groups and their group members between semesters (see “ Methods ”). We use these changes as a ground truth for our analytical model, which we design to predict group membership dynamics and changes in individual opinions.

Properties of groups.

General statistics on the groups detected through hierarchical clustering for each of the available semesters in NetSense.

To study the dynamics of student opinions, specifically, we use demographic surveys that the NetSense study participants completed at the beginning of each semester. These surveys contain 38 questions pertaining to family background, activities on campus, hobbies, and stances on various social and political issues, favorite types of music. The possible answers to these questions also came in a variety of forms, ranging from “yes” and “no” modalities, to closed lists, to open-ended text boxes. In our study, we include the five survey questions that were found to be the most predictive on the formation of new social relationships 29 . These questions are related to general political orientation, as well as stances on the legality of abortion, marijuana usage, gay marriage rights, and alcoholic drinking habits. For political stances, the possible answers rely on a seven-point scale, with one being extremely liberal, four being neutral, and seven being extremely conservative. The other questions use a similar point scale, ranging from strong support to strong opposition to the question, with a neutral stance in the middle. Drinking habits answers describe a range of drinking frequencies, ranging from no drinking to very frequent drinking. For all questions (including drinking habits), we cluster the range of responses into three modalities; joining extreme and moderate stances into two opposing stances while the third stance represents neutral answers. To track a change in opinion across a semester for our ground truth, we simply compare the stances of a student before and after the boundary.

In contrast to other studies that rely on routinely and massively collected Call Record Data, our study relies also on Bluetooth and survey data. This approach allows us to track actual face-to-face groups, rather than hypothetical or ersatz groups purely based on remote communications. While work based on remote telecommunications may have the advantage of scale 30 , it has the disadvantage of not being able to account for face-to-face group dynamics, which is the core scientific question we are after. In this respect, the sample size of human subjects in our study is consistent with that used in the very few other studies that have been able to track on-the-ground human groups over time.

For instance, 21 tracked 100 participants in an executive MBA program at an elite business school using infrared wearable badges on a single occasion, while 31 also tracked 100 subjects (using a similar bluetooth technology approach as NetSense) for 9 months. In the same manner, 20 tracked 24 graduate student participants for a whole year, also using wearable badges. A study that tracked 200 students for a whole year using motion sensors comes close to the sample size in NetSense 19 . All this previous work identifying groups using wearable technology or bluetooth proximity detection is able to identify face-to-face groups using non-obtrusive methods. However, the data used in this previous work lacks measures of people’s opinions, attitudes, and beliefs, and thus is unable to link the observed group dynamics with the opinion distribution across people and groups and related dynamics of opinion change and distribution, as we do in this paper.

The most ambitious effort to study face-to-face groups using computational social science techniques we know of is that of 8 which, in similar design as NetSense, tracked about 1000 freshmen students from an undisclosed European University for several months determining the existence of groups via bluetooth. However, like the other studies mentioned earlier, 8 lacks information on individual opinions and beliefs (and thus cannot link group evolution dynamics to opinion change). While smaller than 8 in terms of sample size, the NetSense data used here has the advantage of tracking participants for almost 2 years, allowing for richer temporal dynamics to be detected. In addition, while 8 discovers a number of descriptive sociometric dynamics of groups similar to the ones we observe, the authors do not develop an underlying formal model capable of predicting the observed patterns, and thus cannot shed light on the individual micro-mechanisms underlying the observed patterns 32 .

The initial number of participants in the NetSense study is 196. However, its retention rate is 71 % over all four semesters. This is comparable to published studies with similar sample sizes 33 and avoids the pitfalls of low retention rates which have been shown to seriously affect results 34 . Like previous work, which has studied group dynamics using data collected from “captive” populations (e.g., MBA executives, graduate and undergraduate students, workers at scientific labs and so forth 8 , 19 , 20 , 25 ), our study also uses a student population, namely, a sample of Freshmen who began their studies at the University of Notre Dame in 2011. In studying face-to-face groups “in the wild” 20 , it is important to note that the main scientific issue is not necessarily scale or “representativeness” in the probability sample sense. Yet, the University of Notre Dame has a strongly diverse undergraduate student body, both socio-demographically and geographically 22 with 89 % of the students from out-of-state 35 . Thus, we consider these students a representative sample of the life experience for half of the U.S. population born around the year 1993. In that respect, our study is also similar to many other published studies focusing on social network dynamics among individuals undergoing other significant transitions, like going into retirement or having a child 33 , 36 , 37 .

Beyond this, studies like ours need to worry about when studying group dynamics in real-life settings is whether the human gatherings detected are in fact face-to-face groups in the sociological sense 1 , 3 , as the scientific objective is to determine the “life-cycle” of actual groups on the ground, not ersatz groups. Therefore, we need data with high ecological validity of the underlying data, study location, and participant population 38 and the NetSense data provides that feature. Absent high ecological validity, even the largest sample of participants will yield the wrong (or misleading) answers.

University of Notre Dame has a nearly entirely residential campus located on north outskirts of South Bend city, Indiana, in the Midwestern United States. Hence, student life there, especially during the first 2 years of study, revolves mostly around activities (studies, recreation, social and religious life) located exclusively on campus. In that sense, University of Notre Dame is a nearly perfect closed-system, a “social laboratory”, where generic behavioral processes applicable to all human groups can be observed with little exogenous disturbance. For the time of observation, almost all the groups to which individuals belong are located on campus. Additionally, for most of these students, this is their first long-time away from home and family, creating a transformative and shared experience in their social lives 39 , 40 . The social ties students form at this stage are both personally and sociologically significant as are the groups that they join 1 . Furthermore, at Notre Dame, freshmen students are required to take a specific set of foundational courses heavily oriented toward philosophy, theology, and humanities to delay disciplinary specialization until the second academic year when students can declare majors. This allows for friendships and groups to form invariant of major, as well as keep specialized courses from initially forcing connections between particular students. As such, in this case, given the scientific objective to study general behavioral processes of face-to-face group formation and evolution at fine-grained time-scales, restriction to a student population should pose no barrier to proper inference.

Sociological processes for determining social group dynamics

Although there is little empirical research on face-to-face group membership dynamics 20 , theoretical work relying on agent-based modeling and simulation in computational social sciences points to three basic processes determining social group dynamics: selective interaction, network-based recruitment, and value homophily. These intuitions can be used as the building blocks to construct a formal model that can shed light on the micro-mechanisms that account for patterns of group evolution observed in the data. First, people selectively concentrate their interactions on members of the groups to which they belong and not on outgroup members 4 , 41 . The reason is a limited budget of time and energy a person can devote to interactions with others. The more people interact in face-to-face settings, the more likely they are to form strong attachments with one another 1 , 42 and to stay with a group 4 . Still, some dyadic ties link members to individuals outside of their current groups 2 . These ties enable individuals to join new groups via the process of network-based recruitment 5 , 7 . The more time and energy people spend interacting with others outside the group, the more likely it is that they will leave their current groups and join new groups 12 . Hence, the extent to which groups attract an individual’s time and energy is an important determinant of whether individuals will stay with the group 5 , 7 .

The benefit that people gain from their current group memberships depends on the extent to which they share important attributes with others, referred to as homophily . It may include shared socio-demographic characteristics 41 , 42 , and shared beliefs and opinions, referred to as value homophily 41 . People are also likely to leave a group when others disagree with them. Because people share values, beliefs, and opinions via social interaction, selective interaction and homophily can be mutually reinforcing 4 .

Utility of group member interactions

To account for the aforementioned sociological processes that drive social group dynamics, we develop a formal model based on subjective utility, a common modeling approach in behavioral science 43 . In the model, member v gains utility from interactions with member m within group g . This utility is a function of the interacting members’ fractions of group meeting attendance and their stances on an attribute a . As reported in 44 , ruling parties tend to have an equally negative attitude toward members of opposition parties. Thus, we treat holders of neutral and extremist views equivalent in terms of the utility of their interactions with other members of a group. We also assume that the utility gain from interactions with members sharing the same stance is twice as large as the utility loss from interactions with members holding different stances. This assumption yields a simple yet effective model.

In the SM, we show results under an alternative assumption that extremists dislike neutrals less than the opposing extremists while neutrals equally less like interactions with other neutrals. Both models yield similar results. The essential property of both models is that individuals optimizing utility drive up polarization of groups. In the SM, we show that replacing the coefficient 2 in Eq. ( 1 ) with a parameter α ≥ 1 and the coefficient 1/2 in Eq. ( S2 ) with a parameter β ≤ 1 preserves this property.

We denote the liberal stance by - 1 , neutral by 0 and conservative by 1. To express relations of stances to each other, we can place them as vertices of the equilateral triangle, with the neutral stance at the top, the liberal stance at left bottom and the conservative stance at right bottom. Then, for each stance s , stance s + denotes its clockwise neighbor, while s - its counterclockwise neighbor. With this notation, 0 + = 1 and 0 - = - 1 , etc. Let for a node m , s m , a denotes its stance for attribute a while for a member v of group g , let w v , g denote a fraction of meetings of g that v attended. Then, utility of v from interactions within g is

where W g , a s = ∑ m ∈ g ∩ s m , a = s w m , g . Thus, the utility of interactions of node v with each member of the group g , including v , is represented by the product of fractions of the meetings in which each of these two members participated. For each pair of members of the same stance the doubled result is added to the utility, while for each pair with different stances, the result is subtracted from the utility. We include v in the list of members of the same stance so it represents the expected vote for this stance. The total utility of node v from interactions with members of g is just the sum of utility over all attributes. The utility of an entire group is the sum of the total utility of all members of this group.

We hypothesize that the members attempting to maximize their utilities drive group evolution dynamics. Indeed, it is natural to expect that people will seek their own benefit, and that they will make changes in their group membership to seek an improvement to their utility. If this is the only acting criterion when an individual makes a change, this change is egocentric . However, participants might also consider the feelings of others when changing group membership or opinions. To account for this, we introduce another criterion based on the average benefits of all group members. This criterion is called strongly altruistic , since it is close to the traditional definition of altruism in the social sciences 45 . However, research in economic games also shows that people are more cooperative when they interact with others whom they believe share a group identity 46 or who share opinions with members of the group 47 . In the dictator game, players in the dictator role transfer more money when they believe the recipient shares their group identity 48 . Hence, it is reasonable to suppose that people making a decision about a change, will take into account how this change will affect members with whom they share a stance on the involved attribute. Accordingly, we refer to a change as weakly altruistic if the total utility change for all members who share the most of their stances, and thus, value homophily, with the person making the change, is positive.

Formally, all three types of changes use the same function computing a difference in utility for a certain subset of members of a group g on which node v , making a change, focuses on. This subset contains just v for egocentric change, the entire group for strongly altruistic change, and all nodes in g with the same stances as v for weakly altruistic change. Therefore for node v in a group g , we compute the utility change by subtracting v ’s utility before the change from this utility after the change. A change is accepted if, across all attributes, the sum of utility changes for all member in the focused subset is positive.

We computed the frequency of the three types of criteria in the empirical data. The left side of Table  2 shows the results. On average, 93 % of changes made by members are egocentric, 85 % of them are weakly altruistic, and 81 % are strongly altruistic. These results show that large majority of individuals make group changes benefiting not only themselves, but also others. It seems counter-intuitive that an average of 7 % of changes result in no utility benefits for the change-making individual in any way. Yet, these types of changes are likely made due to sudden and unexpected circumstances that cannot be traced by our indicators of value homophily. E.g., leaving a group due to the discovery of irrevocable differences of personalities with some members). Interestingly, 81 % of changes also benefited those who did not even align their stances with the node making a change. This might well be an unintentional consequence of egocentric changes. For example, when a holder of minority stances in a group leaves motivated by the low utility this holder is gaining, all holders of majority stances gain utility, and their total gain may be higher than the utility lost by the peers of the leaving member.

Dynamics of altruism and group polarization.

Analysis of the total number of altruistic changes and the growth of group polarization across the three semester boundaries.

Additionally, we found that the differences in utility resulting from the changes made versus not made in the data are always positive. Yet, their magnitude depends on the type of changes compared (see “ Methods ” for details). The biggest differences arise for egocentric changes. Yet, they vary significantly from 27 to 45 % . The smallest are seen for the strongly altruistic changes, for which the differences vary the most from 2 to 32 % .

In addition, the utility gains observed in each semester increase group value homophily. This increases stance polarization across groups. To quantify this change, we define a measure of polarization as a function of stance alignment. To measure polarization across a group, we sum the squares of differences between globally expected fractions of stances of all attributes and the actual fraction of these stances in each group. More formally, let W g , a s denote the sum of fractions of attendances for all group members in g with stance s , while W g , a = ∑ s = - 1 1 W g , a s denote the sum of such fractions for all group members. Then, G g , a s = w g , a ∑ g W g , a s ∑ g W g , a denotes the expected attendance of stance s for attribute a in group g . With this notation, the polarization of group g on attribute a can be expressed as

The total group polarization is just the sum of the values of this function for each attribute. Polarization is 0 when there is a full agreement between the global and local frequencies of stances among members. It grows when members increasingly align their stances with each other. This can be accomplished within each group by members either changing their stances or leaving groups in which their stance is the minority, and joining those where their stance is the majority. Figure  1 illustrates a group evolution in which in each step both utility and polarization increase for groups involved in a change.

Example of group dynamics with polarization and utility growth over time. ( a ) Evolution of group memberships, with three potential groups and nine different participants. These individuals hold three different stances (each marked by its own color, either blue, orange, or grey) for some arbitrary attribute. ( b ) The increase of group utility and group polarization in discrete steps as the members of the example groups change membership to maximize their utility. All steps made are egocentric and altruistic.

For each semester boundary, we use our polarization measure to compute the actual trend of value homophily within the NetSense data by subtracting polarization for each group before a change from this polarization after the change in membership. The right side of Table  2 shows the results that show polarization monotonically increasing over time.

Now, by examining Eq. ( 1 ) for utility and Eq. ( 2 ) for polarization, it is clear that increasing group homophily imposes stronger polarization and a sequence of maximal (i.e., increasing utility the most) strongly altruistic changes irreversibly push groups toward the full stance polarization 49 . Given this, we can conclude that for any state in a group not fully homogeneous, there is a sequence of strongly altruistic changes leading toward complete polarization. These steps increase consensus on stances among group members, showing that value homophily has strong effects on the society-wide distribution of stances. Since 81 % of all changes in the NetSense data are actually strongly altruistic, the above trends are strong in this empirical dataset.

Analyzing the NetSense data even further, we also discover that students holding the campus majority stances change their group membership less frequently than do those holding less popular stances. The data shows that quantitatively, the majority stance holders moving to the next semester retain membership in about 85 % of the groups while such fraction for students with the minority stance is 75 % . Additionally, we find that the majority stance holders not only retain membership of groups for a greater amount of time, but they also enjoy a majority control of membership in a greater number of groups. We find that on average, the number of groups in which the majority of members hold the minority opinion is just 53%, barely over half of the number of groups with a majority of members holding the majority opinion. We also measure the dependence of utility gained on the popularity of stances of a group member on four attributes (we excluded the drinking habits attribute since its values represent ranges of frequency, not stances). The ratio of the average utility gained by members with the minority stance to such utility earned by members with the majority stance is the highest for the legalization of gay marriage (0.55), followed by political leaning (0.29), legalization of marijuana (0.25), and abortion (0.11). We also present the fractions of people with majority and minority stances for the entire population and for the top 10 % of members, ordered by the utility they received. Table ​ Table3 3 presents these results.

Majority and minority stance analysis.

A comparison between stance popularity for fractions of nodes with majority and minority stances among all members and among the top 10 % of members with the highest utility.

We find that the nodes gaining high utility are more likely to hold majority stances than expected by chance based on their fraction in the entire population. Likewise, the high utility nodes are statistically less likely to hold a minority stance, as indicated by the fraction of minority stance holders in the entire population.

To summarize, this section demonstrates that our utility increases for 93 % of the changes made by members in the NetSense dataset and 81 % of all changes also increase utility across the entire group. Moreover, nodes observed to make frequent group membership changes have lower utility than other nodes.

This validates our hypothesis that members attempting to increase their utilities are driving social group dynamics.

Predicting group membership and opinion changes

To test this hypothesis further, we implement an analytical model to forecast future group affiliation and stance changes based on this hypothesis. We hypothesize that maximizing a person’s total utility from group memberships also requires accounting for the time and effort of attending group meetings. Therefore, the total utility for predictions is then the sum of two terms: the ingroup interaction utility and the utility of membership in the given number of groups. Our model analytically computes the ratio of these two terms that maximizes the prediction performance on training subset of our data (the first two semester boundaries), and then predicts membership and opinion changes that specifically improve utility on the remainder (the last semester boundary). To give some reference of the quality of performance of our model, we use a random baseline that randomly predicts changes across our data (see “ Methods ” for details on the analytical model, and SM for the baseline implementation).

Table ​ Table4 4 shows the results for the test data predictions of both the analytical model and the random baseline compared to our ground truth. The results indicate that group joining behavior is more predictable than both group leaving and stance change behavior. This is important, as the random model shows that, as a baseline, this task is very difficult to perform. This is because for group leaving and opinion changes, a person considering a change belongs only to a few groups to leave and holds a few opinions to change. On the other hand, there many groups that are open and available to join for such a person. Still, even for group leaving and opinion changing, prediction quality of the analytical model is substantial when compared to the baseline. Overall, the results demonstrate that the analytical model is very effective at predicting changes. These results also validate maximizing the group membership utility as a process driving group dynamics.

Performance of the models.

A comparison of the Precision, Recall, and F1 between the analytical model and the random baseline model. The baseline model’s results are included in the parentheses.

Previous theoretical and simulation-based works in computational social sciences and complex networks has pointed to a variety of processes involved in selecting group affiliation by humans. Those include network ties, rates of interaction and time investment, and shared knowledge and opinions 2 , 4 , 5 , 7 . Yet, empirical validation of these processes and their relevance to explaining and predicting group affiliation and opinion change behavior has been scant. The main reason is a paucity of naturalistic data in which there is a mapping from such behavior to data on beliefs and opinions of group members.

In this paper, we are using the NetSense study dataset, which is unique in containing longitudinal information about communications, locations, and opinions for randomly selected students from the University of Notre Dame geographically diverse student body. We detected in this data 436 groups and their evolution across four semesters of activities. The data captures a large representative sample of social group dynamics within the context of college life. We observe that groups slowly transition toward ingroup stance homogeneity. Moreover, we find that the frequencies of group changes by individuals strongly depend on popularity of their stances. We use these empirical observations to formulate and validate our hypothesis about the group evolution.

Theoretical works have postulated that the level of homophily with other members in groups and communities defines the benefit of membership 4 , 7 . Recent works have also analyzed the roles of homophily in members popularity 15 , ranking of minorities 16 , and structural change 17 , respectively.

In this paper, we formalize a notion of utility of group membership as a function of compatibility of opinions between a member and all others in its group. This formalization enables us to integrate in a single model the three aforementioned phenomena and to account for the two dynamic patterns observed in empirical data. Using this data, we simulate only those changes of opinions or group memberships, which increase members’ utility. We find that this process recreates the group dynamics observed in empirical data and confirms the theoretically postulated role of homophily in these dynamics.

The total member utility includes another term, which is a function of the number of groups to which this member belongs to account for commitment limit each person has. We demonstrate that when applied to our empirical data, this utility increases after each observed change. This leaves an interesting open question: whose average utility should a member maximize when considering a change of an opinion or group membership? To probe different answers, we introduce three maximization criteria. The first, called egocentric, requires that the change of utility of a decision-maker needs to be positive for the move to be accepted. We also consider a more considerate alternative in the form of the weakly altruistic criterion, which requires the positive average change of utility for group members sharing stances with the node making the change. Finally, we produce a strongly altruistic criterion, when all group members on average benefit from a change.

