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Article • 7 min read

What Is Problem Solving?

By the Mind Tools Content Team

We all spend a lot of our time solving problems, both at work and in our personal lives.

Some problems are small, and we can quickly sort them out ourselves. But others are complex challenges that take collaboration, creativity, and a considerable amount of effort to solve.

At work, the types of problems we face depend largely on the organizations we're in and the jobs we do. A manager in a cleaning company, for example, might spend their day untangling staffing issues, resolving client complaints, and sorting out problems with equipment and supplies. An aircraft designer, on the other hand, might be grappling with a problem about aerodynamics, or trying to work out why a new safety feature isn't working. Meanwhile, a politician might be exploring solutions to racial injustice or climate change.

But whatever issues we face, there are some common ways to tackle them effectively. And we can all boost our confidence and ability to succeed by building a strong set of problem-solving skills.

Mind Tools offers a large collection of resources to help you do just that!

How Well Do You Solve Problems?

Start by taking an honest look at your existing skills. What's your current approach to solving problems, and how well is it working? Our quiz, How Good Is Your Problem Solving? lets you analyze your abilities, and signposts ways to address any areas of weakness.

Define Every Problem

The first step in solving a problem is understanding what that problem actually is. You need to be sure that you're dealing with the real problem – not its symptoms. For example, if performance in your department is substandard, you might think that the problem lies with the individuals submitting work. However, if you look a bit deeper, the real issue might be a general lack of training, or an unreasonable workload across the team.

Tools like 5 Whys , Appreciation and Root Cause Analysis get you asking the right questions, and help you to work through the layers of a problem to uncover what's really going on.

However, defining a problem doesn't mean deciding how to solve it straightaway. It's important to look at the issue from a variety of perspectives. If you commit yourself too early, you can end up with a short-sighted solution. The CATWOE checklist provides a powerful reminder to look at many elements that may contribute to the problem, keeping you open to a variety of possible solutions.

Understanding Complexity

As you define your problem, you'll often discover just how complicated it is. There are likely several interrelated issues involved. That's why it's important to have ways to visualize, simplify and make sense of this tangled mess!

Affinity Diagrams are great for organizing many different pieces of information into common themes, and for understanding the relationships between them.

Another popular tool is the Cause-and-Effect Diagram . To generate viable solutions, you need a solid understanding of what's causing the problem.

When your problem occurs within a business process, creating a Flow Chart , Swim Lane Diagram or a Systems Diagram will help you to see how various activities and inputs fit together. This may well highlight a missing element or bottleneck that's causing your problem.

Quite often, what seems to be a single problem turns out to be a whole series of problems. The Drill Down technique prompts you to split your problem into smaller, more manageable parts.

General Problem-Solving Tools

When you understand the problem in front of you, you’re ready to start solving it. With your definition to guide you, you can generate several possible solutions, choose the best one, then put it into action. That's the four-step approach at the heart of good problem solving.

There are various problem-solving styles to use. For example:

• Constructive Controversy is a way of widening perspectives and energizing discussions.
• Inductive Reasoning makes the most of people’s experiences and know-how, and can speed up solution finding.
• Means-End Analysis can bring extra clarity to your thinking, and kick-start the process of implementing solutions.

Specific Problem-Solving Systems

Some particularly complicated or important problems call for a more comprehensive process. Again, Mind Tools has a range of approaches to try, including:

• Simplex , which involves an eight-stage process: problem finding, fact finding, defining the problem, idea finding, selecting and evaluating, planning, selling the idea, and acting. These steps build upon the basic, four-step process described above, and they create a cycle of problem finding and solving that will continually improve your organization.
• Appreciative Inquiry , which is a uniquely positive way of solving problems by examining what's working well in the areas surrounding them.
• Soft Systems Methodology , which takes you through four stages to uncover more details about what's creating your problem, and then define actions that will improve the situation.

Further Problem-Solving Strategies

Good problem solving requires a number of other skills – all of which are covered by Mind Tools.

For example, we have a large section of resources to improve your Creativity , so that you come up with a range of possible solutions.

By strengthening your Decision Making , you'll be better at evaluating the options, selecting the best ones, then choosing how to implement them.

And our Project Management collection has valuable advice for strengthening the whole problem-solving process. The resources there will help you to make effective changes – and then keep them working long term.

Problems are an inescapable part of life, both in and out of work. So we can all benefit from having strong problem-solving skills.

It's important to understand your current approach to problem solving, and to know where and how to improve.

Define every problem you encounter – and understand its complexity, rather than trying to solve it too soon.