We find that, on average, 93 % of changes made by students in NetSense data are egocentric and 85 % are weakly altruistic. Additionally, over 76 % of these changes are also strongly altruistic at the first semester boundary, with this fraction growing to 86 % at the third semester boundary. These changes increase polarization, resulting in its observed growth. These results empirically confirm that it is important to allow for an altruistic component in agreement with previous work linking altruism, empathy, and group identity. Another observation from empirical data finds the differences in frequency of group membership changes between individuals espousing majority and minority stances. Our utility decays with the decreasing popularity of the member stances. This naturally increases such member motivation to attempt changes to maximize the utility. This agreement with empirical observations validates our hypothesis that utility maximization drives group dynamics.

Subsequently, we introduce a predictive analytical model based on utility maximization to forecast the evolution of groups. It balances the two terms of total utility at the value that globally maximizes model performance on training data. This model accurately predicts affiliation (group joining and group leaving) and stance change.

Overall, these results advance our understanding of group dynamics. They also have important implications for future work on this topic in social sciences, computational social systems and complex networks. In particular, our results show that core processes isolated in previous theoretical and simulation-based work are applicable to naturalistic settings, uncovering the motivations leading people to join or leave groups. We also identify two side effects of utility maximization. The first is that holders of unpopular stances gain lower utility from ingroup interactions and have increased frequency of group changes. The second reveals that desire to spend more time interacting with like-minded others contributes to the increased stance polarization across groups. This kind of polarization has been noted previously 50 , where seemingly innocuous stances and beliefs become highly influential markers determining who interacts with whom, generating small “echo chambers” characterized by opinion homogeneity. This is especially relevant in contemporary contexts featuring relatively low barriers to geographic mobility allowing persons to self-select into social environments and to find and affiliate with groups of their choice online or face-to-face.

Group detection within NetSense

The first step in analyzing group dynamics within the NetSense dataset is to extract groups from the Bluetooth proximity data. As mentioned in the main text, we extract groups from this proximity data using the hierarchical clustering method proposed in 23 . This method first finds persistent connected components in the dynamic network generated from NetSense’s dyadic Bluetooth interactions. Each detected component is a potential group meeting. The largest sequence of components such that each component includes at least a fraction f i of the union of the members of all other components is considered a representation of a meeting of a single group. Members of this group are the nodes that attend at least a fraction f m of the meetings of this group. Finally, each meeting that attracts less than a fraction f mi of its group members is removed. The details of the algorithm to extract groups with the required properties and for finding the best values of parameters that are f i = 0.6 , f m = 0.5 , f mi = 0.3 are presented in 23 . Since this reference uses the same NetSense data as our study does, we use these parameter values to extract groups.

Bluetooth interactions collected on the phones of participants include proximity data of all cell phones. Yet, we found that the ratio of non-participant meetings with participants (required to establish a group) to participant meetings with participants is 0.0489. Thus, no phone owned by a person not enrolled in the NetSense study passed the described above group membership requirements. As the result, the extracted groups include only participants of the NetSense study.

Since groups are not labeled in the data we work with, we must extract a mapping to identify a self-subsisting group across consecutive semesters. The mapping uses the Jaccard Similarity on the two sets of members of both groups. A threshold is set for similarity level for the latter group to be a continuation of the former. By tracking these mappings across semesters, we can identify new or missing members between subsequent group reincarnations. We consider so-identified new members as the true positive cases for joining the affected group and treat missing members as ground truth cases for leaving. However, when an entire group finds no succeeding group mapping in the following semester, we do not record the ingroup participants as a ground truth case of leaving the group en masse. Instead, we just dissolve the group by removing each node from this group for the new semester. The reasoning behind this decision is that the data collection process for NetSense could sometimes be noisy 22 causing the matches to be lost.

With these consistent initial group mappings, we observe in the ground truth data that the majority of people join groups with members with whom they share some communication links. This is in agreement with sociological work on network-based recruitment into groups 5 , 7 discussed previously. To account for this phenomenon, we introduce a lower bound on the number of connections that a given person has with current members of the new group over all meetings in which they participate. This bound is the product of the fraction of group’s members linked via communication contacts to that individual in the current semester and the number of meetings held by the group’s members, which measures group stability. We selected the bound value in such a way that a typical group with five members of which two are familiar to the person attempting to join would require holding 50 meetings to qualify. However, just ten meetings would have sufficed if the joining person had connections to all five members.

Utility difference for changes

Given group mappings between semesters, we can track for each participant all the changes made and not made in group memberships. For example, every group to which participant v does not belong in the subsequent semester is a group that v could have joined, but did not. Additionally, every group that v is a part of in the following semester is a group that v could have left, but abstained from doing so. Between the 196 NetSense participants, 889 group membership and opinion changes were made across all three semester boundaries, and 16,348 possible changes were not. To quantify the actual utility differences between changes made and changes not made, we subtract utility of node v in group g for some attribute a before the change is made from this utility after the change. For a node joining a group without an associated attendance history, the average frequency of attending meetings of its other groups is used, and the frequency is set to 1 if the former is not available. For opinion changes, the total utility change is computed by subtracting the sum of the utility for each group to which node v belongs before the change from that sum after the change.

The analytical predictive model

Our model aims to predict the set of changes maximizing the utility individuals derive from the groups to which they belong or will join with the opinions they hold. In this analytical model, the utility functions use a model parameter x . It defines an exchange rate between utility derived from ingroup interactions versus an adjustment representing the commitment of belonging to the current number of groups. Our analytical model maximizes this augmented version of utility. To find the optimal x , we define a penalty function for changes predicted by the model but not made in reality (false positive changes) and for changes not predicted by the model but made in reality (false negative). There is no penalty for changes predicted correctly (true positive) or changes not made and predicted as such (true negative). Using training data and this penalty system, we find the optimal value of the parameter x . The exact mechanism for analytically solving for the optimal x is shown in the next subsection. Overall, to predict change in group membership and opinions, the model finds the augmented utility differential by subtracting utility before a simulated change is made from this utility after the change.

Equation ( 1 ) formally defines the augmented utility for a node v in group g with a stance on attribute a . Summing over all attributes and over all groups to which v belongs defines the overall utility derived by node v from all ingroup interactions. However, belonging to a group requires the commitment of time, a resource limited for all humans, for attending meetings. Human interactions satisfy a genuine human need, but this part of utility decays from the utility with the optimal time commitment to groups for both under and over participation in groups. This decay grows non-linearly with the imbalance of the committed time. To account for this, we define a quadratic function for each node v , f g ( v ) = W v ( 2 W ¯ - W v ) , where W v = ∑ { g | v ∈ G } w v , g is the total time commitment of node v to all groups to which this node belongs and G denotes a set of all currently existing groups. W ¯ = 1 n ∑ v ∈ P W v is the average time commitment of all nodes to all groups where P denotes a set of all participants, and n stands for the total number of nodes. Using the data from the first two semester boundaries to establish an empirical value of average, we found that W ¯ = 3.95 . The sum of the ingroup interaction and time commitment utility of a node represents its augmented utility.

Both terms of this augmented utility are function of time, but represent different units, therefore we introduce an exchange rate, x , between the two. Hence, a person v who belongs to group g and holds a set A of opinions (attribute values) in a given semester, gains the augmented utility defined as

where G v denotes the set of groups to which node v belongs. When considering a membership change, our model evaluates Eq. ( 3 ) before and after the change is made, subtracting the former from the latter. If the difference is positive, the move is eligible for execution, otherwise it is not. For opinion change, the model subtracts the sum of augmented utilities for v in all the groups to which v belongs before a change from such utility after the change.

The non-linearity of the commitment function makes the eligibility of moves dependent on the order in which they are attempted. When the current number of groups to which a node belongs is below four, the new group joining proceeds to trial before any other change. Otherwise, the group leaving, if one exists, has the execution priority. Opinion changes proceed only after all other changes have finished their trials.

Analytically defining the optimal x

To find an optimal value of x , we define penalty functions for group affiliation and opinion changes. Our model minimizes the total penalty incurred for all individuals within our training data (the first two semester boundaries) to find the globally optimal value of x . The total penalty used is

where C represents the total penalty for all individuals considering a change, x denotes the exchange rate parameter, v is the change-making individual, t represents the semester, P is the set of all study participants, and S is the constant denoting the number of semesters. G v - denotes the groups of which v is currently a member, and G v + denotes the groups that v is eligible to join in semester t . Value p g , v , t l is the penalty for leaving or not leaving group g in semester t + 1 , and p g , v , t j is the penalty for joining or not joining the group g by v in semester t + 1 .

Leaving a group

We can construct the leaving group penalty function based on the utility function as follows:

where Δ g l f v u is computed by subtracting the augmented utility f v u before v leaves group g from that utility after the leave, while | Δ g l f v u | denotes the absolute value of this difference. Using the absolute value in the term containing the unknown parameter x makes the entire function non-linear with respect to x . The factor m v , g , t l represents if person v leaves group g in semester t . In particular, if this person indeed makes the predicted change in the ground truth data, then the value of m v , g , t l is 1, otherwise, it is - 1 . The intuition behind such a penalty function is that a person avoids the penalty for a change if and only if the resulting utility differential is positive.

For changes with a positive utility, the value of m v , t l is equal to 1, so no penalty is incurred when the change is positive. However, if the value of m v , t l is equal to - 1 , a penalty is incurred by the model for predicting a change not made in the ground truth data. Conversely, a change associated with a negative utility differential, but made in the ground truth data incurs a penalty since with m v , t l = 1 , the absolute value of the second term is added to the first. In opposite case, when the second term is equal to the first and when m v , t l = - 1 , the expression in Eq. ( 5 ) reduces to 0.

Joining a group

We construct the joining group penalty function by replacing the difference in the augmented utility for leaving a group in Eq. ( 5 ) with the utility differential of joining a group from. Hence, the penalty for joining a group, p g , v , t j , has properties analogous to properties of penalty for leaving a group. Naturally, the weight of node v is not known before the individual joins the group. Therefore, as before, we use either the average rate of all of v ’s other groups or set the attendance rate to 1.

Solving for the optimal x

The optimal value of x defined by Eq. ( 4 ) is efficiently solvable by replacing each derivative of an absolute value of a term with the product of the sign function of this term and the derivative of this term, yielding the expressions in the form

where A g , v , t l = w g , v ( 2 W v - w g , v - 2 W ¯ ) and B v , t l = ∑ a ∈ A 2 W g , a s v , a - W g , a s v , a + - W g , a s v , a - , where g is a group that node v is leaving. Each term defined by Eq. ( 6 ) changes its value only once for x = B v , t l / A g , v , t l . We will refer to these values as signum discontinuity points. The product of the number of nodes and the number of groups whose memberships are subject to change is the upper bound for the number of signum discontinuity points. After sorting these discontinuity points from smallest to largest, and processing them in this order, we can than find optimal value of x , by computing the derivative’s value at each discontinuity point to identity the smallest value of penalty and corresponding value of x in the current interval between the previous and the current discontinuity point. After processing all discontinuity points, we will have the minimum penalty and the value of x at which it is reached. For our data specifically, using the observations from the first two semester boundaries, we found the optimal x to be 1.4095 × 10 - 4 .

Supplementary Information

Acknowledgements.

We thank S. Levin for the comments. This work was partially supported by the Office of Naval Research (N00014-15-1-2640), the Army Research Office (W911NF-16-1-0524, W911NF-17-C-0099), and the Defense Advanced Research Projects Agency (W911NF-17-C-0099).

Author contributions

A.B. and B.K.S. designed the study, B.K.S formalized the models, A.B. and J.F. implemented the models and conducted the computational experiments. J.F. generated the figures. O.L. and K.C. participated in study design and interpretation of data. A.B., J.F., O.L., and B.K.S, wrote the paper with input from all authors.

Competing interests

The authors declare no competing interests.

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

These authors contributed equally: James Flamino, Boleslaw K. Szymanski and Ashwin Bahulkar.

is available for this paper at 10.1038/s41598-021-96805-7.

2.1 Approaches to Sociological Research

Learning objectives.

By the end of this section, you should be able to:

• Define and describe the scientific method.
• Explain how the scientific method is used in sociological research.
• Describe the function and importance of an interpretive framework.
• Describe the differences in accuracy, reliability and validity in a research study.

When sociologists apply the sociological perspective and begin to ask questions, no topic is off limits. Every aspect of human behavior is a source of possible investigation. Sociologists question the world that humans have created and live in. They notice patterns of behavior as people move through that world. Using sociological methods and systematic research within the framework of the scientific method and a scholarly interpretive perspective, sociologists have discovered social patterns in the workplace that have transformed industries, in families that have enlightened family members, and in education that have aided structural changes in classrooms.

Sociologists often begin the research process by asking a question about how or why things happen in this world. It might be a unique question about a new trend or an old question about a common aspect of life. Once the question is formed, the sociologist proceeds through an in-depth process to answer it. In deciding how to design that process, the researcher may adopt a scientific approach or an interpretive framework. The following sections describe these approaches to knowledge.

The Scientific Method

Sociologists make use of tried and true methods of research, such as experiments, surveys, and field research. But humans and their social interactions are so diverse that these interactions can seem impossible to chart or explain. It might seem that science is about discoveries and chemical reactions or about proving ideas right or wrong rather than about exploring the nuances of human behavior.

However, this is exactly why scientific models work for studying human behavior. A scientific process of research establishes parameters that help make sure results are objective and accurate. Scientific methods provide limitations and boundaries that focus a study and organize its results.

The scientific method involves developing and testing theories about the social world based on empirical evidence. It is defined by its commitment to systematic observation of the empirical world and strives to be objective, critical, skeptical, and logical. It involves a series of six prescribed steps that have been established over centuries of scientific scholarship.

Sociological research does not reduce knowledge to right or wrong facts. Results of studies tend to provide people with insights they did not have before—explanations of human behaviors and social practices and access to knowledge of other cultures, rituals and beliefs, or trends and attitudes.

In general, sociologists tackle questions about the role of social characteristics in outcomes or results. For example, how do different communities fare in terms of psychological well-being, community cohesiveness, range of vocation, wealth, crime rates, and so on? Are communities functioning smoothly? Sociologists often look between the cracks to discover obstacles to meeting basic human needs. They might also study environmental influences and patterns of behavior that lead to crime, substance abuse, divorce, poverty, unplanned pregnancies, or illness. And, because sociological studies are not all focused on negative behaviors or challenging situations, social researchers might study vacation trends, healthy eating habits, neighborhood organizations, higher education patterns, games, parks, and exercise habits.

Sociologists can use the scientific method not only to collect but also to interpret and analyze data. They deliberately apply scientific logic and objectivity. They are interested in—but not attached to—the results. They work outside of their own political or social agendas. This does not mean researchers do not have their own personalities, complete with preferences and opinions. But sociologists deliberately use the scientific method to maintain as much objectivity, focus, and consistency as possible in collecting and analyzing data in research studies.

With its systematic approach, the scientific method has proven useful in shaping sociological studies. The scientific method provides a systematic, organized series of steps that help ensure objectivity and consistency in exploring a social problem. They provide the means for accuracy, reliability, and validity. In the end, the scientific method provides a shared basis for discussion and analysis (Merton 1963). Typically, the scientific method has 6 steps which are described below.

Step 1: Ask a Question or Find a Research Topic

The first step of the scientific method is to ask a question, select a problem, and identify the specific area of interest. The topic should be narrow enough to study within a geographic location and time frame. “Are societies capable of sustained happiness?” would be too vague. The question should also be broad enough to have universal merit. “What do personal hygiene habits reveal about the values of students at XYZ High School?” would be too narrow. Sociologists strive to frame questions that examine well-defined patterns and relationships.

In a hygiene study, for instance, hygiene could be defined as “personal habits to maintain physical appearance (as opposed to health),” and a researcher might ask, “How do differing personal hygiene habits reflect the cultural value placed on appearance?”

Step 2: Review the Literature/Research Existing Sources

The next step researchers undertake is to conduct background research through a literature review , which is a review of any existing similar or related studies. A visit to the library, a thorough online search, and a survey of academic journals will uncover existing research about the topic of study. This step helps researchers gain a broad understanding of work previously conducted, identify gaps in understanding of the topic, and position their own research to build on prior knowledge. Researchers—including student researchers—are responsible for correctly citing existing sources they use in a study or that inform their work. While it is fine to borrow previously published material (as long as it enhances a unique viewpoint), it must be referenced properly and never plagiarized.

To study crime, a researcher might also sort through existing data from the court system, police database, prison information, interviews with criminals, guards, wardens, etc. It’s important to examine this information in addition to existing research to determine how these resources might be used to fill holes in existing knowledge. Reviewing existing sources educates researchers and helps refine and improve a research study design.

Step 3: Formulate a Hypothesis

A hypothesis is an explanation for a phenomenon based on a conjecture about the relationship between the phenomenon and one or more causal factors. In sociology, the hypothesis will often predict how one form of human behavior influences another. For example, a hypothesis might be in the form of an “if, then statement.” Let’s relate this to our topic of crime: If unemployment increases, then the crime rate will increase.

In scientific research, we formulate hypotheses to include an independent variables (IV) , which are the cause of the change, and a dependent variable (DV) , which is the effect , or thing that is changed. In the example above, unemployment is the independent variable and the crime rate is the dependent variable.

In a sociological study, the researcher would establish one form of human behavior as the independent variable and observe the influence it has on a dependent variable. How does gender (the independent variable) affect rate of income (the dependent variable)? How does one’s religion (the independent variable) affect family size (the dependent variable)? How is social class (the dependent variable) affected by level of education (the independent variable)?

Taking an example from Table 12.1, a researcher might hypothesize that teaching children proper hygiene (the independent variable) will boost their sense of self-esteem (the dependent variable). Note, however, this hypothesis can also work the other way around. A sociologist might predict that increasing a child’s sense of self-esteem (the independent variable) will increase or improve habits of hygiene (now the dependent variable). Identifying the independent and dependent variables is very important. As the hygiene example shows, simply identifying related two topics or variables is not enough. Their prospective relationship must be part of the hypothesis.

Step 4: Design and Conduct a Study

Researchers design studies to maximize reliability , which refers to how likely research results are to be replicated if the study is reproduced. Reliability increases the likelihood that what happens to one person will happen to all people in a group or what will happen in one situation will happen in another. Cooking is a science. When you follow a recipe and measure ingredients with a cooking tool, such as a measuring cup, the same results is obtained as long as the cook follows the same recipe and uses the same type of tool. The measuring cup introduces accuracy into the process. If a person uses a less accurate tool, such as their hand, to measure ingredients rather than a cup, the same result may not be replicated. Accurate tools and methods increase reliability.

Researchers also strive for validity , which refers to how well the study measures what it was designed to measure. To produce reliable and valid results, sociologists develop an operational definition , that is, they define each concept, or variable, in terms of the physical or concrete steps it takes to objectively measure it. The operational definition identifies an observable condition of the concept. By operationalizing the concept, all researchers can collect data in a systematic or replicable manner. Moreover, researchers can determine whether the experiment or method validly represent the phenomenon they intended to study.

A study asking how tutoring improves grades, for instance, might define “tutoring” as “one-on-one assistance by an expert in the field, hired by an educational institution.” However, one researcher might define a “good” grade as a C or better, while another uses a B+ as a starting point for “good.” For the results to be replicated and gain acceptance within the broader scientific community, researchers would have to use a standard operational definition. These definitions set limits and establish cut-off points that ensure consistency and replicability in a study.

We will explore research methods in greater detail in the next section of this chapter.

Step 5: Draw Conclusions

After constructing the research design, sociologists collect, tabulate or categorize, and analyze data to formulate conclusions. If the analysis supports the hypothesis, researchers can discuss the implications of the results for the theory or policy solution that they were addressing. If the analysis does not support the hypothesis, researchers may consider repeating the experiment or think of ways to improve their procedure.

However, even when results contradict a sociologist’s prediction of a study’s outcome, these results still contribute to sociological understanding. Sociologists analyze general patterns in response to a study, but they are equally interested in exceptions to patterns. In a study of education, a researcher might predict that high school dropouts have a hard time finding rewarding careers. While many assume that the higher the education, the higher the salary and degree of career happiness, there are certainly exceptions. People with little education have had stunning careers, and people with advanced degrees have had trouble finding work. A sociologist prepares a hypothesis knowing that results may substantiate or contradict it.

Sociologists carefully keep in mind how operational definitions and research designs impact the results as they draw conclusions. Consider the concept of “increase of crime,” which might be defined as the percent increase in crime from last week to this week, as in the study of Swedish crime discussed above. Yet the data used to evaluate “increase of crime” might be limited by many factors: who commits the crime, where the crimes are committed, or what type of crime is committed. If the data is gathered for “crimes committed in Houston, Texas in zip code 77021,” then it may not be generalizable to crimes committed in rural areas outside of major cities like Houston. If data is collected about vandalism, it may not be generalizable to assault.

Step 6: Report Results

Researchers report their results at conferences and in academic journals. These results are then subjected to the scrutiny of other sociologists in the field. Before the conclusions of a study become widely accepted, the studies are often repeated in the same or different environments. In this way, sociological theories and knowledge develops as the relationships between social phenomenon are established in broader contexts and different circumstances.

Interpretive Framework

While many sociologists rely on empirical data and the scientific method as a research approach, others operate from an interpretive framework . While systematic, this approach doesn’t follow the hypothesis-testing model that seeks to find generalizable results. Instead, an interpretive framework, sometimes referred to as an interpretive perspective , seeks to understand social worlds from the point of view of participants, which leads to in-depth knowledge or understanding about the human experience.

Interpretive research is generally more descriptive or narrative in its findings. Rather than formulating a hypothesis and method for testing it, an interpretive researcher will develop approaches to explore the topic at hand that may involve a significant amount of direct observation or interaction with subjects including storytelling. This type of researcher learns through the process and sometimes adjusts the research methods or processes midway to optimize findings as they evolve.

Critical Sociology

Critical sociology focuses on deconstruction of existing sociological research and theory. Informed by the work of Karl Marx, scholars known collectively as the Frankfurt School proposed that social science, as much as any academic pursuit, is embedded in the system of power constituted by the set of class, caste, race, gender, and other relationships that exist in the society. Consequently, it cannot be treated as purely objective. Critical sociologists view theories, methods, and the conclusions as serving one of two purposes: they can either legitimate and rationalize systems of social power and oppression or liberate humans from inequality and restriction on human freedom. Deconstruction can involve data collection, but the analysis of this data is not empirical or positivist.

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Effectiviology

Ingroups and Outgroups: How Social Identity Influences People

An ingroup is a social group that a person identifies as being a part of, based on factors like nationality, race, religion, socioeconomic status, or political affiliation.

An outgroup is a social group that a person does not identify with, based on similar factors as would cause that person to identify with an ingroup (e.g., nationality and religion).

For example, a religious person might view members of their religion as being a part of their ingroup, while viewing members of other religions as being a part of their outgroup.

The concept of ingroups and outgroups has important implications in a wide range of contexts, so it’s important to understand it. As such, in the following article you will learn more about this aspect of social identity, understand the psychology behind it, and see what you can do to account for it in practice.

Examples of ingroups and outgroups

One example of an ingroup and an outgroup appears in the case of a teenager, who views other teenagers as members of their ingroup and adults as members of their outgroup, based on their age.

Another example of an ingroup and an outgroup appears in the case of a college student, who views other students from the same major as belonging to their ingroup, and views students from other majors as belonging to their outgroup.

Many other examples of ingroups and outgroups exist, as people can develop their social identity based on various criteria , such as nationality , ethnicity , race , age , language , socioeconomic status (or social class ), and occupation . The following are specific examples of ways people categorize their ingroups and outgroups, which also illustrate how this type of social identity can influence people’s thoughts and actions:

• In the case of gender , people are sometimes more sympathetic toward members of the same gender as them.
• In the case of religion , people sometimes have negative attitudes toward members of religions other than their own.
• In the case of political affiliation , people sometimes discriminate against members of political movements that rival their own.
• In the case of sports , people are sometimes more likely to view certain behaviors as “rough and dirty” when they’re performed by members of an opposing team, compared to when they’re performed by members of one’s favorite team.

Accordingly, the concept of ingroups and outgroups is associated with various common phenomena, including racism, sexism, nationalism , patriotism, tribalism, collective narcissism , groupthink , and a polarized “us vs. them” mentality .

In addition, given the wide range of criteria that people can use when distinguishing between ingroups and outgroups, there is a huge number of ingroups and outgroups that a person can reasonably identify. Some of these groups will be more important to them than others, though which are more important can vary under different circumstances, for example if the question of membership in a certain group is particularly salient in a certain situation.

People’s tendency to consider ingroups and outgroups is such an innate and powerful impulse that they can be made to identify with a new minimal ingroup based on entirely random , arbitrary , and meaningless criteria. Essentially, the simple act of being told that they’re part of a group can make people identify with it socially, even if the group members have nothing else in common.

Furthermore, the role of social identity has also been found in animals. For example, chimpanzees are more likely to experience contagious yawning when they see a member of their social group yawning, compared to when they see a member of a different group yawning.

Psychology of ingroups and outgroups

A key framework that’s used to explain why and how people distinguish between ingroups and outgroups is  social identity theory , which explains this phenomenon as follows:

“We can conceptualize a group… as a collection of individuals who perceive themselves to be members of the same social category, share some emotional involvement in this common definition of themselves, and achieve some degree of social consensus about the evaluation of their group and of their membership of it… Social groups… provide their members with an identification of themselves in social terms. These identifications are to a very large extent relational and comparative: they define the individual as similar to or different from, as ‘better’ or ‘worse’ than, members of other groups… It is in a strictly limited sense, arising from these considerations, that we use the term social identity . It consists… of those aspects of an individual’s self-image that derive from the social categories to which he perceives himself as belonging. With this limited concept of social identity in mind, our argument is based on the following general assumptions: Individuals strive to maintain or enhance their self-esteem: they strive for a positive self-concept. Social groups or categories and the membership of them are associated with positive or negative value connotations. Hence, social identity may be positive or negative according to the evaluations (which tend to be socially consensual, either within or across groups) of those groups that contribute to an individual’s social identity. The evaluation of one’s own group is determined with reference to specific other groups through social comparisons in terms of value-laden attributes and characteristics. Positively discrepant comparisons between in-group and out-group produce high prestige; negatively discrepant comparisons between ingroup and out-group result in low prestige. From these assumptions, some related theoretical principles can be derived: Individuals strive to achieve or to maintain positive social identity. Positive social identity is based to a large extent on favorable comparisons that can be made between the in-group and some relevant out-groups: the in-group must be perceived as positively differentiated or distinct from the relevant out-groups. When social identity is unsatisfactory, individuals will strive either to leave their existing group and join some more positively distinct group and/or to make their existing group more positively distinct. The basic hypothesis, then, is that pressures to evaluate one’s own group positively through in-group/out-group comparisons lead social groups to attempt to differentiate themselves from each other…” — From “An integrative theory of intergroup conflict” (Tajfel & Turner, 1979)

Other theoretical frameworks have also been proposed to explain the concepts of ingroups and outgroups, including  optimal distinctiveness theory, subjective uncertainty reduction theory, and social dominance theory.

Furthermore, various explanations have been proposed for specific phenomena that are associated with the distinction between ingroups and outgroups. For example:

• Generalizations about outgroup members have been attributed in some cases to the generally higher cost of social interaction with outgroup members, which can arise due to issues such as differences in social norms.
• Devaluation of attributes that are absent from one’s ingroup or that one’s ingroup is low in has been attributed in some cases to the desire to protect personal or collective self-esteem.
• Derogation of outgroup members has been attributed in some cases to the desire to enhance a person’s insecure status within a desirable ingroup.
• Many types of intergroup biases have been attributed to increased empathy toward ingroup members , which is sometimes combined with decreased empathy toward outgroup members.

Note : Just because someone views a certain group of people as being in their ingroup, doesn’t necessarily mean that these people also identify with this ingroup, or consider this person to be a part of it.

Affirmational and negational categorization

When it comes to how people categorize ingroups and outgroups, there is an important distinction between affirmational and negational categorization:

• Affirmational categorization involves defining people by what they  are (e.g., a liberal or a conservative).
• Negational categorization involves defining people by what they aren’t (e.g., not a liberal or not a conservative).

These types of categorizations can result in different social groups, and in different cognitive and behavioral outcomes . For example, one study found that negational categorization leads to increased outgroup derogation, compared to affirmational categorization.

Superordinate groups and subgroups

In the context of ingroups and outgroups, a superordinate group (or superordinate category ) is a social group within which social subgroups (or subordinate groups/categories ) exist . For example, a university student might view university students as a superordinate group within which there are various subgroups of students (e.g., as differentiated based on major or the university they’re attending).

The concept of superordinate groups and subgroups is important when it comes to how people view ingroups and outgroups. As one paper notes:

“From the perspective of the ingroup projection model, the evaluation of intergroup differences depends, first, on whether the ingroup and outgroup are perceived to be included in a shared superordinate category. If not, there is no expectation that the outgroup comply with the same norms or values as the ingroup. The outgroup’s difference is not identity threatening and can be observed in a neutral or even interested way, as something irrelevant or perhaps exotic. If, however, ingroup and outgroup are perceived to be included in a superordinate category, the value or status differentiation between the groups depends on their relative prototypicality for the superordinate group. If there is agreement between the groups about the representation of the superordinate group and the subgroups’ relative prototypicality, the implied value differentiation will be regarded as legitimate and will be non-conflictual. If there is a tendency for one group or both groups to project their own group’s characteristics onto the superordinate group, basically representing it in their own group’s image, the two groups will likely disagree about their subgroups’ relative prototypicality, value, and status, implying intergroup conflict and intergroup discrimination.”

Fluidity of social identity

Social identity can be fluid, rather than stable, meaning that it can change based on factors like the environment a person is in. For example, while a supporter of an opposing sports team might be construed as an outgroup member in some situations, the same person might be recategorized as an ingroup member in other situations, such as when the salient social identity is the superordinate category of “sports fan”.

Accounting for ingroups and outgroups

Accounting for the concept of ingroups and outgroups can be useful in various situations, such as when you want to understand and predict people’s behavior, including your own. For example, this can help you understand why some people apply double standards by criticizing members of their outgroups for behaviors that they fully tolerate among members of their ingroup.

When accounting for ingroups and outgroups in this manner, there are various factors that you can consider in any given situation, including:

• What ingroups and outgroups does the person in question identify?
• What could prompt this person to care about the ingroup/outgroup distinction?
• What are the distinguishing features of each group?
• What do the different groups have in common?
• What are potential points of disagreement between the groups?
• How can the division into social groups influence this person’s behavior toward members of their ingroup and toward members of their outgroup?

The intergroup bias

People often treat others differently based on whether they are members of their ingroup or outgroup, a phenomenon referred to as the intergroup bias (or the  ingroup-outgroup bias ).

The intergroup bias can influence cognition (i.e., thoughts about people, for example in the form of stereotyping), attitude  (i.e., evaluation of people, for example in the form of prejudice), and behavior (i.e., actions toward people, for example in the form of discrimination). Its influence can be explicit , when the person who’s displaying it is aware that they’re doing so, or implicit , when the person who’s displaying it is unaware that they’re doing so, and may even be trying to avoid being biased.

There are two main ways in which the intergroup bias manifests:

• Ingroup favoritism (sometimes referred to as ingroup bias ). This involves favoring one’s ingroup, as well as things that are associated with it (e.g., its characteristics ), often at the expense of outgroup members. This type of bias is associated with phenomena such as nepotism and cronyism, which involve giving preferential treatment to one’s family, friends, or associates, for example when it comes to hiring situations.
• Outgroup antagonism (sometimes referred to as outgroup bias ). This involves displaying negative thoughts, statements, or behaviors directed at outgroups and things that are associated with them, often in the form of outgroup derogation or outgroup hostility . For example, this can involve being prejudiced against outgroup members, and in some cases even infrahumanizing or dehumanizing them.

The intergroup bias can also manifest in various other ways . For example, the linguistic intergroup bias means that people tend to  encode and communicate positive ingroup and negative outgroup behaviors more abstractly than they do negative ingroup and positive outgroup behaviors. Similarly, the outgroup homogeneity effect , can lead the outgroup to be viewed as being more homogeneous than it is or than the ingroup is , and this effect is associated with various cognitive and behavioral patterns , such as generalizing or stereotyping outgroup members ( though an ingroup homogeneity effect can also occur in some cases ). Moreover, people generally have an easier time communicating with people in their ingroup than with people in their outgroup.

These various can co-occur and influence one another , but this doesn’t always happen . For example, it’s possible for intergroup discrimination to occur due to ingroup favoritism, even in the absence of outgroup antagonism.

In addition, various moderating factors  can influence the intergroup bias, for example when it comes to the way in which it manifests . Such factors include, for example, intergroup competition , intergroup similarity , ingroup essentialism , cultural background (e.g. individualistic or collectivist ), and the age and self-esteem of group members.

Overall, the intergroup bias leads people to treat their ingroup and outgroup differently, for example by favoring things that are associated with their ingroup and devaluing those that are associated with their outgroup. This bias can take many forms, and various factors, like intergroup competition, can influence the likelihood that people will display it, as well as the way in which they do so.

Note : It’s possible to conceptualize the intergroup bias as a type of bias, and consequently to categorize the various manifestations of this bias (e.g., ingroup favoritism) as intergroup biases . There are various types of intergroup biases, such as group-serving biases , which cause people to overvalue their ingroup (e.g., by attributing its failures to external factors and its successes to internal factors). In addition, the intergroup bias itself can be considered to be a type of group bias , since it involves social groups.

How to reduce intergroup bias

To reduce the intergroup bias, you can use various combinations of the following techniques:

• Increase awareness of the issue. For example, you can explain what ingroups and outgroups are, why people form them, and how they can influence people’s thoughts and actions.
• Increase the motivation to change. For example, you can explain why the intergroup bias can be problematic , both in general and in the current circumstances.
• Look for things that are shared between the groups. For example, try to find common struggles that the people in both groups share , or characteristics that are shared by members of the two groups.
• Create a shared group identity. For example, you can encourage people to create a group identity that is shared by those in their ingroup and outgroup, potentially while maintaining the distinctiveness of each of the subgroups.
• Identify positive things about the outgroup. For example, try to identify attributes in the outgroup that you find admirable.
• Identify negative things about the ingroup. For example, try to identify attributes in the ingroup that you would criticize if they were displayed by outgroup members.
• Consider the heterogeneity of the outgroup. For example, try to find ways in which the members of the outgroup are different from one another, particularly when it comes to factors that are considered stereotypical of the outgroup.
• Empathize with outgroup members. For example, consider how outgroup members might feel when you act toward them in a certain way, and how you would feel if someone acted the same way toward you.
• Get the perspective of outgroup members. For example, ask someone in the outgroup to explain their beliefs to you.
• Increase contact between group members. Interactions , extended contact , and positive connections across groups can reduce the ingroup bias. Such connections can involve many things , such as direct friendship, cooperation in specific situations, knowledge that an ingroup member has a close relationship with an outgroup member, and even imagined interactions with outgroup members .
• Use general debiasing techniques . For example, you can set up optimal conditions for interactions between group members, and ask people to slow down their reasoning process.

When deciding which techniques to use and how to use them, you should consider both personal and situational factors that pertain to the situation, like:

• Who are the people involved? For example, are you trying to influence yourself, someone else, or a group of people?
• In what context are you trying to influence the people involved? For example, are you their friend in school or their supervisor at work?
• What kind of intergroup bias exists? For example, how do the people in question feel about outgroup members? Are there any active disagreements between groups or just a lack of connection? Are people aware of the bias? If necessary, you can gather information about this to understand the situation better, for example by asking people for their input.
• What are you trying to achieve? For example, are you trying to get people to sympathize more with those that they currently consider to be in their outgroup, or are you trying to get people to be more critical of their own ingroup? Are you trying to only change people’s behavior, or also their underlying attitudes?

It’s important to note that people generally tend to attribute more biased intergroup beliefs to others than to themselves. This is important when it comes to dealing with the intergroup bias, since it means that people, including you, might not realize that they suffer from this bias, or might not realize the extent to which they suffer from it.

Overall, you can reduce the intergroup bias in various ways, including increasing awareness of the issue, creating shared group identity, identifying positive things about the outgroup and negative things about the ingroup, empathizing with outgroup members, and increasing contact between group members. When deciding which techniques to use and how to use them, you should consider relevant personal and situational factors, like who are the people involved and what kind of intergroup bias they’re displaying.

Summary and conclusions

• An ingroup is a social group that a person identifies as being a part of, based on factors like nationality and religion, while an outgroup is a social group that a person does not identify with, based on similar factors.
• For example, a religious person might view members of their religion as being a part of their ingroup, and at the same time view members of other religions as being a part of their outgroup.
• People can identify with ingroups and outgroups based on many factors, like ethnicity, gender, age, occupation, political affiliation, and even arbitrary criteria like being told they’re part of team A and someone else is a part of team B.
• The  intergroup bias involves unequal treatment of ingroups and outgroups, for example in the form of blindly favoring the ingroup and hating the outgroup.
• You can reduce the intergroup bias in various ways, including increasing awareness of the issue, creating shared group identity across groups, identifying positive things about the outgroup and negative things about the ingroup, empathizing with outgroup members, and increasing contact between group members.

Other articles you may find interesting:

• The Empathy Gap: Why People Fail to Understand Different Perspectives
• The Zero-Sum Bias: When People Think that Everything is a Competition
• Double Standards: What They Are and How to Respond to Them

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Article contents

The social brain hypothesis and human evolution.

• Robin I. M. Dunbar Robin I. M. Dunbar Department of Experimental Psychology, University of Oxford
• https://doi.org/10.1093/acrefore/9780190236557.013.44
• Published online: 03 March 2016

Primate societies are unusually complex compared to those of other animals, and the need to manage such complexity is the main explanation for the fact that primates have unusually large brains. Primate sociality is based on bonded relationships that underpin coalitions, which in turn are designed to buffer individuals against the social stresses of living in large, stable groups. This is reflected in a correlation between social group size and neocortex size in primates (but not other species of animals), commonly known as the social brain hypothesis, although this relationship itself is the outcome of an underlying relationship between brain size and behavioral complexity. The relationship between brain size and group size is mediated, in humans at least, by mentalizing skills. Neuropsychologically, these are all associated with the size of units within the theory of mind network (linking prefrontal cortex and temporal lobe units). In addition, primate sociality involves a dual-process mechanism whereby the endorphin system provides a psychopharmacological platform off which the cognitive component is then built. This article considers the implications of these findings for the evolution of human cognition over the course of hominin evolution.

• social brain
• social neuroscience
• brain evolution
• mentalizing
• theory of mind

Introduction

Primates have unusually large brains for body size compared to all other vertebrates. The conventional explanation for this is known as the “social brain hypothesis,” which argues that primates need large brains because their form of sociality is much more complex than that of other species (Byrne & Whiten, 1988 ). This does not mean that they live in larger social groups than other species of animals (in fact, they don’t), but rather that their groups have a more complex structure. In exploring the nature of this unique kind of primate sociality, this article shall argue that, so far, social neuroscience has barely scratched the surface of what is actually involved in what it means to be social. To borrow an analogy, social neuroscience has devoted its time to examining the bricks and mortar in great detail but has so far overlooked the complexity of the building that lies at the real heart of primate (and human) sociality.