There's a range of general problem-solving approaches, helping you to generate possible answers, choose the best ones, and then implement your solution.

Some complicated or serious problems require more specific problem-solving systems, especially when they relate to business processes.

By boosting your creativity, decision-making and project-management skills, you’ll become even better at solving all the problems you face.

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What is Problem Solving? (Steps, Techniques, Examples)

By Status.net Editorial Team on May 7, 2023 — 5 minutes to read

What Is Problem Solving?

Definition and importance.

Problem solving is the process of finding solutions to obstacles or challenges you encounter in your life or work. It is a crucial skill that allows you to tackle complex situations, adapt to changes, and overcome difficulties with ease. Mastering this ability will contribute to both your personal and professional growth, leading to more successful outcomes and better decision-making.

Problem-Solving Steps

The problem-solving process typically includes the following steps:

• Identify the issue : Recognize the problem that needs to be solved.
• Analyze the situation : Examine the issue in depth, gather all relevant information, and consider any limitations or constraints that may be present.
• Generate potential solutions : Brainstorm a list of possible solutions to the issue, without immediately judging or evaluating them.
• Evaluate options : Weigh the pros and cons of each potential solution, considering factors such as feasibility, effectiveness, and potential risks.
• Select the best solution : Choose the option that best addresses the problem and aligns with your objectives.
• Implement the solution : Put the selected solution into action and monitor the results to ensure it resolves the issue.
• Review and learn : Reflect on the problem-solving process, identify any improvements or adjustments that can be made, and apply these learnings to future situations.

Defining the Problem

To start tackling a problem, first, identify and understand it. Analyzing the issue thoroughly helps to clarify its scope and nature. Ask questions to gather information and consider the problem from various angles. Some strategies to define the problem include:

• Brainstorming with others
• Asking the 5 Ws and 1 H (Who, What, When, Where, Why, and How)
• Analyzing cause and effect
• Creating a problem statement

Generating Solutions

Once the problem is clearly understood, brainstorm possible solutions. Think creatively and keep an open mind, as well as considering lessons from past experiences. Consider:

• Creating a list of potential ideas to solve the problem
• Grouping and categorizing similar solutions
• Prioritizing potential solutions based on feasibility, cost, and resources required
• Involving others to share diverse opinions and inputs

Evaluating and Selecting Solutions

Evaluate each potential solution, weighing its pros and cons. To facilitate decision-making, use techniques such as:

• SWOT analysis (Strengths, Weaknesses, Opportunities, Threats)
• Decision-making matrices
• Pros and cons lists
• Risk assessments

After evaluating, choose the most suitable solution based on effectiveness, cost, and time constraints.

Implementing and Monitoring the Solution

Implement the chosen solution and monitor its progress. Key actions include:

• Communicating the solution to relevant parties
• Setting timelines and milestones
• Assigning tasks and responsibilities
• Monitoring the solution and making adjustments as necessary
• Evaluating the effectiveness of the solution after implementation

Utilize feedback from stakeholders and consider potential improvements. Remember that problem-solving is an ongoing process that can always be refined and enhanced.

Problem-Solving Techniques

During each step, you may find it helpful to utilize various problem-solving techniques, such as:

• Brainstorming : A free-flowing, open-minded session where ideas are generated and listed without judgment, to encourage creativity and innovative thinking.
• Root cause analysis : A method that explores the underlying causes of a problem to find the most effective solution rather than addressing superficial symptoms.
• SWOT analysis : A tool used to evaluate the strengths, weaknesses, opportunities, and threats related to a problem or decision, providing a comprehensive view of the situation.
• Mind mapping : A visual technique that uses diagrams to organize and connect ideas, helping to identify patterns, relationships, and possible solutions.

Brainstorming

When facing a problem, start by conducting a brainstorming session. Gather your team and encourage an open discussion where everyone contributes ideas, no matter how outlandish they may seem. This helps you:

• Generate a diverse range of solutions
• Encourage all team members to participate
• Foster creative thinking

When brainstorming, remember to:

• Reserve judgment until the session is over
• Encourage wild ideas
• Combine and improve upon ideas

Root Cause Analysis

For effective problem-solving, identifying the root cause of the issue at hand is crucial. Try these methods:

• 5 Whys : Ask “why” five times to get to the underlying cause.
• Fishbone Diagram : Create a diagram representing the problem and break it down into categories of potential causes.
• Pareto Analysis : Determine the few most significant causes underlying the majority of problems.