The original idea for the social brain dates back to the 1970s, when a number of primatologists suggested that primate intelligence might be related to the demands of their more complex social world (Jolly, 1969 ; Humphrey, 1976 ; Kummer, 1982 ), and the name itself was later coined by the neuroscientist Lesley Brothers ( 1990 ). The primary evidence in support of the social brain hypothesis comes from the fact that, across primates, there is a correlation between mean social group size and more or less any measure of brain size one cares to use (Fig. 1 ) (Dunbar, 1992 , 1998 ; Barton, 1996 ; Barton & Dunbar, 1997 ; Dunbar & Shultz, 2007 ; Dunbar, 2011a ), although the relationship improves as the measure of brain size is focused more toward the frontal lobes (Joffe, 1997 ; Dunbar, 2011a ). In this respect, primates differ from almost all other mammals and birds: in most birds and nonprimate mammals, large brains are associated not with social group size but with a monogamous mating system (Shultz & Dunbar, 2007 , 2010a , b ; Pérez-Barbería et al., 2007 ). Note that in Figure 1 there appears to be an obvious grade difference between the apes and the monkeys. This suggests that apes require a proportionately larger brain than monkeys to deal with groups of the same size, implying that their form of sociality requires more computing power to handle. More careful analysis has since revealed that there are similar grade differences among the monkeys, with a clear distinction between more and less intensely social species. As indicated in Figure 1 , extrapolating from the relationship between group size and neocortex size in apes predicts a natural social group size for humans of around 150 (Dunbar, 1993 ). There is considerable evidence for the existence of such a group size in terms of both natural human groupings (e.g., community sizes in small scale societies) and personal social networks (Dunbar, 2008 , 2011b ).

Figure 1. Mean social group size plotted against relative neocortex volume (indexed as the ratio of neocortex volume to the volume of the subcortical brain) in anthropoid primates. Filled circles: apes (including humans); unfilled circles: monkeys. The regression lines indicate grades of increasing socio-cognitive complexity (indexed by the increasing density of the line). (Redrawn from Dunbar, 2014 .)

Secondary support for the social brain hypothesis comes from neuroimaging studies, which have recently shown that the size of an individual’s living group (macaques: Sallet et al., 2011 ) or personal social network (humans: Lewis et al., 2011 ; Powell et al., 2012 , 2014 ; Kanai et al., 2012 ) correlates with the size of core regions of the brain, mostly in the temporal and, especially, the frontal lobes. These regions turn out to be essentially those involved in the mentalizing, or theory of mind, neural network. This is an important finding because it demonstrates that the social brain hypothesis applies not just at the level of the species but also at the level of the individual. Individuals with more processing capacity in core brain units have proportionately larger social networks.

Historically, a number of alternative ecological and developmental hypotheses have been proposed for why primates have such large brains (for an overview, see Dunbar, 2012b ). Among these, the importance of foraging skills, and especially the role of social learning of foraging skills, has attracted a great deal of interest (e.g., Reader et al., 2011 ). This is not the place to discuss the ensuing debates in detail, but some points of clarification are desirable. It is important to note at the outset that everyone agrees that foraging skills have played an important role in primate evolution; the critical question is whether these have been the main, or only, driver of increases in brain size or whether they are an evolutionary by-product of large brains evolving for other (perhaps mainly social) reasons because the same cognitive skills (causal reasoning, predictive reasoning, planning, etc.) underpin both kinds of behavioral outcomes.

In fact, ecology lies at the heart of all explanations for brain evolution, including the social brain hypothesis: the core differences between them are (1) whether animals solve their ecological problems by individual trial-and-error learning or do so socially and (2) which particular ecological problem (foraging or avoidance of predation) is the more fundamental selection pressure (i.e., the evolutionary driver). What makes the social brain hypothesis intrinsically social is that it claims that animals solve their ecological problems by first solving the problem of group cohesion and coordination. One reason for thinking this is that the primary ecological problem faced by primates (and probably most other animals) is the risk of predation (either by predator species or by conspecific raiders) rather than how to find food (as important as this is in the life of any animal). Primates, like most other animals, solve the predation risk problem by living in groups (van Schaik, 1983 ; Shultz et al., 2004 ; Shultz & Finlayson, 2010 ) and have opted to do so by evolving an unusual form of bonded sociality to maintain group coherence through time (Dunbar & Shultz 2010 ). In effect, primates solve the predation problem indirectly by first solving the problem of creating coherent, stable, coordinated social groups. The issue thus comes down to the task demands of foraging versus social coordination.

A second issue we need to clarify is that the social brain hypothesis has sometimes been seen as simply the quantitative relationship between social group size and brain size shown in Figure 1 . In fact, it should properly be seen in systemic terms as a set of causally related functional behavioral relationships. Animals need to solve a variety of ecological problems in order to be able to survive and reproduce successfully, and primates solve these problems communally in a way that requires them to solve a number of social and physiological problems first. In effect, primates establish the means to solve the ecological problem (an alliance or coalition) ahead of its need, and the capacity to form coalitions in anticipation of their future need seems to be a unique feature of monkey and ape behaviour (Harcourt, 1992 ). It is this that gives rise to the unique form of primate sociality that we refer to as “bonded sociality”, in contrast to the more casual groupings found in most other species of birds and mammals where social groups (herds) can fragment and come together relatively easily (Dunbar & Shultz, 2010 ).

Maintaining the coherence and cohesion of bonded social groups through time is very demanding because animals have to be able to override the natural tendency for the stresses of social life to drive them apart (Dunbar 2010a , 2012a ), and the social brain hypothesis argues that this comes down to resolving various tensions and stresses in both dyadic relationships and the collective set of relationships formed within a social group. To do this, monkeys and apes require novel cognitive skills, and these cognitive skills in turn require appropriate hardware (or wetware in this case) to underpin them. Hence, the relationship between brain size and group size is indirect, and the real functional relationship in the social brain hypothesis is that between brain (or brain region) size and/or wiring and social cognitive abilities or competences that allow primates to manage relationships (Dunbar, 1998 , 2011a , 2012a ). In effect, group size is an emergent property of how well the animals solve the problems associated with living in close proximity.

In other words, in contrast to all the alternative ecological hypotheses that have been proposed (for overviews, see Dunbar 2011a , 2012b ), the social brain hypothesis is a two-step explanation for the evolution of large brains in primates. In contrast to all alternative hypotheses, it explicitly claims that primates are doing something radically different to all other species of animals. The ultimate evolutionary driver is not simply the capacity to engage socially or live in large groups but the extent to which this allows the animals to solve the problems associated with successful survival and reproduction. The proximate mechanism involves solving the coordination problem that lies at the heart of maintaining cohesive social groups. To the extent that primates solve this second problem (group coordination), they also solve the first (predation risk).

What’s So Social About Primate Sociality?

All mammals and birds are, of course, social in some generic sense. The central premise of the social brain hypothesis is that sociality in anthropoid primates (and perhaps a very small number of other mammalian families, including elephants, the dolphin family, and maybe the camel family, that also live in complex, multi-level social systems: Hill et al., 2008 , Shultz & Dunbar, 2010a ) is a step up from this: it involves a more bonded form of sociality built around intense dyadic relationships (friendships) (Silk, 2002 ; Dunbar & Shultz, 2010 ; Massen et al., 2010 ). This form of bonded sociality is a response to the need to handle the stresses that arise when animals live in close proximity and cannot escape these pressures simply by leaving (i.e., by group fission). Living in groups creates significant stresses (mainly due to harassment from conspecifics) that radically affect female fitness (Dunbar, 1980 , 1988 ; Hill et al., 2000 ; Smuts & Nicholson, 1989 ; Roberts & Cords, 2013 ) via an endocrinological mechanism that is now relatively well understood. Among other effects, social stress destabilizes the female menstrual endocrinology system and results in amenhorrea (temporary infertility) (Bowman et al., 1978 ; Abbott et al., 1986 ). Unless animals are able to find solutions that buffer them against these and other costs, group fission is inevitable because the cumulative costs for low-ranking females in terms of lost reproduction can become intense. These stresses are a linear function of group size: the more animals there are in the group, the more individuals one can be harassed by. Moreover, sociality itself is costly: for both primates (Dunbar, 1991 ; Lehmann et al., 2007 ) and humans (Roberts & Dunbar, 2011 ; Miritello et al., 2013 ), relationships require the investment of considerable quantities of time for their maintenance, and this time cost is more or less proportional to the number of individuals involved multiplied by relationship quality (Sutcliffe et al., 2012 ). This is partly because the mechanism involved in creating and servicing relationships involves the endorphin system: the more frequently this is activated, the stronger the relationship. We'll return to this later.

The endogenous stresses that the animals face from living in groups act as a constraint on group size because they create centrifugal forces that, if not defused, will eventually cause the group to break up. In species that do not have bonded social systems (most non-monogamous birds and mammals), these stresses are resolved by individuals simply leaving one group to join a smaller one on an ad hoc basis (the bees-around-a-honeypot model of sociality). This solution is not available to species that live in bonded social systems because of the resistance to individuals transferring between groups created by bonded relationships: members of a group do not tolerate strangers.

Anthropoid primates deal with these stresses by forming defensive alliances mediated by social grooming (Dunbar, 1980 ; Silk et al., 2003 , 2009 ; Wittig et al., 2008 ), and this in turn gives rise to highly structured social networks (Dunbar, 2008 , 2012b ; Lehmann & Dunbar, 2009 ). It is this “decision” to use coalitions as a buffer for the stresses of group living that seems to create the complexity that is widely recognized as characteristic of primate societies. This social world is more complex to handle than the physical world, partly because it is dynamic and in a constant state of flux, and partly because it involves phenomena (other individuals’ mind states) that cannot be perceived directly but instead have to be inferred (Dunbar, 2010a , 2011a , 2012b ). In effect, social systems of this kind are implicit social contracts. For a group to be stable through time, its members have to be willing to allow each other to have a fair (though not necessarily equal!) share of the benefits of sociality. Failure to hold back on prepotent actions that would offer immediate benefits to oneself (such as stealing someone else’s newly discovered food item or displacing someone from a safe roosting site) risks driving others away and destabilizing group cohesion.

One explanation for the grade structure observed in Figure 1 is that this reflects a step-change in the complexity of primate social relationships and the behaviors that underpin them as neocortex volume increases. Indeed, across primates, neocortex size correlates with increasing use of sophisticated mating strategies, larger grooming cliques, higher frequencies of tactical deception, and the formation of coalitions (Pawłowski et al., 1998 ; Kudo & Dunbar, 2001 ; Byrne & Corp, 2004 ; Dunbar & Shultz, 2007 ), as well as increasing complexity of both visual (Dobson, 2009 ) and vocal (McComb & Semple, 2005 ) communication repertoires. One example of this is that cognitively more advanced species like macaques are aware of third-party relationships and refrain from attacking or exploiting another animal when they know that individual has powerful allies, even when those allies are not physically present (Datta, 1983 ). Computational models suggest that managing third-party relationships is more demanding in terms of information processing time than managing simple dyadic relationships (Dávid-Barrett & Dunbar, 2013 ). Similarly, playback experiments have demonstrated that baboons (another cerocpithecine) can integrate at least two different relationship dimensions (kinship and dominance) simultaneously, an ability that may be beyond cognitively less well-endowed species (Bergman et al., 2003 ).

There has been a near-universal tendency to assume that the social groups of all animals are “of a kind.” However, in anthropoid primates, grooming networks become increasingly substructured as the number of individuals in the group increases, especially so in species that have larger neocortices (Kudo & Dunbar, 2001 ; Hill et al., 2008 ; Lehmann et al., 2009 ; Lehmann & Dunbar, 2009b ). In effect, these species are able to maintain two qualitatively distinct kinds of relationship simultaneously: intimate relationships with principal grooming partners (allies) and weaker ones with other group members. In this respect, monkey and ape relationships resemble the two-tier structure of human social relationships, where parallel distinctions are drawn between weak and strong “ties” (Granovetter, 1973 ; Sutcliffe et al., 2012 ) and, cutting across the weak/strong divide, between family and friends (Curry et al., 2013 ; Roberts & Dunbar, 2011 ; Roberts et al., 2014 ). This gives the social systems of anthropoid primates (and those of a small number of other mammals) a layered structure (Hill et al., 2008 ) similar to that found in humans (Zhou et al., 2005 ; Hamilton et al., 2007 ; Dunbar et al., 2015 ). While in both humans and primates an individual’s relationships with the other members of their social group can be ranked on a simple continuum based on frequency of interaction (or emotional closeness: Sutcliffe et al., 2012 ; Roberts et al., 2014 ), these nonetheless cluster into quite discrete layers of very distinctive size, as shown in Figure 2 . The numerical sizes of these grouping layers seem to be common to both human social networks and the structure of primate social groups (Hill et al., 2008 ), and one explanation for the differences in social complexity between species may be the number of layers that can be maintained as a coherent, stable system.

Figure 2. The circles of acquaintanceship for normal human adults. Ego indicates the subject of the network. Normal adult humans are embedded in a series of hierarchically inclusive layers of friendship, with each successive layer enclosing a larger number of individuals at a progressively lower level of emotional closeness. The layers have very distinct sizes, with a scaling ratio that approximates three (each layer is three times the size of the layer immediately inside it). The average sizes of each layer are indicated by the numbers against each circle in Figure 2 , although there is considerable individual variation. The circle of ~150 corresponds to the number of individuals with whom one has reciprocated relationships of trust, obligation, and reciprocity. Beyond the 150 layer there are at least two further layers: the layer of acquaintances (totaling ~500 individuals) and the number of faces one can put names to (~1500 individuals). While the two innermost layers (at 5 and 15) tend to be densely interconnected and constitute a single subnetwork, the remaining layers typically consist of more isolated sets of subnetworks (work colleagues, different sets of hobby club friends, church friends, distant family, etc.) for whom the only connection is via Ego. Each of the four innermost layers is typically split between extended family members and unrelated friends, with an overall ratio of about 50:50 (Sutcliffe et al., 2012 ).

In humans at least, there is evidence suggesting that the size of an individual’s personal social network correlates with their mentalizing competences, indexed as the ability to solve multiple-individual false belief tasks (Stiller & Dunbar, 2007 ; Lewis et al., 2011 ; Powell et al., 2012 ). Mentalizing, perhaps the archetypal form of social cognition, is the ability to handle other individuals’ mind states simultaneously and forms a naturally recursive sequence from first order intentionality (I know my own mind state) through second order (I know that A knows something—otherwise known as formal theory of mind) to a maximum of around fifth order (I know that A knows that B knows that C knows that D knows something) in most normal adult humans (Stiller & Dunbar, 2007 ). Since mentalizing competences (the number of different mind states one can have in mind at the same time) correlate with the volume of core areas in the frontal lobes (Lewis et al., 2011 ; Powell et al., 2012 , 2014 ), it follows that maintaining larger social groups is more demanding in terms of the need to allocate neural resources to those regions of the brain implicated in this task.

Further evidence that social cognition is likely to impose limits on social group size comes from an agent-based model that used processor time to assess the cognitive demands of different levels of information processing associated with managing relationships: this demonstrated not only that more complex information processing is more demanding but, more importantly, that this in turn sets limits on the size of group that can be maintained (Dàvid-Barrett & Dunbar, 2013 ; see also McNally et al., 2012 , Moreira et al., 2013 ). It may be no coincidence, then, that the social brain graph in fact consists of a series of socio-cognitive grades (Dunbar, 1993 , 2011a ; Lehmann et al., 2007 ).

There is evidence that social cognition is itself significantly more demanding than more conventional forms of cognition. We have shown, using both reaction time experiments and fMRI in humans, that mentalizing tasks (those that involve modeling the mental states of other individuals [for more details, see below]) are cognitively more demanding than equivalent non-mentalizing (i.e., purely factual memory) tasks and involve the recruitment of more neural circuitry, and that the magnitude of this difference increases with the complexity of the proposition being processed (Lewis et al., forthcoming ). One reflection of the fact that social cognition may be very costly is that it seems to develop much more slowly than more conventional instrumental cognition. In humans, emotional cue recognition (Deeley et al., 2008 ) and aspects of social cognition such as theory of mind (Blakemore & Choudhury, 2006 ; Henzi et al., 2007 ) can take as long as two decades to mature: their developmental progress seems to map onto the slow process of myelinization in the frontal lobes, which in humans is not completed until well into the third decade (Sowell et al., 2001 , 2003 ; Gogtay et al., 2004 ). Socialization seems to play an important role in this: Joffe ( 1997 ) showed that, across primates, the best predictor of the non-V1 neocortex volume is the length of the period of socialization (the period between weaning and puberty), suggesting that a considerable amount of practice over a lengthy period is required to develop the skills that underpin the social brain. These findings suggest that social skills require conscious thought in frontal lobe units before they eventually become automated and localized elsewhere in the cortex or subcortical regions (in humans, as late as the mid-20s). In other words, merely having a big computer (i.e., brain) is not enough: the hardware requires programming, and this is in large part dependent on extensive social experience. This is social learning on a dramatic scale and may explain why social learning appears to be so important in primates (Reader et al., 2011 ). A useful by-product of this is that the cognition that underpins social learning in this context then becomes available for the exchange of factual information about foraging among adults. Although this has sometimes been interpreted as the driver of brain evolution on the basis of correlational evidence (Reader & Laland, 2002 ; Reader et al., 2011 ; Pasquaretta et al., 2015 ), it could, in fact, just as easily be a consequence rather than the cause of brain evolution—a possibility that, surprisingly perhaps, never seems to have been considered.

Neuropsychology and the Social Brain

In primates, the neocortex accounts for a very large proportion of total brain volume (50–80%, compared to 10–40% in all other mammals) (Finlay et al., 2001 ). This probably explains why even total brain volume on its own gives a reasonable correlation with group size and other social variables in primates—subject to some error variance introduced by species like the gorilla and orangutan that have unusually large cerebella and relatively small neocortices and for whom neocortex size gives a significantly better prediction of community size than does total brain size (Dunbar, 1992 , 2011a ). The fit is improved by excluding striate cortex (the primary visual area, V1, in the occipital lobe: see Fig. 3 ) (Joffe & Dunbar, 1997 ), and it is improved still further by narrowing the focus down to the frontal lobes (Dunbar, 2011a ), implying that the automated processing of incoming perceptual stimuli is not itself a major component of the social brain processes—and why would it be, given that it is the meaning attached to these percepts rather than the percepts themselves that lies at the heart of complex sociality? Since the successive visual processing areas (V1 through V5/MT) scale isometrically with each other up through the occipital and parietal lobes (Dougherty et al., 2003 ; Yan et al., 2009 ), it is likely that the fit would be improved still further by excluding these and other basic perceptual processing regions in the brain (i.e., by focusing mainly on the social cognition circuits in the frontal and temporal lobes). Nonetheless, the fact that the brain acts as a distributed processing network may explain why many of the comparative analyses reveal respectable correlations between social behavior and relatively large brain regions like the neocortex.

Figure 3. The main brain regions involved in mentalizing (the “theory of mind network”). PFC, prefrontal cortex; ACC, anterior cingulate cortex (buried within the cortex); TPJ, temporoparietal junction; STS, superior temporal sulcus; V1, primary visual cortex (striate cortex) in occipital lobe. Dashed arrows indicate the principal connections of the “theory of mind” network.

A number of analyses have shown that executive function skills also increase with brain (or brain region) volume (Dunbar et al., 2005 ; Deaner et al., 2006 ; Shultz & Dunbar, 2010b ; Reader et al., 2011 ). Inevitably, these analyses rely on extremely coarse anatomical resolutions and so have not allowed us to narrow down the cortical circuits involved in any detail (although the availability of more sophisticated imaging techniques may offer new opportunities in this respect; see Mars et al., 2014 ). In the only serious attempt to address this issue to date, Passingham and Wise ( 2012 ) concluded that some brain regions (notably the dorsal prefrontal cortex and the frontal pole [Brodman area 10 at the very center of the forehead]; Fig. 3 ) are crucial for causal evaluation and strategic planning in anthropoid primates. However, their analysis was inevitably based on a very small sample of species. That said, the question as to what function(s) these competences subserve remains open: they may well be generic skills required for all forms of decision-making. All the experimental tests on which these studies are based (“odd-one out” problems, mapping tasks, analogical reasoning, causal reasoning) involve tasks that are essentially instrumental (mainly foraging tasks) rather than social ones. The problem for comparative psychology has always been that genuinely social tasks are not easy to devise: they tend to have long time delays to their outcomes (sometimes on the scale of a lifetime; see Silk et al., 2003 , 2009 ), and experimentalists require an immediately measurable outcome. This has been compounded by a long-held and widespread assumption that, in the wild, animals do very little other than sleep and search for food. Historically, there has been no incentive to devise more complex tasks.

If the different social and ecological uses to which primates put their brains depend on essentially the same cognitive mechanism (and, in particular, the same second-order cognitive processes such as causal reasoning, one-trial learning, analogical reasoning, comparison between two or more alternative projections into the future; Passingham & Wise, 2012 ), it may not be too surprising that there is evidence to support both the instrumental and the social hypotheses. However, a task analysis suggests that, while certain kinds of cognition are likely to be common to all the functional hypotheses for primate brain evolution, there is a natural asymmetry among the hypotheses. The kinds of cognition required to support bonded relationships may allow social (i.e., cultural) transmission of information or novel foraging behaviors, but the reverse is probably not the case; similarly, the kinds of cognition required to support social transmission of foraging skills would likely allow individual trial-and-error learning of foraging behavior, but the reverse is not the case. This is especially likely to be true to the extent that the real complexity of social relationships depends on the need to model other individuals’ minds and behavior in a virtual mental state space, something that seems to be cognitively very demanding even for humans (Lewis et al., forthcoming ). Some evidence to support this suggestion is provided by one of the few experimental studies to compare social and instrumental cognitive skills across primate species directly: Herrmann et al. ( 2007 ) found striking differences between humans and great apes in performance on social tasks but much less so on instrumental tasks.

This suggests (1) that the cognitive demands of instrumental tasks are significantly less than those of social tasks and (2) that the ability to manage social tasks depends crucially on frontal lobe volume (in particular). It would seem that only the social hypotheses would naturally provide for the other hypotheses as emergent by-products. This is not to say that cognitive evolution did not begin with solving simple ecological problems like food-finding (it almost certainly did), but rather to suggest that the demands of social cognition have resulted in additional more sophisticated cognitive competences being added to this mix and that these have, in turn, then allowed more sophisticated food-finding strategies.

In the previous section, it was suggested that mentalizing may be central to complex sociality in humans because it allows individuals to work with virtual representations of other individuals in a mental state space. Meta-analyses of a large number of neuroimaging studies of theory of mind in humans have identified the medial and/or orbitofrontal prefrontal cortex (PFC) as being differentially activated during mentalizing tasks in more than 90% of studies, the temporoparietal junction in 58%, the anterior cingulate cortex in 55%, and superior temporal sulcus (STS) in 50%; other regions that were less commonly activated included the amygdala and the insula (13% of studies in both cases) (Carrington & Bailey, 2009 ; see also Gallagher & Frith, 2003 ; van Overwalle, 2009 ; Apperly, 2012 ). Figure 3 shows the relative locations of these regions in the brain. It is well known that lesions in the prefrontal cortex specifically disrupt social skills, whereas those elsewhere typically do not (Kolb & Wishaw, 1996 ), while the role of the prefrontal cortex and the temporoparietal areas in managing false belief tasks (the benchmark for theory of mind) has been confirmed experimentally using transcranial magnetic stimulation to knock these regions out during experimental tasks (Costa et al., 2008 ). Recently, Makinodan et al. ( 2012 ) reported that mice that had been socially isolated immediately after weaning exhibited irrecoverable functional deficits in both the prefrontal cortex and its myelination, indicating that there may be a critical period that is vital for neurotypical development in a region that is crucial for normal adult social behavior.

This network also appears to be present in at least the catarrhine primates (Rushworth et al., 2013 ), although it is unlikely that it is capable of producing fully functional theory of mind sensu stricto in these species. What it probably does allow is perspective-taking, and that may be an important evolutionary and developmental precursor for full-blown theory of mind as well as being functionally essential for much of what is involved in the social interactions of nonhuman primates. There is considerable evidence that great apes, at least, are able to take others’ perspective into account (Hare et al., 2000 , 2001 ), and perspective-taking is probably crucial to managing monogamous pair-bonded relationships, since monogamy requires close coordination between the pair in a way that is not as necessary in the more fluid social systems that characterize most birds and mammals. Perspective-taking may thus have provided the initial step that started the evolutionary process that eventually gave rise to the evolution of full-blown mentalizing (Dunbar, 2011b ). This would explain why large brains are associated with monogamous mating systems rather than with group size in birds and non-primate mammals (Shultz & Dunbar, 2007 ; Pérez-Barbería et al., 2007 ).

In humans, damage to these prefrontal regions is associated with dramatic (and usually catastrophic) changes in personality and empathy, commonly resulting in socially inappropriate behavior (Adolphs, 1999 ) as well as more directly utilitarian responses on emotionally salient moral dilemmas such as the “trolley task” (Koenigs et al., 2007 ). More broadly, there is evidence from clinical studies that lesions in the prefrontal cortex tend to disrupt the processing (manipulation) of knowledge as well as social skills, whereas lesions in the temporal cortex tend to disrupt factual knowledge but leave the processing of social knowledge unaffected (Roca et al., 2010 ; Woolgar et al., 2010 ). Low densities of gray matter in the prefrontal cortex have also been linked to socially dysfunctional conditions such as schizophrenia (Lee et al., 2004 ; Yamada et al., 2007 ). More importantly for present purposes, individual differences in mentalizing competences in normal human adults correlate with the volume of neural matter in the key regions of the theory of mind network, especially those in the frontal lobes (Lewis et al., 2011 ; Powell et al., 2010 , 2014 ).

Seeley et al. ( 2007 ) have suggested that the regions associated with mentalizing constitute two distinct functional networks: an “executive control” network (involving mainly the dorsolateral prefrontal cortex and parietal areas) and an “emotional salience” network (involving mainly the anterior insular cortex and the anterior cingulate cortex, the amygdala and the hypothalamus), although the former may be specifically associated with rational thinking (“fluid IQ”) rather than social cognition per se (Woolgar et al., 2010 ). Nonetheless, emotion and cognition are not entirely independent of each other: the anterior insula and the medial prefrontal cortex are included in both networks, suggesting some level of interaction between the two networks (Craig, 2009 ).

The prefrontal cortex seems to be crucially involved in the management of social relationships in both humans (Powell et al., 2010 , 2012 , 2014 ; Lewis et al., 2011 ; Kanai et al., 2012 ) and macaques (Sallet et al., 2013 ). More importantly, perhaps, Powell et al. ( 2012 ) have shown, using path analysis, that there is a clear causal sequence here: individual differences in orbitofrontal cortex volume determine mentalizing competences (how well individuals do on multi-level/multi-individual false belief tasks), and mentalizing competences in turn determine the individual’s social network size. In humans, the medial and mid-prefrontal cortex is also associated with moral judgment, critical assessment, and core executive functions related to self-control, deception, and lying (MacDonald et al., 2000 ; Karton & Bachmann, 2011 ), all of which are associated with both social skills in general and theory of mind in particular.

This relationship between mentalizing competences and the volume of the frontal lobe in humans seems to be mirrored in the comparative evidence from primates. It is generally accepted that monkeys do not have theory of mind (second order intentionality) and are thus effectively first order intentional (they are aware of their own mental states, but not those of other individuals). In contrast, there is some evidence to suggest that great apes do understand others’ mind states (chimpanzees: O’Connell & Dunbar, 2003 ; Hare et al., 2000 , 2001 ; Crockford et al., 2012 ; orangutans: Cartmill & Byrne, 2007 ): although they are certainly not as good at formal theory of mind (the ability to pass false belief tests) as 6-year-old children (almost all of whom are fully expert on the task), they are about as good as 4-year-olds (most of whom are on the verge of acquiring this skill). By contrast, normal adult humans have been repeatedly shown to cope with fifth order intentionality (Kinderman et al., 1998 ; Stiller & Dunbar, 2007 ; Powell et al., 2010 ). For the limited data available, these competency levels turn out to map linearly against frontal lobe volume (Fig. 4 ). Figure 4 also plots the putative position of other monkey and great ape species for whom frontal lobe volume data are available on the assumption that their mentalizing competences are the same as those of the other members of their respective taxa. Notice how all these points cluster very tightly around the regression line: no monkey has a frontal lobe volume large enough to move it up to second order, and no great ape has one small enough to move it down to first order or large enough to move it up to third order. These data seem to tell us that mentalizing competences (whatever they actually are) are a function of the absolute volume of the frontal lobes (and most likely gray matter regions within the prefrontal cortex). It is important to appreciate that we still do not really understand what theory of mind (or mentalizing, more generally) actually involves cognitively (Roth & Leslie, 1998 ). Nonetheless, it seems to provide us with a convenient and reliable natural scale of social cognitive abilities, whatever the actual cognitive mechanisms involved may be.

Figure 4. Mentalizing competences (indexed as the maximum achievable order of intentionality) of six Old World monkey and four great ape species, plotted against frontal lobe volume. Monkeys are generally assumed to be first order intentional; experimental evidence suggests that chimpanzees and orangs are just about second order intentional, whereas adult humans are fifth order intentional. Species for whom mentalizing competences have been estimated experimentally (left to right: chimpanzees, orangutans, and humans) are indicated by solid symbols; species for whom mentalizing competences are not known but who are assumed to have the same mentalizing competences as other members of their taxonomic family are indicated by open symbols. (Redrawn from Dunbar, 2009 . Frontal lobe volume data from Bush & Allman, 2004 .)

Indeed, the conventional mentalizing (or intentionality) scale essentially treats all competences below full theory of mind (i.e., second order intentionality) as a homogeneous set. This is almost certainly a radical oversimplification. Sperber & Wilson ( 1986 ) argued that there is a series of finer scale gradations at the lower end of this scale (see also Gärdenfors, 2012 ). This makes sense in the light of the fact that, behaviorally, some species of animals (baboons, macaques, spider monkeys) seem to be socially and cognitively more complex than other species (e.g., colobines, howlers, antelope) (Deaner et al., 2006 , 2007 ; Shultz & Dunbar, 2010b ), despite the fact that on the conventional scale all would be regarded as first order intentional. Unpacking the lower end of the scale may allow us to evaluate better the cognitive differences between the different nonhuman species.

Passingham and Wise ( 2012 ) have pointed out that anthropoid primates are characterized by the evolution of entirely new regions in the prefrontal cortex (in particular Brodman area 10, the frontal pole; Fig. 3 ) that are not present in prosimians or other mammals (see also Sallet et al., 2013 ). They argue that these new regions allowed monkeys and apes to engage in cognitive strategies that other mammals (including prosimian primates) are unable to master. These include one-trial learning (as opposed to more laborious forms of association learning), propositional reasoning, and the capacity to compare the future consequences of two or more alternative behavioral strategies (Passingham & Wise, 2012 ). Among the anthropoid primates, it seems that only the callitrichids (marmosets and tamarins) lack area 10—which might account for this taxon’s unusually labile social system, which can flip rapidly between monogamy, polygamy, polygynandry, and polyandry (Dunbar, 1995a ,b; Opie et al., 2013 ), and the fact that their neocortex:group size ratio is completely out of line with those of all obligately monogamous primates (Dunbar, 2010b ).

So far in this section, we have focused in a rather conventional way on the neuroanatomy of sociality. There is, however, an important aspect of the neurobiology of primate sociality that we need to consider, and this has to do with the role played by neuroendocrines. Much fuss has been made of the role of oxytocin in social relationships (Insel & Young, 2001 ); this mechanism is certainly widely distributed among mammals and has been shown to correlate with some aspects of social behavior in both chimpanzees (Crockford et al., 2013 , 2014 ) and humans (Kosfeld et al., 2005 ) (for an overview, see Dunbar 2010c ). However, the oxytocin system habituates very quickly (Dunbar, 2010c ). More importantly, it is an endogenous response that appears to be insensitive to relationship quality or quantity: it causes individuals to act more or less affiliatively depending on the expression of the relevant gene, but it does not allow them to influence the responses of the individuals with whom they interact. It has been argued that the very unusual kind of bonded social relationships that are found in anthropoid primates (Silk, 2002 ; Shultz & Dunbar, 2010a ; Massen et al., 2010 ) necessitated a more robust bonding mechanism, and this involved exploiting the endorphin system (van Wimersma Greidanus et al., 1988 ; Panksepp et al., 1997 ; Depue & Morrone-Strupinsky, 2005 ; Curley & Keverne, 2005 ; Broad et al., 2006 ; Barr et al., 2008 ; Dunbar, 2010b ; Machin & Dunbar, 2011 ; Resendez et al., 2013 ).

In primates, endorphin activation is triggered by social grooming (Keverne et al., 1989 ), and we have been able to show, using positron emission tomography (PET), that light stroking of precisely the kind that so characteristically defines social grooming in primates also triggers endorphin activation in the human brain, and frontal lobe in particular (Nummenmaa et al., under review). It seems likely that this mechanism is mediated by the afferent c-tactile neurons, a unique set of unmyelinated (hence slow) neurons that respond only to slow stroking and which are not associated with a return motor loop from the brain (Olausson et al., 2010 ; Morrison, 2012 ; Vrontou et al., 2013 ). The significance of this is that the endorphin system responds exogenously (i.e., it is triggered in the recipients of grooming by their social partners, rather than merely endogenously in the groomer as is the case for oxytocin) and so is more responsive to both the quantity of time invested in a relationship and the number of social partners. An endorphin agonist, such as morphine, increases the attractiveness ratings of faces as well as the motivation for continuing to view them, whereas antagonists like naltrexone decrease both (Chelnokova et al., 2014 ). Similarly, PET studies reveal that the density of μ ‎-receptors (the opioid receptors that have a particular affinity for β ‎-endorphins) in core areas of the brain correlate with both the size of personal social network (Nummenmaa et al., under review) and an individual’s attachment style (Nummenmaa et al., in press). These findings suggest a central role for endorphins in the processes that underpin social relationships.

Building a close relationship with someone requires time, and there is a strong correlation between time devoted to socializing with an individual and willingness to support or offer help to that individual in both monkeys (Dunbar, 1980 , 2012a ) and humans (Roberts & Dunbar, 2011 ; Curry & Dunbar, 2013 ; Sutcliffe et al., 2012 ; Curry et al., 2013 ). By triggering endorphin activiation, time spent interacting—grooming in the case of primates, engaging in laughter (Dunbar et al., 2012b ) and perhaps other activities as well as affective touch (Nummenmaa et al., in press) in the case of humans—probably sets up an emotional attachment that allows a very rapid response based on a quantitative index of the quality of the relationship.

In sum, primate social bonding seems to involve a two-process mechanism. In effect, the endorphin system is used to create an internal psychopharmacological platform that enables the individuals to develop a more cognitive long-term relationship that involves reciprocity, obligation, and trust (Sutcliffe et al., 2012 ). The latter, of course, is where the social brain comes in, but it is important to appreciate that beneath the simple group–brain size correlation there is a more complex neurobiological story as well as a more complex behavioral superstructure that is supported by these neurological mechanisms.

Neuropsychological research offers considerable potential for understanding both the processing demands of different kinds of cognition and how these relate to neurological pathways in the brain, and hence to the volumetric demands on different brain units and their interconnections (see also Mars et al., 2014 ). Although there has been considerable interest in social cognition in the recent neuroimaging literature, much of it has typically been concerned with judgments of trustworthiness or with reward and punishment in simple dyadic contexts (e.g., Knoch et al., 2006 ; Behrens et al., 2008 ; Lebreton et al., 2009 ). While this clearly provides valuable insight into how such judgments are made, it does not really capture the richness of the social world in which humans and other primates live. Nor does it engage with the question of just how and why humans differ from other primates, or why anthropoid primates differ from other mammals not just in cognitive abilities but also in their social style. It is these issues that need to be addressed, and so far they have been conspicuous by their absence from the literature on brain evolution.

Social Cognition and Human Evolution

Human evolution has always been viewed through the lens of anatomy and archaeology, with a clear focus on the “stones and bones” of the archaeological record. While this has spawned an interest in the cognitive aspects of human evolution (sometimes referred to as cognitive archaeology; Renfrew & Zubrow, 1994 ), in practice the focus has been on task analyses of the demands of tool-making (e.g., Gowlett, 2006 ). More recently attempts have been made to relate these to mentalizing abilities (Barham, 2010 ; Cole, 2012 ). However, Gamble et al. ( 2011 ) and Gowlett et al. ( 2012 ) remind us that the processes of evolution, and human evolution in particular, do not proceed through material culture as such but through the behavior and minds of the people who made the material culture. Here, social cognition is likely to play an especially important role, and, difficult as this may be to study, it needs to be given much more attention.

Although archaeologists have shied away from grappling with social and cognitive evolution, our growing knowledge of the finer details of the cognitive differences between both human and other primates and, at the level of individual differences, within humans offers the possibility of a more principled approach. Given the explicit quantitative relationships between social and cognitive traits and brain (or brain region) volumes, it may, for example, be possible to make more informed inferences about human cognitive evolution. We do not, of course, have access to soft tissue morphology from fossil species, but there has been a long tradition within paleoanthropology of making inferences about brain composition from the impressions created on the inside of the skull by the brain (Bruner, 2010 ; Bruner et al., 2003 ). More importantly, perhaps, the tight allometric scaling between brain regions in living primates allows us to make inferences about the sizes of these units in fossil specimens, given observed cranial volumes. It is, of course, necessary to be cautious in interpreting individual cases, given that there are well-known exceptions to these allometric relationships in living primates (e.g., the large cerebella and small neocortices of the gorilla and orangutan that we noted earlier). Other exceptions include the impact that latitude has on the size of the visual system in both modern humans (Pearce & Dunbar, 2012 ; Pearce & Bridge, 2013 ) and Neanderthals (Pearce et al., 2013 ), which in the latter case at least results in a smaller neocortex than would be predicted on the basis of cranial volume. Nonetheless, such extrapolations from general equations can tell us something about the overall pattern of evolution. What is important here is that these trajectories are not open-ended: we know roughly where the trajectory started (essentially, the brain composition and cognition of great apes) and where it ended (those of modern humans); our problem is to infer how the changes that must have occurred are strung out between these two endpoints. This will be illustrated here with just two contrasting examples.

The easiest and most secure extrapolation is that for social group size, since the social brain relationship is robust and empirically well substantiated. Using standard allometric equations to interpolate from cranial volume to neocortex volume, we can estimate the community sizes for individual fossil specimens of the main hominin species (Fig. 5 ). The community sizes for living chimpanzees are shown on the left side of the graph for comparison. Two things may be noted. First, for most of early human evolution (the australopithecine phase, represented by the genus Australopithecus and its allies) predicted community sizes do not differ from those observed in living chimpanzees. In effect, early hominins were just ordinary great apes. Second, community size undergoes a rapid increase with the appearance of the genus Homo at around 2 million years ago, stabilizes for about a million and a half years, and then increases rapidly and exponentially through archaic humans ( Homo heidelbergensis and allies) into modern humans. To the extent that community size represents the outcome of the cognitive processes that underpin the social brain, these data reflect the pattern of change in cognition over time.

Figure 5. Median (±50% and 95% ranges) social group for the main hominin species, in temporal order of appearance. Social group is estimated by interpolating through a series of equations from cranial volume, via brain size and neocortex size, to group size (using the relationship shown for apes in Fig. 1 ). The equations are given in Aiello & Dunbar ( 1993 ) and Gowlett et al. ( 2012 ). The values are for individual fossil specimens. The equivalent values for individual chimpanzee populations, based on actual community sizes, are shown on the left. (After Gowlett et al., 2012 .)