SWOT Analysis

SWOT analysis helps you examine the Strengths, Weaknesses, Opportunities, and Threats related to your problem. To perform a SWOT analysis:

• List your problem’s strengths, such as relevant resources or strong partnerships.
• Identify its weaknesses, such as knowledge gaps or limited resources.
• Explore opportunities, like trends or new technologies, that could help solve the problem.
• Recognize potential threats, like competition or regulatory barriers.

SWOT analysis aids in understanding the internal and external factors affecting the problem, which can help guide your solution.

Mind Mapping

A mind map is a visual representation of your problem and potential solutions. It enables you to organize information in a structured and intuitive manner. To create a mind map:

• Write the problem in the center of a blank page.
• Draw branches from the central problem to related sub-problems or contributing factors.
• Add more branches to represent potential solutions or further ideas.

Mind mapping allows you to visually see connections between ideas and promotes creativity in problem-solving.

Examples of Problem Solving in Various Contexts

In the business world, you might encounter problems related to finances, operations, or communication. Applying problem-solving skills in these situations could look like:

• Identifying areas of improvement in your company’s financial performance and implementing cost-saving measures
• Resolving internal conflicts among team members by listening and understanding different perspectives, then proposing and negotiating solutions
• Streamlining a process for better productivity by removing redundancies, automating tasks, or re-allocating resources

In educational contexts, problem-solving can be seen in various aspects, such as:

• Addressing a gap in students’ understanding by employing diverse teaching methods to cater to different learning styles
• Developing a strategy for successful time management to balance academic responsibilities and extracurricular activities
• Seeking resources and support to provide equal opportunities for learners with special needs or disabilities

Everyday life is full of challenges that require problem-solving skills. Some examples include:

• Overcoming a personal obstacle, such as improving your fitness level, by establishing achievable goals, measuring progress, and adjusting your approach accordingly
• Navigating a new environment or city by researching your surroundings, asking for directions, or using technology like GPS to guide you
• Dealing with a sudden change, like a change in your work schedule, by assessing the situation, identifying potential impacts, and adapting your plans to accommodate the change.
• How to Resolve Employee Conflict at Work [Steps, Tips, Examples]
• How to Write Inspiring Core Values? 5 Steps with Examples
• 30 Employee Feedback Examples (Positive & Negative)

What Is Problem Solving?

You will often see beach clean-up drives being publicized in coastal cities. There are already dustbins available on the beaches,…

You will often see beach clean-up drives being publicized in coastal cities. There are already dustbins available on the beaches, so why do people need to organize these drives? It’s evident that despite advertising and posting anti-littering messages, some of us don’t follow the rules.

Temporary food stalls and shops make it even more difficult to keep the beaches clean. Since people can’t ask the shopkeepers to relocate or prevent every single person from littering, the clean-up drive is needed.  This is an ideal example of problem-solving psychology in humans. ( 230-fifth.com ) So, what is problem-solving? Let’s find out.

What Is Problem-Solving?

At its simplest, the meaning of problem-solving is the process of defining a problem, determining its cause, and implementing a solution. The definition of problem-solving is rooted in the fact that as humans, we exert control over our environment through solutions. We move forward in life when we solve problems and make decisions. 

We can better define the problem-solving process through a series of important steps.

Identify The Problem: 

This step isn’t as simple as it sounds. Most times, we mistakenly identify the consequences of a problem rather than the problem itself. It’s important that we’re careful to identify the actual problem and not just its symptoms. 

Define The Problem: 

Once the problem has been identified correctly, you should define it. This step can help clarify what needs to be addressed and for what purpose.

Form A Strategy: 

Develop a strategy to solve your problem. Defining an approach will provide direction and clarity on the next steps. 

Organize The Information:  

Organizing information systematically will help you determine whether something is missing. The more information you have, the easier it’ll become for you to arrive at a solution.  

Allocate Resources:  

We may not always be armed with the necessary resources to solve a problem. Before you commit to implementing a solution for a problem, you should determine the availability of different resources—money, time and other costs.

Track Progress: 

The true meaning of problem-solving is to work towards an objective. If you measure your progress, you can evaluate whether you’re on track. You could revise your strategies if you don’t notice the desired level of progress. 

Evaluate The Results:  

After you spot a solution, evaluate the results to determine whether it’s the best possible solution. For example, you can evaluate the success of a fitness routine after several weeks of exercise.

Meaning Of Problem-Solving Skill

Now that we’ve established the definition of problem-solving psychology in humans, let’s look at how we utilize our problem-solving skills.  These skills help you determine the source of a problem and how to effectively determine the solution. Problem-solving skills aren’t innate and can be mastered over time. Here are some important skills that are beneficial for finding solutions.