We can, however, go one step further by considering cognition directly in the form of mentalizing competences, bearing in mind that these are almost certainly simply an emergent index of more conventional forms of cognition. Given that these appear to correlate linearly with the size of the frontal lobe, and, in general, brain units all correlate with total brain volume, it is in principle a simple matter of interpolating through a series of equations from cranial volume to mentalizing abilities. These are plotted in the same way for all major hominin species in Figure 6 . The values for Neanderthals are corrected to take account of their larger occipital lobes and smaller frontal lobes, reflecting their relatively larger visual system (Pearce et al., 2013 ). Once again, our benchmarks are provided by great apes at level 2 intentionality and modern humans at level 5, and our problem is simply to decide the pattern of change between these two fixed points.

Figure 6. Median (±50% and 95% ranges) mentalizing competences, indexed as the maximum achievable level of intentionality, for the main hominin species, in temporal order of appearance. Mentalizing competences are estimated by interpolating through a series of equations from cranial volume, via brain size and frontal lobe volume, to intentionality level (using the relationship for Fig. 5 , and the equation for mentalizing competences from Dunbar 2010a ). The values are for individual fossil specimens. (After Dunbar, 2015 .)

Two points may be noted from this graph. First, once again, australopithecines were simply jobbing great apes, with no particular pretensions to advanced cognition. Second, all fossil anatomically modern humans (i.e., members of our own species) typically achieve level 5 intentionality, but no archaic humans (including Neanderthals) were likely to have done so. To be sure, all of these would have made level 4 intentionality, which, in the grand scheme of things, is itself pretty impressive: they would not have been intellectual slouches by any means. In cognitive terms, they would have been in the same bracket as the lower end of the normal distribution for modern human adults, and at about the same intellectual level as young teenagers. However, this key difference between archaic and modern humans would have had crucial implications in respect to their capacities for both language and culture.

In normal adult humans, individual differences in the ability to manage complex multi-clause sentences correlates one-to-one with mentalizing competences (Oesch & Dunbar, under review). In other words, mentalizing competences seem to determine how complex our language can be, and this would have had inevitable consequences both for the length of the propositional chains that Neanderthals could have managed and, hence, for the complexity of the stories they told. It may also have had implications for the complexity of the culture that these species would have been able to produce, and this at least seems to be borne out by the archaeological evidence. Attempts to claim that Neanderthal culture was as complex as that of contemporary anatomical modern humans (e.g., Zilhão et al., 2010 ) notwithstanding, the fact is that neither the Neanderthals nor the other archaic humans produced cultural artefacts that were nearly as sophisticated as those of contemporary anatomically modern humans (Klein, 1999 ). Neanderthal tools lacked both the technical sophistication of those developed by modern humans (multi-component tools like bows and arrows or spear-throwers) and the capacity to miniaturize (fine bone and flint points that functioned as arrowheads, buttons, awls, needles), and there is no evidence at all to suggest that they ever produced the kinds of “frivolous” material culture (Venus figurines, toys, cave paintings) that modern humans began to produce in abundance around the time the Neanderthals went extinct (Dunbar, 2015 ). This may be associated with the fact that several genes associated with both brain enlargement and neural efficiency in humans show evidence for strong recent selection (Burki & Kaessmann, 2004 ; Evans et al., 2005 ; Mekel-Bobrov et al., 2005 ; Uddin et al., 2008 ; Wang et al., 2008 ). This does not, of course, mean that Neanderthals were, as a result, in any sense intellectually primitive: it simply means they were not yet quite in the same league as modern humans, and this necessarily has consequences for what they could accomplish in social, cultural, and ecological terms.

On a more general note, human evolution provides a framework within which modern human behavior and cognition can be understood. It can tell us why we ended up the way we are, and so provide insights into the design, and perhaps flexibility, of the human mind. The importance of this historical framework is frequently overlooked in psychology, with its emphasis on the mechanisms and development of behavior in the here and now. Asking how and why we got to be the way we are can tell us a great deal about those mechanisms, especially when seen against a background of primate cognitive and social evolution. And it should remind us, above all, that human social evolution, like that of all primates, is not simply about individual traits but about how these traits enable us to live in an extensive, complex, highly dynamic social world.

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Social Groups: Definition, Types, Importance, Examples

Definition : A social group refers to two or more individuals who share a common social identification, and who perceive themselves to be members of the same social category Hence, the shared perception or understanding that the individual feels as though they belong to a group is instrumental in defining a social group. It is this shared perception that distinguishes social groups from aggregates, this shared perception is referred to as social identification . An aggregate is a group of people who are at the same place at the same time, for example, a number of individuals waiting together at a bus stop may share a common identification, but do not perceive themselves as belonging to a group, hence a collection of individuals waiting at a bus stop are an aggregate and not a social group. Another collection of individuals that needs to be distinguished from social groups are categories, categories consist of sets of people who share similar characteristics across time and space. A similar characteristic can be race or gender, again the feeling of belonging amongst the individuals involved is what distinguished individuals in a social group and individuals in a category.

Types of Social Groups

Social groups are of two kinds- primary and secondary groups . The former is small and tightly knit, bound by a very strong sense of belonging, family is a typical example of this kind of social group. In this type of group, the common interest shared amongst the individuals is the emotional attachment to the group in and of itself. Conversely, secondary groups are large impersonal groups, whose members are bound primarily by a shared goal or activity as opposed to emotional ties. For this reason, individuals typically join secondary groups later in life. Employees at a company would constitute as a secondary group. As individuals within a secondary group grow closer they might form a primary group, wherein the individuals are no longer goal-oriented but are instead a group based on emotional connection.

Lastly, reference groups refer to a group to which an individual or another group is compared. Sociologists call any group that individuals use as a standard for evaluating themselves and their own behaviour a reference group. The behaviours of individuals in groups are usually considered aspirational and therefore are grounds for comparison. From the existence of multiple groups, one can also make the distinction between in-groups and out-groups. This distinction is entirely relative to the individuals, hence reference groups are typically out-groups ie. groups that an individual is not a part of, though an individual may aspire to be a part of the reference group.

The acknowledgment of in-groups and out-groups is relevant to an individual’s perception and thus behaviour. Individuals are likely to experience a preference or affinity towards members of their groups,, this is referred to as in-group bias. This bias may result in groupthink, wherein a group believes that there is only one possible solution or mindset which will lead to a consensus. These poor decisions are not the result of individual incompetence when it comes to decision making but instead but instead due to the social rules and norms that exist in the group, such as the nature of leadership and the nature of homogeneity.

A byproduct of in-group bias is intergroup aggression- experience feelings of contempt and a desire to compete to the members of out-groups. This desire to ‘harm’ members of the out-group is a result of dehumanization wherein the members of the out-group are less they deserve the humane treatment. It is a combination of intergroup aggression and groupthink which often results in harmful prejudice. The dehumanization of out-groups is often used as a political agenda. A notable example from history is the way that the Jewish community (out group) were dehumanized by Nazis (in group), through means such as stereotyping. Hence, prejudice can be a result of extreme intergroup aggression.

Group Behaviour and Social Roles

Within a social group individuals typically display group behaviour, which is seen through the expression of cohesive social relationships. This group behaviour is likely the result of social or psychological interdependence for the satisfaction of needs, attainment of goals or consensual validation of attitudes or values. Hence, group behaviour which is expressed through cooperative social interaction hinges on interdependence. It is this group behaviour that yields the development of an organized role relationship. Social roles are the part people play as members of a social group. With each social role you adopt, your behaviour changes to fit the expectations both you and others have of that role. In addition to social roles, groups also create social norms- these are the unwritten rules of beliefs, attitudes, and behaviours that are considered acceptable in a particular social group. The ability to develop roles and norms which are guided by a common interest is referred to as social cohesion.  The behaviour attached to the norms and roles fulfilled by individuals within a certain social group is usually not the same behaviours exhibited when that individual is not with their group ie. social interaction theory. For example, in a family (which is a social group) a mother is likely not to behave in the way she would in another social group such as at her place of work.

All social groups have an individual who fulfils the leadership role, a leader is an individual who influences the other members of a group, their position may or may not be explicitly stated.

Leadership function considers the intention by which the leader behaves on, either instrumental or expressive. An instrumental leader is focused on a group’s goals, giving orders and making plans in order to achieve those goals. An expressive leader, by contrast, is looking to increase harmony and minimize conflict within the group. In addition to leadership function, there are also three different leadership styles. Democratic leaders focus on encouraging group participation as obsessed with acting and speaking on behalf of the group. Secondly, laissez-faire leaders take a more hands-off approach by encouraging self-management. Lastly, authoritarian leaders are the most controlling by issuing roles to members and setting rules, usually, without input from the rest of the group.

In secondary groups, every member is has a definitive role, however as secondary groups are goal oriented the roles differ from group to group. It is also not uncommon within secondary groups for roles to change. For example, in a school research assignment, initially individuals may fulful roles such as writer, illustrator, researcher, etc in order to write a report. But in the second half which is focused on presenting the repot, members may take up new roles such as presenter, debator, etc, as the goals of the group shifts.

Norms can be simply defined as the expectations of behaviour from group members. These norms which dictate group behaviour can largely be attributed to the groups’ goals and leadership styles. However norms need not only be the result of in-group occurrences, reference groups oftentimes dictate what is and what is not acceptable behaviour. Typically there are norms that apply to the group as a whole known as general norms . Additionally,  there are also norms that are role-specific. For example consider a family (ie. a primary group) everyone in the family attends dinner at 8 pm, while the father cooks dinner and the child sets the table. Everyone attending dinner is a general norm while the act of cooking dinner and setting the table are role-specific to the father and child, respectively.

The Importance of Social Groups

Social groups, primary groups, such as family, close friends, and religious groups, in particular, are instrumental an individuals socialization process. Socialization is the process by which individuals learn how to behave in accordance with the group and ultimately societies norms and values. According to Cooley self-identity is developed through social interaction. Hence, from an identity perspective, primary social groups offer the means through which an individual can create and mold their identity. The development of identity is most rapid and crucial in childhood, hence the importance of family and friends, but the development of identity does continue throughout one’s life. Additionally, from a psychological perspective, primary groups are able to offer comfort and support. Secondary groups, such as members of a group assignment, tend to have less of an influence on identity, in part because individuals within these types of groups are older and hence have a self-identity as well as are familiar with the socialization process.

Natasha Dmello

Natasha D'Mello is currently a communications and sociology student at Flame University. Her interests include graphic design, poetry and media analysis.

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Alexander Yu. Olshanskii

A.Yu. Olshanskii is a Centennial Professor at the Department of Mathematics of Vanderbilt University. Before joining Vanderbilt University, he was a Professor of Mathematics in Moscow State University. His research expertise is mostly in combinatorial and geometric group theory although he has made significant contributions to other areas (finite groups and Lie algebras, in particular). There are very few specialists in group theory whose contributions to the modern understanding of group theory is comparable to Olshanskii's. He solved several key problems in group theory including

• B.H. Neumann's problem about existence of non-finitely based varieties of groups,
• Shmidt-Tarski's problem about existence of infinite non-cyclic groups with all proper subgroups cyclic of prime order,
• von Neumann's problem about existence of non-amenable groups without free non-cyclic subgroups,
• Gromov's problem about existence of infinite quotients of finite exponent for non-elementary hyperbolic groups,
• Gromov's problem about possible distortions of subgroups of finitely presented groups.

Olshanskii's geometric method of graded van Kampen diagrams allowed him and his students to solve many other old and well-known problems in group theory. This includes the solution of Burnside problem for even exponents by S. Ivanov, a former student of Olshanskii, and a construction of a finitely generated non-trivial divisible group by V. Guba, another former student of Olshanskii. The latest applications of his method were a construction of a finitely presented non-amenable group without free non-abelian subgroups (by A.Yu. Olshanskii and M. V. Sapir), and the construction of an infinite finitely generated group with exactly two conjugacy classes (by D. Osin, also a former student of Olshanskii).

Many of the monster groups constructed by Olshanskii and his students are, in modern terms, inductive limits of Gromov-hyperbolic groups. Hyperbolicity plays an important role in Olshanskii's method, and several well known facts about Gromov-hyperbolic groups can be traced back to papers of Olshanskii. After hyperbolic groups were formally introduced into group theory by Gromov, Olshanskii established several key facts about them including the (strong) genericity of hyperbolic groups (conjectured by Gromov), SQ-universality of non-elementary hyperbolic groups, and others.

A.Yu. Olshanskii has more than 20 PhD students . He wrote a very influential book Geometry of defining relations in groups , and several big survey papers. He was an invited speaker at the ICM in Warsaw, 1982, and many other international conferences. Olshanskii is a recipient of several prizes including the Malcev's prize of the Russian Academy of Sciences, Kargapolov prize, and the prize of the Moscow Mathematical Society.

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Trouble in Moscow: From the life of the "Liesma" ["Flame"] Group

[This account covers the Latvian anarchists’ activities in Moscow, up to the Cheka raids of April 1918, when the Bolsheviks attacked anarchists in the city in the name of “Law and Order”]

The group was founded in August 1917 and from the beginning worked in the syndicalist direction.

Before its foundation comrades worked independently, as well as together with existing Russian groups. Later, in view of much greater efficiency if comrades could communicate in Latvian, working with Latvian workers, comrades decided to unite in a permanent group and found quarters which could be open at any time to interested workers, where existing anarchist literature would be available for their use, where on certain days comrades would be able to come together, read lectures, organise “question and answer” evenings for comrades and the broader public. But because such quarters were difficult to find, members gathered once a week in a tiny private apartment, where they were only able, packed like sardines, to review and discuss the most important issues for the group.

When the October revolution started all comrades subscribed either to the Red Guard or to the anarchist fighting organisation, and took the most active part in the October battles, extending their solidarity (hand in hand) with the formerly oppressed but now empowered and oppressing Bolshevik-Communists.

Other comrades, who at the time of the fighting were at the printing house “ Moskovski Listok ” ( The Moscow Sheet ), fought a fierce battle against the Junkers and only because of the cowardice of soldiers who had been called to the assistance of the anarchists after two days of fierce fighting were they disarmed and subjected to the Junker’s violence. Together with coats and hats, also the whole capital of the group – several hundred roubles – was looted. (One of the comrades happened to have the money on him). Thus the group again remained without any means, and we had to postpone our plans to open permanent quarters for an indefinite time.

But time went on. Comrade Bolsheviks, who seized the Government’s money, started to fall behind the growing revolution and, unable to forget their God Marx’s holy words that social revolution is only possible with the concentration of capital and that a lot of time was needed to reach it, it was necessary to step upon the tail of the revolution, so that it wouldn’t derail from its prescribed path and topple the theory laid out in the thick volumes of Marx’s Capital .

All this made comrades think that there was no time to wait until capital would “concentrate” in their cash box in order to rent quarters; quarters had to be acquired now, in the nearest future, irrespective of how and by what means. As social expropriations were already happening in other cities, where private houses, shops, factories and other private property were being nationalised, our comrades considered this a justified and important step in continuing the revolution, and decided to look out for an appropriate building where we could start our club.

In the end such a house was found in Presnensk Pereulok number 3. It was a small house without furniture and needed repairs in order to make living in it possible, but the group still occupied it and after a couple of weeks the Latvian Anarchists’ Club of the “Liesma” group opened there.

In that time, as best we could, we bought in books and literature for our reading table and every Sunday public lectures were held which often attracted an audience of over a hundred people. On Wednesday evenings we organised theoretical reading circles for our comrades themselves, where various political issues were discussed. Special focus was upon the spreading of anarchist ideas, which in the end set the group on a distinct communist [anarchist-communist, not Bolshevik] platform.

With the growth of the group, many and various new needs appeared, one of the most important of which was the need to find a way to publish literature, because it was impossible to gather large masses of people in the tiny building – we had to give the masses something to read. We had to organise communes, show the masses an example and instil in them faith in the future free order (system). In order to realise all this we needed a larger building and financial means, money.

In January the group occupied a house in Malaja (Little) Dimitrovka, but because the house was inhabited, we had to share it with the earlier inhabitants (the owner of the house), and the group took only half of the house. The other half of the house with all the belongings (except for some furniture and the library) was given to the owner with the right to rent it.

The Club is now moving to the new quarters, while the former house is being renovated for a commune (communal flat).

The group started publishing literature. Because of lack of resources, at present only three pamphlets are being published and the other texts will be printed gradually, at the end of each job when the main work has been finished.

Apart from the ideological work, the group has also founded a Fighting Unit with acting members. So that the Fighting Unit could be self-reliant (independent), full ammunition and food parcels for all members were received from the main Red Headquarters. Their task was to defend the revolution, together with the Moscow workers, against the counter-revolutionary element that only waited to raise its head again.

With the arrival of the Latvian group from Kharkov on the order of the Revolutionary Committee, Group “Liesma”, together with the Russian group “Kommuna” occupied a manor house in Vedenski Pereulok (side street), with two “fleugels” (out buildings), where only three people lived. One of these “fleugels” was occupied by “Kommuna”, which had only just been organised and still didn’t have their own quarters. The other “fleugel” was occupied by “Liesma” for the comrades from Kharkov, who had to come to Moscow at the beginning of April.

But because there were exceptionally many historical things in the newly occupied house – precious porcelain, old silver, famous masters’ paintings, extensive libraries and an enormous collection of various ancient icons, the value of which was enormous (indescribable), after an evaluation by some artists both groups decided that, considering that [no] one person was able to use such treasures, which were not in the possession even of many a museum, and which were absolutely out of the reach for a wider public, they consider it their duty to see to it that all these historical treasures should be accessible to the broadest masses of people.

The group established contact with the members of the City Art Committee who took it upon themselves to organise and open a museum, which was also done in the first days of April.

Also, the group “Liesma” established contacts with the actors of the Moscow Latvian Theatre in order to open a Latvian Anarchist Theatre, which promises good results and has met a sympathetic response from the actors. A common united meeting of representatives of both theatres was planned on 12 April, at which the foundation of the Latvian Workers’ Theatre would be laid. “But man supposes and God disposes”… In the night of 11/12 April we were woken up by a terrible noise, amid shooting and noise we could hear people screaming. In the first moments we couldn’t ask anybody either. All rooms were overfilled with soldiers, who were on a horrible looting spree – they just went mad like beasts who broke out of cages – who were ready to tear you to pieces with their teeth for every word you dared to say.

Later we found out that the unexpected guests were a unit of the Soviet government army, the Latvian Riflemen and others, and that on orders of the government we were arrested for some dark deeds, and like in October from the side of the Junkers, now on the orders of the Bolsheviks we were to be destroyed. After several days of torture in the cellars of the Kremlin and behind the walls of Butyrka Prison, we were recognised as “ideological revolutionaries” and were released with the following words from the high authorities: “we fight against bandits, but we leave ideological workers in peace”.

We were recognised as “ideological” workers, but only after our ideological work had been completely destroyed, the literature which had cost us so much efforts and selfless work was burned, the printing press confiscated, all the capital looted. Rendered harmless, we were let off to go where we wanted.

But it is possible to suppress a man, not an idea, and the “Liesma” group, having been robbed twice, did not stop its activities but renewed its work again with twice as much dedication and energy.

Pooling our last strength and means together, we started replacing our literature and started publishing our magazine to spread our ideas even more energetically.

“ R”

The author of this article, “R”, was Janis Birze (Remus), a Latvian anarchist who had taken part in the 1905 Revolution in the Baltic, first as a member of the Latvian Social Democratic Workers Party ( LSDSP ) then as a member of the Anarchist-Communist group “Liesma” and the leader of an anarchist fighting group that carried out numerous expropriations and attempted assassinations in Riga. Arrested in 1907, he was sentenced to 6 years hard labour on 2 April 1908, which he served in Riga and Pleskav (Pskov) prisons, afterwards being exiled to Jenisejas district (Siberia), in the region of Kansk, Vidrina pagasts. Freed by the revolution in March 1917, Birze re-formed “Liesma” in Moscow as this article describes. Of his subsequent life all that is known is that he worked in the Soviet Union in the trade sphere during the 1920s and 30s. His last known place of work was Novosibirsk, where “his life was ended” (according to a Soviet account written in 1962) at the end of the 1930s.

[February 2024 correction: This text is not written by Janis Birze; The actual author is unknown. See Trouble in Moscow [1918] correction which includes more details of Birze’s life.]