Communication

Communication is a critical skill when you have to work in teams.  If you and your colleagues have to work on a project together, you’ll have to collaborate with each other. In case of differences of opinion, you should be able to listen attentively and respond respectfully in order to successfully arrive at a solution.

As a problem-solver, you need to be able to research and identify underlying causes. You should never treat a problem lightly. In-depth study is imperative because often people identify only the symptoms and not the actual problem.

Once you have researched and identified the factors causing a problem, start working towards developing solutions. Your analytical skills can help you differentiate between effective and ineffective solutions.

Decision-Making

You’ll have to make a decision after you’ve identified the source and methods of solving a problem. If you’ve done your research and applied your analytical skills effectively, it’ll become easier for you to take a call or a decision.

Organizations really value decisive problem-solvers. Harappa Education’s   Defining Problems course will guide you on the path to developing a problem-solving mindset. Learn how to identify the different types of problems using the Types of Problems framework. Additionally, the SMART framework, which is a five-point tool, will teach you to create specific and actionable objectives to address problem statements and arrive at solutions. 

Explore topics & skills such as Problem Solving Skills , PICK Chart , How to Solve Problems & Barriers to Problem Solving from our Harappa Diaries blog section and develop your skills.

Table of Contents

The problem-solving process, how to solve problems: 5 steps, train to solve problems with lean today, what is problem solving steps, techniques, & best practices explained.

Problem solving is the art of identifying problems and implementing the best possible solutions. Revisiting your problem-solving skills may be the missing piece to leveraging the performance of your business, achieving Lean success, or unlocking your professional potential. 

Ask any colleague if they’re an effective problem-solver and their likely answer will be, “Of course! I solve problems every day.” 

Problem solving is part of most job descriptions, sure. But not everyone can do it consistently. 

Problem solving is the process of defining a problem, identifying its root cause, prioritizing and selecting potential solutions, and implementing the chosen solution.

There’s no one-size-fits-all problem-solving process. Often, it’s a unique methodology that aligns your short- and long-term objectives with the resources at your disposal. Nonetheless, many paradigms center problem solving as a pathway for achieving one’s goals faster and smarter. 

One example is the Six Sigma framework , which emphasizes eliminating errors and refining the customer experience, thereby improving business outcomes. Developed originally by Motorola, the Six Sigma process identifies problems from the perspective of customer satisfaction and improving product delivery. 

Lean management, a similar method, is about streamlining company processes over time so they become “leaner” while producing better outcomes. 

Trendy business management lingo aside, both of these frameworks teach us that investing in your problem solving process for personal and professional arenas will bring better productivity.

1. Precisely Identify Problems

As obvious as it seems, identifying the problem is the first step in the problem-solving process. Pinpointing a problem at the beginning of the process will guide your research, collaboration, and solutions in the right direction. 

At this stage, your task is to identify the scope and substance of the problem. Ask yourself a series of questions: 

• What’s the problem? 
• How many subsets of issues are underneath this problem? 
• What subject areas, departments of work, or functions of business can best define this problem? 

Although some problems are naturally large in scope, precision is key. Write out the problems as statements in planning sheets . Should information or feedback during a later step alter the scope of your problem, revise the statements. 

Framing the problem at this stage will help you stay focused if distractions come up in later stages. Furthermore, how you frame a problem will aid your search for a solution. A strategy of building Lean success, for instance, will emphasize identifying and improving upon inefficient systems. 

2. Collect Information and Plan 

The second step is to collect information and plan the brainstorming process. This is another foundational step to road mapping your problem-solving process. Data, after all, is useful in identifying the scope and substance of your problems. 

Collecting information on the exact details of the problem, however, is done to narrow the brainstorming portion to help you evaluate the outcomes later. Don’t overwhelm yourself with unnecessary information — use the problem statements that you identified in step one as a north star in your research process. 

This stage should also include some planning. Ask yourself:

• What parties will ultimately decide a solution? 
• Whose voices and ideas should be heard in the brainstorming process? 
• What resources are at your disposal for implementing a solution? 

Establish a plan and timeline for steps 3-5. 

3. Brainstorm Solutions

Brainstorming solutions is the bread and butter of the problem-solving process. At this stage, focus on generating creative ideas. As long as the solution directly addresses the problem statements and achieves your goals, don’t immediately rule it out. 

Moreover, solutions are rarely a one-step answer and are more like a roadmap with a set of actions. As you brainstorm ideas, map out these solutions visually and include any relevant factors such as costs involved, action steps, and involved parties. 

With Lean success in mind, stay focused on solutions that minimize waste and improve the flow of business ecosystems. 

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4. Decide and Implement

The most critical stage is selecting a solution. Easier said than done. Consider the criteria that has arisen in previous steps as you decide on a solution that meets your needs. 

Once you select a course of action, implement it. 

Practicing due diligence in earlier stages of the process will ensure that your chosen course of action has been evaluated from all angles. Often, efficient implementation requires us to act correctly and successfully the first time, rather than being hurried and sloppy. Further compilations will create more problems, bringing you back to step 1. 

5. Evaluate

Exercise humility and evaluate your solution honestly. Did you achieve the results you hoped for? What would you do differently next time? 

As some experts note, formulating feedback channels into your evaluation helps solidify future success. A framework like Lean success, for example, will use certain key performance indicators (KPIs) like quality, delivery success, reducing errors, and more. Establish metrics aligned with company goals to assess your solutions.

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What Is Problem Solving? How Software Engineers Approach Complex Challenges

From debugging an existing system to designing an entirely new software application, a day in the life of a software engineer is filled with various challenges and complexities. The one skill that glues these disparate tasks together and makes them manageable? Problem solving . 

Throughout this blog post, we’ll explore why problem-solving skills are so critical for software engineers, delve into the techniques they use to address complex challenges, and discuss how hiring managers can identify these skills during the hiring process. 

What Is Problem Solving?

But what exactly is problem solving in the context of software engineering? How does it work, and why is it so important?

Problem solving, in the simplest terms, is the process of identifying a problem, analyzing it, and finding the most effective solution to overcome it. For software engineers, this process is deeply embedded in their daily workflow. It could be something as simple as figuring out why a piece of code isn’t working as expected, or something as complex as designing the architecture for a new software system. 

In a world where technology is evolving at a blistering pace, the complexity and volume of problems that software engineers face are also growing. As such, the ability to tackle these issues head-on and find innovative solutions is not only a handy skill — it’s a necessity. 

The Importance of Problem-Solving Skills for Software Engineers

Problem-solving isn’t just another ability that software engineers pull out of their toolkits when they encounter a bug or a system failure. It’s a constant, ongoing process that’s intrinsic to every aspect of their work. Let’s break down why this skill is so critical.

Driving Development Forward

Without problem solving, software development would hit a standstill. Every new feature, every optimization, and every bug fix is a problem that needs solving. Whether it’s a performance issue that needs diagnosing or a user interface that needs improving, the capacity to tackle and solve these problems is what keeps the wheels of development turning.

It’s estimated that 60% of software development lifecycle costs are related to maintenance tasks, including debugging and problem solving. This highlights how pivotal this skill is to the everyday functioning and advancement of software systems.

Innovation and Optimization

The importance of problem solving isn’t confined to reactive scenarios; it also plays a major role in proactive, innovative initiatives . Software engineers often need to think outside the box to come up with creative solutions, whether it’s optimizing an algorithm to run faster or designing a new feature to meet customer needs. These are all forms of problem solving.

Consider the development of the modern smartphone. It wasn’t born out of a pre-existing issue but was a solution to a problem people didn’t realize they had — a device that combined communication, entertainment, and productivity into one handheld tool.

Increasing Efficiency and Productivity

Good problem-solving skills can save a lot of time and resources. Effective problem-solvers are adept at dissecting an issue to understand its root cause, thus reducing the time spent on trial and error. This efficiency means projects move faster, releases happen sooner, and businesses stay ahead of their competition.

Improving Software Quality

Problem solving also plays a significant role in enhancing the quality of the end product. By tackling the root causes of bugs and system failures, software engineers can deliver reliable, high-performing software. This is critical because, according to the Consortium for Information and Software Quality, poor quality software in the U.S. in 2022 cost at least $2.41 trillion in operational issues, wasted developer time, and other related problems.

Problem-Solving Techniques in Software Engineering

So how do software engineers go about tackling these complex challenges? Let’s explore some of the key problem-solving techniques, theories, and processes they commonly use.

Decomposition

Breaking down a problem into smaller, manageable parts is one of the first steps in the problem-solving process. It’s like dealing with a complicated puzzle. You don’t try to solve it all at once. Instead, you separate the pieces, group them based on similarities, and then start working on the smaller sets. This method allows software engineers to handle complex issues without being overwhelmed and makes it easier to identify where things might be going wrong.

Abstraction

In the realm of software engineering, abstraction means focusing on the necessary information only and ignoring irrelevant details. It is a way of simplifying complex systems to make them easier to understand and manage. For instance, a software engineer might ignore the details of how a database works to focus on the information it holds and how to retrieve or modify that information.