“ From the life of the ‘Liesma’ group” Published in “Liesma ” ( Flame ) No. 1, Moscow July 1918, by the Moscow Latvian Anarchist Group “Liesma”

Thanks to Phil Ruff for providing this text.

From: "From the life of the 'Liesma' group" Published in "Liesma" (Flame) No. 1, Moscow July 1918, by the Moscow Latvian Anarchist Group "Liesma" . Translated by: Philip Ruff .

In KSL: Bulletin of the Kate Sharpley Library No. 55-56, October 2008 [Double issue]

• Birze, Janis (1884-1938)
• Bolshevik repression of anarchists after 1917
• Bolshevism / Leninism
• Communist Party CP
• History of Anarchism
• Printing and Publishing
• Russia / Russian Empire / Soviet Union
• Russian Revolution and Russian Civil War

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• KSL: Bulletin of the Kate Sharpley Library No. 55-56, October 2008 [Double issue] .

Supreme Court Wary of States’ Bid to Limit Federal Contact With Social Media Companies

A majority of the justices appeared convinced that government officials should be able to try to persuade private companies, whether news organizations or tech platforms, not to publish information so long as the requests are not backed by coercive threats.

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Adam Liptak

Reporting from Washington

Here’s the latest on the First Amendment case.

A majority of the Supreme Court seemed wary on Monday of a bid by two Republican-led states to limit the Biden administration’s interactions with social media companies, with several justices questioning the states’ legal theories and factual assertions.

Most of the justices appeared convinced that government officials should be able to try to persuade private companies, whether news organizations or tech platforms, not to publish information so long as the requests are not backed by coercive threats.

The dispute was the latest in an extraordinary series of cases this term requiring the justices to assess the meaning of free speech in the internet era.

Justices Brett M. Kavanaugh and Elena Kagan, both former White House lawyers, said interactions between administration officials and news outlets provided a valuable analogy. Efforts by officials to influence coverage are, they said, part of a valuable dialogue that is not prohibited by the First Amendment.

Members of the court also raised questions about whether the plaintiffs — Missouri and Louisiana, along with five individuals — had suffered the kind of injury that gave them standing to sue. They also suggested that a broad injunction prohibiting contacts between many officials and the platforms was not a proper remedy in any event.

“I don’t see a single item in your briefs that would satisfy our normal tests,” Justice Kagan told J. Benjamin Aguiñaga, Louisiana’s solicitor general.

Justice Sonia Sotomayor accused the states of distorting the record in the case. “I have such a problem with your brief,” she told Mr. Aguiñaga. “You omit information that changes the context of some of your claims. You attribute things to people who it didn’t happen to.”

Mr. Aguiñaga apologized “if any aspect of our brief was not as forthcoming as it should have been.”

The justices peppered Mr. Aguiñaga with hypothetical questions about national security, doxxing of public officials and contests that could endanger teenagers, all suggesting that there is a role for vigorous efforts by the government to combat harmful speech.

Justice Samuel A. Alito Jr., the member of the court who appeared most sympathetic to the states’ position, urged his colleagues to remain focused on the case before them.

“Whatever coercion means,” he said, “whatever happened here is sufficient.”

The case arose from a barrage of communications from administration officials urging platforms to take down posts on topics like the coronavirus vaccines and claims of election fraud. Last year, a federal appeals court severely limited such interactions .

The Supreme Court put that injunction on hold last year while it considered the administration’s appeal. If it were to go into effect, said Brian H. Fletcher, a lawyer for the government, it would prohibit all sorts of speech, including public comments from the press secretary or other senior officials seeking to discourage posts harmful to children or conveying antisemitic or Islamophobic messages.

He added that the social media companies had been moderating content on their platforms long before they were contacted by officials, had powerful business incentives to do so and were following their own policies. The companies acted independently of the government, he said, and often rejected requests to take down postings.

“These were sophisticated parties,” he said. “They routinely said no to the government. They weren’t open about it. They didn’t hesitate to do it. And when they said no to the government, the government never engaged in any sort of retaliation.”

Justice Alito said the volume and intensity of the contacts were troubling, as was the suggestion in some of them that the government and the platforms were partners in an effort to combat misinformation about the pandemic.

Mr. Fletcher responded that the messages had to be understood “in the context of an effort to get Americans vaccinated during a once-in-a-lifetime pandemic” at “a time when thousands of Americans were still dying every week.” The platforms, he added, acknowledged “a responsibility to give people accurate information.”

Mr. Aguiñaga presented a different picture of the relationship between the government and the platforms.

“Behind closed doors, the government badgers the platforms 24/7,” he said. “It abuses them with profanity. It warns that the highest levels of the White House are concerned. It ominously says that the White House is considering its options.”

“Under this onslaught,” he added, “the platforms routinely cave.”

The court this term has repeatedly grappled with fundamental questions about the scope of the government’s authority over major technology platforms. On Friday, the court set rules for when government officials can block users from their private social media accounts. Last month, the court considered the constitutionality of laws in Florida and Texas that limit large social media companies from making editorial judgments about which messages to allow.

Those four cases, along with the one on Monday, will collectively rebalance the power of the government and powerful technology platforms in the realm of free speech.

A second argument on Monday posed a related constitutional question about government power and free speech, though not in the context of social media sites. It concerns whether a state official in New York violated the First Amendment by encouraging companies to stop doing business with the National Rifle Association. The justices appeared to be favoring the gun rights group.

The states in Monday’s first case, Murthy v. Missouri, No. 23-411, did not dispute that the platforms were entitled to make independent decisions about what to feature on their sites. But they said the conduct of government officials in urging them to take down what they say is misinformation amounted to censorship that violated the First Amendment.

A unanimous three-judge panel of the U.S. Court of Appeals for the Fifth Circuit agreed, saying that officials from the White House, the surgeon general’s office, the Centers for Disease Control and Prevention, and the F.B.I. had most likely crossed constitutional lines in their bid to persuade platforms to take down posts about what they had flagged as misinformation.

The panel, in an unsigned opinion , said the officials had become excessively entangled with the platforms or used threats to spur them to act. The panel entered an injunction forbidding many officials to coerce or significantly encourage social media companies to remove content protected by the First Amendment.

The Biden administration filed an emergency application in September asking the Supreme Court to pause the injunction, saying that the government was entitled to express its views and to try to persuade others to take action.

The court granted the administration’s application , put the Fifth Circuit’s ruling on hold and agreed to hear the case.

Three justices dissented. “Government censorship of private speech is antithetical to our democratic form of government, and therefore today’s decision is highly disturbing,” Justice Alito wrote, joined by Justices Clarence Thomas and Neil M. Gorsuch.

Those same three justices voiced the most skepticism of the Biden administration’s position at Monday’s argument.

Other justices asked about government interactions with the press. Justice Kavanaugh, who served in the White House in the administration of President George W. Bush, said that it was “probably not uncommon for government officials to protest an upcoming story on surveillance or detention policy and say, you know, if you run that it’s going to harm the war effort and put Americans at risk.”

That was perfectly proper, he suggested, adding that it would be a different matter if the request were backed by a threat of an antitrust action.

Justice Kavanaugh said he understood, based on his earlier government service, that there are “experienced government press people throughout the federal government who regularly call up the media and berate them.”

Justice Kagan echoed the point.

“Like Justice Kavanaugh,” she said, “I’ve had some experience encouraging the press to suppress their own speech.”

She sketched out some of those conversations: “You just wrote a bad editorial. Here are the five reasons you shouldn’t write another one. You just wrote a story that’s filled with factual errors. Here are the 10 reasons why you shouldn’t do that again.”

“I mean,” she said, “this happens literally thousands of times a day in the federal government.”

Chief Justice John G. Roberts Jr., another former White House lawyer, registered a lighthearted dissent, to laughter. “I have no experience coercing anybody,” he said.

But he added that the government is not monolithic and that different parts of it may hold and press competing views.

Justice Alito, who has been the subject of critical news coverage, seemed taken by the idea of pushing back against it, wondering aloud whether the court’s public information officer was in the courtroom.

“Maybe she should take a note about this,” he said. “So whenever they write something that we don’t like, she can call them up and curse them out and say ‘Why don’t we be partners? We’re on the same team.’”

What happens next? The court will probably not issue a decision until June.

Now that the arguments in the case are complete, the justices will cast tentative votes at a private conference in the coming days. The senior justice in the majority will then assign the majority opinion to a colleague — or keep it. Draft opinions, most likely including concurrences and dissents, will be prepared and exchanged.

On average, it takes the Supreme Court about three months after an argument to issue a decision. But rulings in a term’s more important cases — and this one qualifies — tend not to arrive until near the end of the term in June, no matter how early they were argued.

There are other reasons to think the decision will not arrive until late June. The case was argued in the court’s next-to-last two-week sitting, and the court will be busy this month and next with arguments on abortion and former President Donald J. Trump’s claim that he is immune from prosecution on charges that he plotted to overturn the 2020 election.

The decision must also be harmonized with rulings in related cases, including ones on whether states may prohibit technology platforms from deleting posts based on the viewpoints they express and whether a state official in New York violated the First Amendment by encouraging companies to stop doing business with the National Rifle Association.

Scholars have given varied explanations for why the biggest cases tend to land in June, no matter when they were argued. One is that justices keep polishing the opinions that will define their legacies until the last possible moment.

A 2015 study in The Duke Law Journal suggested a more personal reason: “The justices, most of whom have busy social schedules in Washington, may want to avoid tensions at their social functions by clustering the most controversial cases in the last week or two of the term — that is, just before they leave Washington for their summer recess.”

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The court is hearing a related case on the N.R.A.

The question in the social media case is in one sense about government power over the internet. But at bottom it is about something more fundamental: striking the right balance between government advocacy for its policies, which is permissible, and coercion backed by threats of punishment, which is not.

The justices will return to that tension in Monday’s second argument, over whether a state official in New York violated the First Amendment by encouraging companies to stop doing business with the National Rifle Association after the 2018 school shooting in Parkland, Fla.

That question is at a general level the same as the one in the social media case, and its answer will also involve finding the constitutional line between persuasion and coercion.

The second case, National Rifle Association v. Vullo, No. 22-842, concerns the activities of Maria Vullo, a former superintendent of the New York State Department of Financial Services. In the aftermath of the school shooting in Parkland, Ms. Vullo said banks and insurance companies should consider whether they wanted to provide services to the group.

The N.R.A. sued, saying Ms. Vullo’s efforts leveraged government power in a way that violated the First Amendment.

A unanimous three-judge panel of the U.S. Court of Appeals for the Second Circuit, in New York, ruled against the N.R.A. Judge Denny Chin , writing for the panel, acknowledged that government officials may not “use their regulatory powers to coerce individuals or entities into refraining from protected speech.

“At the same time, however,” he wrote, “government officials have a right — indeed, a duty — to address issues of public concern.”

Ms. Vullo’s actions were on the right side of the constitutional line, Judge Chin wrote. Key documents, he said, “were written in an evenhanded, nonthreatening tone and employed words intended to persuade rather than intimidate.”

In its petition seeking Supreme Court review , the N.R.A. said the appeals court’s ruling could have sweeping consequences.

“The Second Circuit’s opinion below gives state officials free rein to financially blacklist their political opponents — from gun-rights groups to abortion-rights groups to environmentalist groups and beyond,” the petition said.

One sign that the N.R.A. has a plausible First Amendment argument: It is represented by the American Civil Liberties Union . David Cole, the A.C.L.U.’s national legal director, will argue the case on behalf of the gun rights group.

“In this hyper-polarized environment, where few are willing to cross the aisle on anything,” Mr. Cole said, “the fact that the A.C.L.U. is defending the N.R.A. here only underscores the importance of the free-speech principle at stake.”

Charlie Savage

Oral arguments in the case are over.

Fletcher, the Justice Department lawyer, is now back for rebuttal.

Jim Rutenberg

Justice Jackson asks Aguiñaga whether government can’t move against harm, like posts that might lead teens to commit suicide, and can’t tell the platforms to move to reduce the posts. Aguiñaga says the government can call platforms to say there’s a problem, but can’t apply pressure to remove that content.

“Is it your view that the government authorities could not declare those circumstances a public emergency and encourage social media platforms to take down the information that is instigating this problem?” “Your honor, the government absolutely can use the pulpit to say publicly, here’s what we recognize to be a public health issue, emergency. We this is obviously extremely terrible and the public shouldn’t tolerate this. Platforms — we see it’s going on on the platforms — but they can’t call the platforms and say, listen, we really think you should be taking this down because look at the problems that it’s causing.” “If it’s protected speech, your honor, then I think we get closer. But like, look, if you think that that’s if that’s clearly the way you’re asking the question, I understand that the instinct that that may, you know, may not be a First Amendment issue. I guess what I fall back on, your honor, is that at least where the government itself — there is no emergency like this. There’s nothing —” “No, my hypothetical is there is an emergency. My hypothetical is that there is an emergency, and I guess I’m asking you in that circumstance, can the government call the platforms and say this information that you are putting up on your platform is creating a serious public health emergency? We are encouraging you to take it down.” “I was with you right until that last comment, your honor. I think they absolutely can call and say this is a problem. It’s going rampant on your platforms. But the moment that the government tries to use its ability as the government and its stature as the government to pressure them to take it down, that is when you’re interfering with the third-party speech rights.”

Justice Ketanji Brown Jackson asks if the government could actually tell platforms they needed to take down leaked classified information. Aguiñaga, the Louisiana lawyer, says the government could do that. “I think that would be a great example where strict scrutiny would be in the government’s favor.”

“Part of the reason why you might be running into all of these difficulties with respect to the different factual circumstances is because you’re not focusing on the fact that there are times in which the government can, depending on the circumstances, encourage, perhaps even coerce, because they have a compelling interest in doing so. And so that’s why I keep coming back to the actual underlying First Amendment issue, which we can isolate in this case and just talk about about coercion. But I think that you have to admit that there are certain circumstances in which the government can provide information, encourage the platforms to take it down, tell them to take it down. I mean, what about what about the hypo of someone posting classified information? They say it’s my free speech right. I believe that, you know, I got access to this information and I want to post it. Are you suggesting that the government couldn’t say to the platforms, we need to take that down?” “No, your honor, because I think that would be a great example where strict scrutiny would cut in the government’s favor.”

Heightening the problem of the flawed factual record undergirding the litigation, Justice Sotomayor starkly accuses Aguiñaga himself of distorting facts of what happened: “I have such a problem with your brief, counselor. You omit information that changes the context of some of your claims. You attribute things to people who it didn’t happen to — at least in one of the defendants, it was her brother that something happened to, not her. I don’t know what to make of all this because I am not sure how we get to prove direct injury in any way.”

Aguiñaga apologizes if any of the brief is not “as forthcoming” as it should have been.

This exchange between Justice Kagan and Aguiñaga, in which the Louisiana lawyer concedes that it can be OK for the government to provide information to the platforms under some circumstances, shows the problem with having an unreliable factual record compiled by Judge Doughty about what actually happened. Fletcher is citing the district court’s findings to say the government crossed the line into official censorship, but are the specifics accurate?

Aguiñaga goes at a key issue in the government content moderation efforts of the past few years — what began as attempts to address foreign meddling and disinformation moved to cover speech from Americans in 2020, over an election and a pandemic.

In an exchange with Justice Kagan, Aguiñaga, the Louisiana lawyer, identifies a difference from the hypothetical Justice Kavanaugh brought up about government officials raising concerns with a newspaper about publishing an article: That is the government going directly to the speaker. What is “so pernicious” here is that the government is going to a third party — the platforms — and people may never learn about it.

Steven Lee Myers

Aguiñaga describes the communications between officials and the platforms as “unrelenting government pressure” going on outside of the public eye. “Pressuring platforms in back rooms, shielded from public view, is not using a bully pulpit. That’s just being a bully.”

The justices and Fletcher keep referencing a 1963 precedent, Bantam Books, Inc. v. Sullivan . It centered on a state commission in Rhode Island that was empowered to notify distributors of certain books and magazines it considered to be obscene that it had decided the materials were objectionable, request its “cooperation,” and to advise them that the commission had a duty to recommend prosecution of purveyors of obscenity. The Supreme Court ruled that these notices intimidated businesses and resulted in the suppression of the sale of the books and magazines --- an unconstitutional system of informal censorship.

Aguiñaga disputes the Biden administration’s standard for the case: “We don’t need coercion as a theory,” he said. He said the government “cannot induce, encourage or promote” to get private actors to do what government cannot: censor Americans’ speech.

Benjamin Aguiñaga, the solicitor general of Louisiana, is now arguing. Louisiana is one of the Republican-controlled states that brought the lawsuit arguing that the government was coercing social media platforms into taking down posts, amounting to government censorship.

Justice Kavanaugh, a former lawyer in George W. Bush's White House, raises a national-security analogy. He notes that it’s “not uncommon” for government officials to protest to a newspaper an upcoming story on surveillance or detention policy and say, “If you run that, it is going to harm the war effort and put Americans at risk.” The implication is under the lower-court rulings, the government would not be allowed to express such concerns.

Fletcher, the government lawyer, agrees with Justice Kavanaugh that that is an example of a valuable interchange as long as it stays on the persuasion side of the line. “Platforms — newspapers — want to know if their publishing a story might put lives at risk. And they don’t have to listen to the government, but that’s information that they can consider when exercising their editorial judgment.”

Justice Kavanaugh adds that it would become problematic coercion if the government tacked on that “And if you publish the story we’re going to pursue antitrust action against you.” Fletcher agrees again with him: “Huge problem, yeah.”

Fletcher argues that the social media platforms are large companies with sufficient clout to rebuff government efforts to influence them. In fact, when university researchers working with the government flagged misinformation about the 2020 election, the platforms refused to do anything two-thirds of the time .

Justice Kavanaugh pivots back to the Biden “killing people” line and notes that in a national security context there is some history of the government warning media outlets that their stories threaten to endanger Americans’ lives.

Justice Kagan floated the idea of resolving the case by saying the plaintiffs were not entitled to an injunction because they could not show they faced an imminent threat of future harm at the time of litigation, without getting into past content moderation disputes. Fletcher, the government lawyer, agrees that would be the narrowest and easiest way to resolve the matter.

Fletcher, the government lawyer, argues that government officials can persuade a private party to do something the private party is lawfully allowed to do, even when the government could not do that thing itself. He gives various examples: when government officials called on colleges to do more about antisemitic speech on campuses after the Oct. 7 attacks in Israel, encouraging parents to monitor their children’s cell phone usages, or internet companies to watch out for child sexual abuse on their platforms, even if the Fourth Amendment would prevent the government from doing that directly. Telling social media companies that the government thinks their algorithms or posting of certain things are causing harm is the same, he said.

Here’s the moment Justice Gorsuch was referring to regarding the president’s “killing people” line.

Justice Gorsuch asks if President Biden’s statement that the platforms were “killing people” by allowing misinformation to flow in the middle of the pandemic would amount to coercion. Fletcher says the president made clear afterward it was “exhortation, not threat.”

Alito is saying he can’t imagine the federal government cajoling and threatening print media. Fletcher notes that there is that sort of back and forth with the press, but Alito is getting at the central unsettled element in all of these cases. The platforms are something different, they provide pipelines, but through their algorithms and rules they are also applying their own version of editorial standards.

Justice Alito just demonstrated that he has bought into the misinformation in the lower court’s work, citing an example where a White House official said in an email to Facebook. “I want an answer on what happened here and I want it today.” In reality that (inappropriate) language was about getting a technical problem fixed with the presidential Instagram account, not about content moderation.

When Fletcher, the government lawyer, points out that the example Alito cited as happening repeatedly actually only happened once and had nothing to do with content moderation, Alito blows past the demonstration of his misunderstanding. “OK, well, put that aside. There’s all the rest.”

Fletcher raised an issue that some experts and research organizations involved in the case have: that many of communications cited in the lower courts included disputed facts, including quotations taken out of context.

Terry A. Doughty, the Trump-appointed district court judge who set the case off (after the Republican plaintiffs filed it in a place that would ensure he got the case) issued a ruling that has itself been criticized as being riddled with misinformation and conspiracy theories about what happened, setting up an unreliable factual record for the constitutional issues at play.

Here’s a recent item on the Just Security website that catalogs many ways Doughty torqued the facts to play into right-wing culture war notions — for example, falsely editing a quote in an email to Dr. Anthony Fauci to remove the word “published” before the words “take down” in a way that made it look like a scientist was urging steps to remove misinformation about vaccines, as opposed to publishing a rebuttal to it.

That goes to another major question in the case — did government action directly cause platforms to remove speech? The government has argued that it left it to platforms to make their own decisions as it flagged and even cajoled the companies about content.

Justice Samuel Alito and Fletcher, the government lawyer, are sparring over whether there are sufficient facts to show that the plaintiffs’ injuries — having their posts taken down to having accounts suspended by social media companies — were caused by government actions, giving them standing to seek an injunction. This is a major problem with this case, according to many legal observers.

The question, as the rhetoric in the case has gone so far, is whether the White House used its classic “bully pulpit” or used its “pulpit to bully.”

Brian Fletcher, the principal deputy solicitor general for the Biden administration, argues that the government has a right to speak to social media companies in an attempt to persuade them to choose to remove or reduce certain matters, so long as it does not coerce them. He said the test should be whether the government makes threats; bully-pulpit exhortations are protected by the First Amendment, he argued.

Michael D. Shear and David McCabe

Here’s how a Trump-appointed judge saw the Biden administration pressuring companies to censor speech.

This First Amendment case is a flashpoint in a broader effort by conservatives to document what they contend is a liberal conspiracy by Democrats and tech company executives to silence their views, and it taps into fury on the right about how social media companies have treated stories about the origins of Covid, the 2020 election and Hunter Biden, the president’s son.

The final outcome could shape the future of First Amendment law in a rapidly changing media environment and alter how far the government can go in trying to prevent the spread of potentially dangerous claims, particularly in an election or during emergencies like a pandemic.

The government’s actions at the heart of the case were intended largely as public health measures during the coronavirus pandemic. But a federal judge in Louisiana framed his ruling back in July through the filter of partisan culture wars — asking whether the government violated the First Amendment by unlawfully threatening the social media companies to censor speech that the Biden administration found distasteful and potentially harmful to the public.

In his ruling, Judge Terry A. Doughty described dozens of interactions between the administration and social media companies, including how two months after President Biden took office, his top digital adviser had emailed officials at Facebook urging them to do more to limit the spread of “vaccine hesitancy” on the social media platform.

Judge Doughty also outlined how officials at the Centers for Disease Control and Prevention had held “weekly sync” meetings with Facebook, once emailing the company 16 “misinformation” posts. And in the summer of 2021, he wrote, the surgeon general’s top aide had repeatedly urged Google, Facebook and Twitter to do more to combat disinformation.

The case sets up a showdown between the justices and a conservative appeals court.

The appeals court that partly upheld limits on the Biden administration’s communications with social media companies has a reputation for issuing decisions too conservative for the Supreme Court, which is itself tilted to the right by a six-justice supermajority of Republican appointees.

Of the appeals court’s 17 active judges, only five were appointed by Democratic presidents. Six members of the court were appointed by President Donald J. Trump.

The court, the U.S. Court of Appeals for the Fifth Circuit, in New Orleans, hears appeals from federal trial courts in Louisiana, Mississippi and Texas. Those forums often attract ambitious lawsuits from conservative litigants correctly anticipating a favorable reception, and rulings from trial judges in those states are often affirmed by the Fifth Circuit.

But when those cases reach the Supreme Court, they sometimes fizzle out. An attack on the constitutionality of the Consumer Financial Protection Bureau, endorsed by three Trump appointees on the Fifth Circuit, did not seem to fare well before the justices when it was argued in October. Another, in which the Fifth Circuit struck down a federal law barring domestic abusers from carrying guns, was also met with skepticism .

Other rulings from the Fifth Circuit, on issues like immigration , abortion pills and so-called ghost guns , have also met with at least tentative disapproval from the Supreme Court, suggesting that the appeals court is out of step with the justices.

At a news briefing in September, Irv Gornstein, the executive director of Georgetown’s Supreme Court Institute, said the Fifth Circuit had staked out positions that “at least some of the center bloc of conservatives aren’t going to be able to stomach.”

He added that some of the rulings by the Fifth Circuit were “delivered from Crazy Town” and that “it would be shocking if at least some of those decisions are not reversed.”

The case is one of several about the intersection of free speech and technology on the court’s docket.

The Supreme Court hears First Amendment cases fairly often. But it has never before considered as many cases on what the Constitution has to say about free speech in the internet era as it will in its current term, set to end in June.

Monday’s argument will be the fifth one since October considering the fundamental question of the scope of government power over social media platforms. The decision in that case and the four others will collectively mark the boundaries of free expression in the digital age.

Last month, the Supreme Court considered two cases on whether Florida and Texas could limit prominent social media companies from moderating content on their platforms, appearing skeptical of the breadth of laws that had been enacted in an effort to shield conservative voices on technology sites.

On Friday, the court, in two unanimous rulings, set requirements for when elected officials could block people from their social media accounts.

The court’s decisions in the five cases will have broad political and economic implications. A ruling that tech platforms have no editorial discretion to decide which posts to allow, for instance, would expose users to a greater variety of viewpoints, but it would almost certainly amplify the ugliest aspects of the digital age, including hate speech and disinformation.

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Former Treasury Secretary Steve Mnuchin says he’s putting together investor group to buy TikTok

The U.S. House of Representatives passed a bill this week that would ban TikTok in the U.S. if its China-based owner doesn’t sell, now the fate of the bill rests with the U.S. Senate.

TikTok once again finds itself in a precarious position as lawmakers in Washington move forward with a bill that could lead to a nationwide ban on the platform. (AP Production: Marissa Duhaney)

FILE - Former Treasury Secretary Steve Mnuchin speaks with reporters outside the White House, March 29, 2020, in Washington. Mnuchin says he’s going to put together an investor group to buy TikTok, a day after the House of Representatives passed a bill that would ban the popular video app in the U.S. if its China-based owner doesn’t sell its stake.(AP Photo/Patrick Semansky, File)

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Devotees of TikTok, Mona Swain, center, and her sister, Rachel Swain, right, both of Atlanta, monitor voting at the Capitol in Washington, March 13, 2024. (AP Photo/J. Scott Applewhite, File)

Devotees of TikTok gather at the Capitol in Washington, as the House passed a bill that would lead to a nationwide ban of the popular video app if its China-based owner doesn’t sell, Wednesday, March 13, 2024. Lawmakers contend the app’s owner, ByteDance, is beholden to the Chinese government, which could demand access to the data of TikTok’s consumers in the U.S. (AP Photo/J. Scott Applewhite)

Former Treasury Secretary Steven Mnuchin said Thursday that he will put together an investor group to buy TikTok after the House passed a bill that would ban the popular video app in the U.S. if its China-based owner does not sell its stake.

During an interview on CNBC’s “Squawk Box,” Mnuchin, who served under President Donald Trump, said he had spoken “to a bunch of people” about creating an investor group that would purchase the popular social media company. He offered no details about who may be in the group or about TikTok’s possible valuation.

“This should be owned by U.S. businesses,” Mnuchin said. “There’s no way that the Chinese would ever let a U.S. company own something like this in China.”

TikTok did not respond to a request for comment.

AP AUDIO: Former Treasury Secretary Steve Mnuchin says he’s putting together investor group to buy TikTok.

AP correspondent Jackie Quinn reports on a former U.S. cabinet member’s effort to acquire TikTok.

The House bill, passed by a vote of 352-65, now goes to the Senate, where its prospects are unclear. Lawmakers in the Senate have indicated that the measure will undergo a thorough review. If it passes in the Senate, President Joe Biden has said he will sign it.

House lawmakers acted on concern that TikTok’s current ownership structure is a national security threat . Lawmakers from both parties and administration officials have voiced concerns that TikTok’s parent company, ByteDance, could be compelled by Chinese authorities to hand over data on American users, spread pro-Beijing propaganda or suppress topics unfavorable to the Chinese government.

TikTok, for its part, has long denied that it could be used as a tool of Chinese authorities. The company insists it has never shared U.S. user data with the Chinese government and will not do so if asked. To date, the U.S. government also has not provided evidence that shows TikTok shared such information with authorities in China.

Asked whether the Mnuchin consortium could assuage national security concerns about TikTok, White House national security spokesman John Kirby said the administration was focused on providing “context and information” to the Senate.

The fight over the platform takes place as U.S.-China relations have shifted into strategic rivalry, especially in areas such as advanced technology and data security, seen as essential to each country’s economic prowess and national security.

If passed and signed into law, the House bill would give ByteDance 180 days to sell the platform to a buyer that satisfies the U.S. government. It would also bar ByteDance from controlling TikTok’s algorithm, which feeds users videos based off their preferences.

In addition to Mnuchin, some other investors, including “Shark Tank” star Kevin O’Leary, have voiced interest in buying TikTok’s U.S. business. But experts have said it could be challenging for ByteDance to sell the platform to a buyer who could afford it in a few months.

Big tech companies are best positioned to make such a purchase, but they would likely face intense scrutiny from antitrust regulators, which Mnuchin emphasized.

“I don’t think this should be controlled by any of the big U.S. tech companies. I think there could be antitrust issues on that,” he said during the interview. “This should be something that’s independent so we have a real competitor. And users love it, so it shouldn’t be shut down.”

He also said the app would need to be rebuilt in the U.S. with new technology.

In many ways, social media companies have become battlegrounds for partisan disagreements about how to control disinformation while protecting free speech. Mnuchin’s effort to buy TikTok comes as Trump and his allies have long complained about what they see as social media muzzling conservative voices.

Trump himself has voiced opposition to the House bill, saying that a ban on TikTok would help its rival, Facebook, which he continues to lambast over his 2020 election loss. Some other Republicans who oppose the bill say the U.S. should simply tell Americans about the security concerns with TikTok, but let them decide if they want to use the platform.

Meanwhile, some Democrats have expressed concern about singling out one company when other social media platforms also collect vast amounts of data on users. Opponents of the bill also say it would disrupt the lives of content creators who rely on the platform for income and run afoul of the First Amendment, which protects free speech.

This isn’t the first time a TikTok sale has been in play.

When Mnuchin was Treasury secretary, the Trump administration brokered a deal in 2020 that would have had U.S. corporations Oracle and Walmart take a large stake in TikTok on national security grounds.

The deal would have also made Oracle responsible for hosting all TikTok’s U.S. user data and securing computer systems to ensure national security requirements are satisfied. Microsoft also made a failed bid for TikTok that its CEO, Satya Nadella, later described as the “strangest thing” he had ever worked on.

Instead of congressional action, the 2020 arrangement was in response to a series of executive actions by Trump targeting TikTok.

But the sale never went through for a number of reasons. Trump’s executive orders got held up in court as the 2020 presidential election loomed. China also imposed stricter export controls on its technology providers.

Associated Press journalists Matt O’Brien, Aamer Madhani and Ali Swenson contributed to this report.

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Anatomy of a Vasectomy

Plus, why Gypsy Rose Blanchard exited social media and called it the “doorway to hell”

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Episode Notes

Rachelle and Candice catch up on Matthew Koma, the husband of singer/actress Hilary Duff, who got a vasectomy and documented his post-op Valium spiral on Instagram. Then, they give an update on Gypsy Rose Blanchard , who wiped her public Instagram and TikTok accounts after her parole officer allegedly warned that she might get in trouble and go back to jail.

This podcast is produced by Se’era Spragley Ricks, Daisy Rosario, Candice Lim and Rachelle Hampton.

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Candice Lim is the co-host of ICYMI, Slate’s podcast about internet culture. She comes to Slate from NPR, where she was an assistant producer at Pop Culture Happy Hour . Prior to that, she was an intern at NPR’s How I Built This , the Hollywood Reporter, WBUR, and the Orange County Register. She graduated from Boston University with a bachelor's degree in journalism and grew up in Orange County, California.

Rachelle Hampton is a culture writer and reporter at Slate and co-host of ICYMI. Her work has appeared in the New Republic, Pacific Standard, Smithsonian Magazine, and In These Times. She still hasn't recovered from Tumblr’s demise.

House Republican budget calls for raising the retirement age for Social Security

WASHINGTON — A new budget by a large and influential group of House Republicans calls for raising the Social Security retirement age for future retirees and restructuring Medicare.

The proposals, which are unlikely to become law this year, reflect how many Republicans will seek to govern if they win the 2024 elections. And they play into a fight President Joe Biden is seeking to have with former President Donald Trump and the Republican Party as he runs for re-election.

The budget was released Wednesday by the Republican Study Committee , a group of more than 170 House GOP lawmakers, including many allies of Republican presidential nominee Donald Trump. Apart from fiscal policy, the budget endorses a series of bills “designed to advance the cause of life,” including the Life at Conception Act, which would aggressively restrict abortion and potentially threaten in vitro fertilization , or IVF, by establishing legal protections for human beings at “the moment of fertilization.” It has recently caused consternation within the GOP following backlash to an Alabama Supreme Court ruling that threatened IVF.

The RSC, which is chaired by Rep. Kevin Hern, R-Okla., counts among its members Speaker Mike Johnson, R-La., and his top three deputies in leadership. Johnson chaired the RSC from 2019 to 2021; his office did not immediately respond when asked about the new budget.

For Social Security, the budget endorses "modest adjustments to the retirement age for future retirees to account for increases in life expectancy." It calls for lowering benefits for the highest-earning beneficiaries. And it emphasizes that those ideas are not designed to take effect immediately: "The RSC Budget does not cut or delay retirement benefits for any senior in or near retirement."

The new budget also calls for converting Medicare to a "premium support model," echoing a proposal that Republican former Speaker Paul Ryan had rallied support for. Under the new RSC plan, traditional Medicare would compete with private plans and beneficiaries would be given subsidies to shop for the policies of their choice. The size of the subsidies could be pegged to the "average premium" or "second lowest price" in a particular market, the budget says.

The plan became a flashpoint in the 2012 election, when Ryan was GOP presidential nominee Mitt Romney's running mate, and President Barack Obama charged that it would "end Medicare as we know it." Ryan defended it as a way to put Medicare on better financial footing, and most of his party stood by him.

Medicare is projected to become insolvent in 2028, and Social Security will follow in 2033. After that, benefits will be forcibly cut unless more revenues are added.

Biden has blasted Republican proposals for the retirement programs, promising that he will not cut benefits and instead proposing in his recent White House budget to cover the future shortfall by raising taxes on upper earners.

The RSC budget also presents a conundrum for Trump, who has offered shifting rhetoric on Social Security and Medicare without proposing a clear vision for the future of the programs.

Notably, the RSC budget presents three possible options to address the projected insolvency of the retirement programs: raise taxes, transfer money from the general fund or reduce spending to cover the shortfall.

It rejects the first two options.

"Raising taxes on people will further punish them and burden the broader economy–something that the spend and print regime has proven to be disastrous and regressive," the budget says, adding that the committee also opposes "a multi-trillion-dollar general fund transfer that worsens our fiscal situation."

That leaves spending cuts.

The RSC budget launches blistering criticism at "Obamacare," or the Affordable Care Act, and calls for rolling back its subsidies and regulations that were aimed at extending insurance coverage.

Sahil Kapur is a senior national political reporter for NBC News.

Ex-Turning Point volunteer charged in Jan. 6 riot highlights the group's toxic influence

My friends, happy Tuesday! Here's your Tech Drop, the top news of the week at the intersection of tech and politics.

Turning Point USA knows how to pick 'em

On Friday, federal authorities arrested Isabella Deluca, a social media influencer with ties to the pro-Trump organization Turning Point USA, for her alleged role in the deadly Jan. 6 Capitol riot. My NBC News colleague Ryan Reilly reported Monday that Deluca, who has hundreds of thousands of followers on her social media platforms, also appears to have worked for Rep. Paul Gosar, R-Ariz., after participating in the riot (Gosar's office told the Arizona Mirror it had been unaware of Deluca's history). Turning Point also emailed the Mirror that it hadn’t previously heard of the charges and that Deluca was “an unpaid volunteer ambassador, one of 200.” (Deluca did not respond to NBC News' request for comment.) Deluca’s arrest and notoriety in the conservative movement are a reminder of Turning Point's corrosive impact on the Republican Party and of its  he a v y r el i a n ce on social media influencers  to spread its far-right messaging. For more on that, read the work of scholar Matthew Boedy , who’s literally writing the book on Turning Point USA and Christian nationalism.

And read more on Deluca’s arrest at NBC News

Peer review

Politico is out with an article this week on the trend among voter suppression artists who, in their purported quest to defend “election integrity,” are bringing lawsuits to obtain voter records and, in many cases, posting those records online. Understandably, some officials are concerned about this tactic being deployed to intimidate voters from showing up at the polls or from casting votes that could result in their harassment. I expect to see more intimidation tactics like this from the right in the months ahead, considering Donald Trump has already told his party that patrolling the vote is more important in the upcoming election than voting itself . 

Read more in Politico

Women's group reacts to creepy robocall

The League of Women Voters filed a lawsuit last Thursday against a political operative and two Texas-based companies said to be responsible for sending out deceptive robocalls that mimicked President Joe Biden’s voice to New Hampshire voters ahead of the state’s primary in January. The robocalls, which misinformed voters about participating in the primary, caused widespread alarm and underscored the potential threat to democracy that artificial intelligence-enabled tools and other deceptive media could pose in the upcoming elections.

Read more at the Associated Press

The sharks are circling TikTok

Check out my blog from last week about the rich right-wingers who’ve expressed interest in buying TikTok in the event that the Senate passes a bill to force its sale from the China-based company ByteDance. I’ve criticized lawmakers for focusing on TikTok’s national security issues while seemingly ignoring similar issues at American-based social media companies like Instagram and X. But I also think the hair-on-fire panic from avid TikTokers has been similarly ignorant of TikTok’s negative influence on political discourse. That said, take a look at this list of wannabe buyers; I wouldn’t feel comfortable with any of these right-wing rich dudes having access to my data anymore than I'd want China’s government potentially accessing it. 

Read more at the ReidOut Blog

Texas screws Pornhub

The porn website Pornhub blocked potential users in Texas from accessing its platform after the state passed a law that requires sites that offer “sexual material harmful to minors” to verify that its users are at least 18 years old. Pornhub’s parent company has criticized the law as ineffective in serving its ostensible purpose of protecting children. Mashable has an article that explains why critics say such laws are nearly impossible to enforce and carry their own security risks. Some Texans aren’t taking the decision lying down, though. After Pornhub blocked Texans' access, online searches for VPNs — networks that can be used to evade the block — reportedly spiked in the state .

Read more at The Texas Tribune

Meta is shutting down Crowdtangle in August, ending access to a tool long used by academics and journalists to monitor how disinformation and hate speech spread on social media sites. Meta says a new platform, called the “Meta Content Library,” will take its place, although it’s being made available largely to academics and nonprofit groups; most journalists won’t be able to access it. Understandably, people who specialize in misinformation research are concerned about the timing of this decision and any effect it might have on the 2024 elections. 

Read more at The Wall Street Journal

Pro-conspiracy theory crowd heads to court

You should judge the principles of online content moderation by its enemies. All eyes are on the Supreme Court after it heard arguments in a case that could bar the federal government from offering any kind of guidance to social media companies about curbing the spread of disinformation. For years now, right-wingers have framed anti-disinformation efforts as biased against conservatives. Tellingly, one of the organizations participating in the suit to gut the government’s anti-misinformation efforts is Children’s Health Defense , the anti-vaccine organization founded by conspiracy theorist and presidential candidate Robert F. Kennedy Jr. The organization hosted a rally outside the Supreme Court on Monday. 

Read more in Wired

This article was originally published on MSNBC.com

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