Algorithmic Thinking

At its core, software engineering is about creating algorithms — step-by-step procedures to solve a problem or accomplish a goal. Algorithmic thinking involves conceiving and expressing these procedures clearly and accurately and viewing every problem through an algorithmic lens. A well-designed algorithm not only solves the problem at hand but also does so efficiently, saving computational resources.

Parallel Thinking

Parallel thinking is a structured process where team members think in the same direction at the same time, allowing for more organized discussion and collaboration. It’s an approach popularized by Edward de Bono with the “ Six Thinking Hats ” technique, where each “hat” represents a different style of thinking.

In the context of software engineering, parallel thinking can be highly effective for problem solving. For instance, when dealing with a complex issue, the team can use the “White Hat” to focus solely on the data and facts about the problem, then the “Black Hat” to consider potential problems with a proposed solution, and so on. This structured approach can lead to more comprehensive analysis and more effective solutions, and it ensures that everyone’s perspectives are considered.

This is the process of identifying and fixing errors in code . Debugging involves carefully reviewing the code, reproducing and analyzing the error, and then making necessary modifications to rectify the problem. It’s a key part of maintaining and improving software quality.

Testing and Validation

Testing is an essential part of problem solving in software engineering. Engineers use a variety of tests to verify that their code works as expected and to uncover any potential issues. These range from unit tests that check individual components of the code to integration tests that ensure the pieces work well together. Validation, on the other hand, ensures that the solution not only works but also fulfills the intended requirements and objectives.

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Evaluating Problem-Solving Skills

We’ve examined the importance of problem-solving in the work of a software engineer and explored various techniques software engineers employ to approach complex challenges. Now, let’s delve into how hiring teams can identify and evaluate problem-solving skills during the hiring process.

Recognizing Problem-Solving Skills in Candidates

How can you tell if a candidate is a good problem solver? Look for these indicators:

• Previous Experience: A history of dealing with complex, challenging projects is often a good sign. Ask the candidate to discuss a difficult problem they faced in a previous role and how they solved it.
• Problem-Solving Questions: During interviews, pose hypothetical scenarios or present real problems your company has faced. Ask candidates to explain how they would tackle these issues. You’re not just looking for a correct solution but the thought process that led them there.
• Technical Tests: Coding challenges and other technical tests can provide insight into a candidate’s problem-solving abilities. Consider leveraging a platform for assessing these skills in a realistic, job-related context.

Assessing Problem-Solving Skills

Once you’ve identified potential problem solvers, here are a few ways you can assess their skills:

• Solution Effectiveness: Did the candidate solve the problem? How efficient and effective is their solution?
• Approach and Process: Go beyond whether or not they solved the problem and examine how they arrived at their solution. Did they break the problem down into manageable parts? Did they consider different perspectives and possibilities?
• Communication: A good problem solver can explain their thought process clearly. Can the candidate effectively communicate how they arrived at their solution and why they chose it?
• Adaptability: Problem-solving often involves a degree of trial and error. How does the candidate handle roadblocks? Do they adapt their approach based on new information or feedback?

Hiring managers play a crucial role in identifying and fostering problem-solving skills within their teams. By focusing on these abilities during the hiring process, companies can build teams that are more capable, innovative, and resilient.

Key Takeaways

As you can see, problem solving plays a pivotal role in software engineering. Far from being an occasional requirement, it is the lifeblood that drives development forward, catalyzes innovation, and delivers of quality software. 

By leveraging problem-solving techniques, software engineers employ a powerful suite of strategies to overcome complex challenges. But mastering these techniques isn’t simple feat. It requires a learning mindset, regular practice, collaboration, reflective thinking, resilience, and a commitment to staying updated with industry trends. 

For hiring managers and team leads, recognizing these skills and fostering a culture that values and nurtures problem solving is key. It’s this emphasis on problem solving that can differentiate an average team from a high-performing one and an ordinary product from an industry-leading one.

At the end of the day, software engineering is fundamentally about solving problems — problems that matter to businesses, to users, and to the wider society. And it’s the proficient problem solvers who stand at the forefront of this dynamic field, turning challenges into opportunities, and ideas into reality.

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Encyclopedia of the Sciences of Learning pp 2680–2683 Cite as

Problem Solving

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Cognition ; Problem typology ; Problem-based learning ; Problems ; Reasoning

Problem solving is the process of constructing and applying mental representations of problems to finding solutions to those problems that are encountered in nearly every context.

Theoretical Background

Problem solving is the process of articulating solutions to problems. Problems have two critical attributes. First, a problem is an unknown in some context. That is, there is a situation in which there is something that is unknown (the difference between a goal state and a current state). Those situations vary from algorithmic math problems to vexing and complex social problems, such as violence in society (see Problem Typology ). Second, finding or solving for the unknown must have some social, cultural, or intellectual value. That is, someone believes that it is worth finding the unknown. If no one perceives an unknown or a need to determine an unknown, there is no perceived problem. Finding...

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Jonassen, D.H., Hung, W. (2012). Problem Solving. In: Seel, N.M. (eds) Encyclopedia of the Sciences of Learning. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-1428-6_208

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What Is Problem-Solving Therapy?

Arlin Cuncic, MA, is the author of "Therapy in Focus: What to Expect from CBT for Social Anxiety Disorder" and "7 Weeks to Reduce Anxiety." She has a Master's degree in psychology.

Daniel B. Block, MD, is an award-winning, board-certified psychiatrist who operates a private practice in Pennsylvania.

Verywell / Madelyn Goodnight

Problem-Solving Therapy Techniques

How effective is problem-solving therapy, things to consider, how to get started.

Problem-solving therapy is a brief intervention that provides people with the tools they need to identify and solve problems that arise from big and small life stressors. It aims to improve your overall quality of life and reduce the negative impact of psychological and physical illness.

Problem-solving therapy can be used to treat depression , among other conditions. It can be administered by a doctor or mental health professional and may be combined with other treatment approaches.

At a Glance

Problem-solving therapy is a short-term treatment used to help people who are experiencing depression, stress, PTSD, self-harm, suicidal ideation, and other mental health problems develop the tools they need to deal with challenges. This approach teaches people to identify problems, generate solutions, and implement those solutions. Let's take a closer look at how problem-solving therapy can help people be more resilient and adaptive in the face of stress.

Problem-solving therapy is based on a model that takes into account the importance of real-life problem-solving. In other words, the key to managing the impact of stressful life events is to know how to address issues as they arise. Problem-solving therapy is very practical in its approach and is only concerned with the present, rather than delving into your past.

This form of therapy can take place one-on-one or in a group format and may be offered in person or online via telehealth . Sessions can be anywhere from 30 minutes to two hours long. 

Key Components

There are two major components that make up the problem-solving therapy framework:

• Applying a positive problem-solving orientation to your life
• Using problem-solving skills

A positive problem-solving orientation means viewing things in an optimistic light, embracing self-efficacy , and accepting the idea that problems are a normal part of life. Problem-solving skills are behaviors that you can rely on to help you navigate conflict, even during times of stress. This includes skills like:

• Knowing how to identify a problem
• Defining the problem in a helpful way
• Trying to understand the problem more deeply
• Setting goals related to the problem
• Generating alternative, creative solutions to the problem
• Choosing the best course of action
• Implementing the choice you have made
• Evaluating the outcome to determine next steps

Problem-solving therapy is all about training you to become adaptive in your life so that you will start to see problems as challenges to be solved instead of insurmountable obstacles. It also means that you will recognize the action that is required to engage in effective problem-solving techniques.

Planful Problem-Solving

One problem-solving technique, called planful problem-solving, involves following a series of steps to fix issues in a healthy, constructive way:

• Problem definition and formulation : This step involves identifying the real-life problem that needs to be solved and formulating it in a way that allows you to generate potential solutions.
• Generation of alternative solutions : This stage involves coming up with various potential solutions to the problem at hand. The goal in this step is to brainstorm options to creatively address the life stressor in ways that you may not have previously considered.
• Decision-making strategies : This stage involves discussing different strategies for making decisions as well as identifying obstacles that may get in the way of solving the problem at hand.
• Solution implementation and verification : This stage involves implementing a chosen solution and then verifying whether it was effective in addressing the problem.

Other Techniques

Other techniques your therapist may go over include:

• Problem-solving multitasking , which helps you learn to think clearly and solve problems effectively even during times of stress
• Stop, slow down, think, and act (SSTA) , which is meant to encourage you to become more emotionally mindful when faced with conflict
• Healthy thinking and imagery , which teaches you how to embrace more positive self-talk while problem-solving

What Problem-Solving Therapy Can Help With

Problem-solving therapy addresses life stress issues and focuses on helping you find solutions to concrete issues. This approach can be applied to problems associated with various psychological and physiological symptoms.

Mental Health Issues

Problem-solving therapy may help address mental health issues, like:

• Chronic stress due to accumulating minor issues
• Complications associated with traumatic brain injury (TBI)
• Emotional distress
• Post-traumatic stress disorder (PTSD)
• Problems associated with a chronic disease like cancer, heart disease, or diabetes
• Self-harm and feelings of hopelessness
• Substance use
• Suicidal ideation

Specific Life Challenges

This form of therapy is also helpful for dealing with specific life problems, such as:

• Death of a loved one
• Dissatisfaction at work
• Everyday life stressors
• Family problems
• Financial difficulties
• Relationship conflicts

Your doctor or mental healthcare professional will be able to advise whether problem-solving therapy could be helpful for your particular issue. In general, if you are struggling with specific, concrete problems that you are having trouble finding solutions for, problem-solving therapy could be helpful for you.

Benefits of Problem-Solving Therapy

The skills learned in problem-solving therapy can be helpful for managing all areas of your life. These can include:

• Being able to identify which stressors trigger your negative emotions (e.g., sadness, anger)
• Confidence that you can handle problems that you face
• Having a systematic approach on how to deal with life's problems
• Having a toolbox of strategies to solve the issues you face
• Increased confidence to find creative solutions
• Knowing how to identify which barriers will impede your progress
• Knowing how to manage emotions when they arise
• Reduced avoidance and increased action-taking
• The ability to accept life problems that can't be solved
• The ability to make effective decisions
• The development of patience (realizing that not all problems have a "quick fix")

Problem-solving therapy can help people feel more empowered to deal with the problems they face in their lives. Rather than feeling overwhelmed when stressors begin to take a toll, this therapy introduces new coping skills that can boost self-efficacy and resilience .

Other Types of Therapy

Other similar types of therapy include cognitive-behavioral therapy (CBT) and solution-focused brief therapy (SFBT) . While these therapies work to change thinking and behaviors, they work a bit differently. Both CBT and SFBT are less structured than problem-solving therapy and may focus on broader issues. CBT focuses on identifying and changing maladaptive thoughts, and SFBT works to help people look for solutions and build self-efficacy based on strengths.

This form of therapy was initially developed to help people combat stress through effective problem-solving, and it was later adapted to address clinical depression specifically. Today, much of the research on problem-solving therapy deals with its effectiveness in treating depression.

Problem-solving therapy has been shown to help depression in: 

• Older adults
• People coping with serious illnesses like cancer

Problem-solving therapy also appears to be effective as a brief treatment for depression, offering benefits in as little as six to eight sessions with a therapist or another healthcare professional. This may make it a good option for someone unable to commit to a lengthier treatment for depression.

Problem-solving therapy is not a good fit for everyone. It may not be effective at addressing issues that don't have clear solutions, like seeking meaning or purpose in life. Problem-solving therapy is also intended to treat specific problems, not general habits or thought patterns .

In general, it's also important to remember that problem-solving therapy is not a primary treatment for mental disorders. If you are living with the symptoms of a serious mental illness such as bipolar disorder or schizophrenia , you may need additional treatment with evidence-based approaches for your particular concern.

Problem-solving therapy is best aimed at someone who has a mental or physical issue that is being treated separately, but who also has life issues that go along with that problem that has yet to be addressed.

For example, it could help if you can't clean your house or pay your bills because of your depression, or if a cancer diagnosis is interfering with your quality of life.

Your doctor may be able to recommend therapists in your area who utilize this approach, or they may offer it themselves as part of their practice. You can also search for a problem-solving therapist with help from the American Psychological Association’s (APA) Society of Clinical Psychology .

If receiving problem-solving therapy from a doctor or mental healthcare professional is not an option for you, you could also consider implementing it as a self-help strategy using a workbook designed to help you learn problem-solving skills on your own.

During your first session, your therapist may spend some time explaining their process and approach. They may ask you to identify the problem you’re currently facing, and they’ll likely discuss your goals for therapy .

Keep In Mind

Problem-solving therapy may be a short-term intervention that's focused on solving a specific issue in your life. If you need further help with something more pervasive, it can also become a longer-term treatment option.

Get Help Now

We've tried, tested, and written unbiased reviews of the best online therapy programs including Talkspace, BetterHelp, and ReGain. Find out which option is the best for you.

Shang P, Cao X, You S, Feng X, Li N, Jia Y. Problem-solving therapy for major depressive disorders in older adults: an updated systematic review and meta-analysis of randomized controlled trials .  Aging Clin Exp Res . 2021;33(6):1465-1475. doi:10.1007/s40520-020-01672-3

Cuijpers P, Wit L de, Kleiboer A, Karyotaki E, Ebert DD. Problem-solving therapy for adult depression: An updated meta-analysis . Eur Psychiatry . 2018;48(1):27-37. doi:10.1016/j.eurpsy.2017.11.006

Nezu AM, Nezu CM, D'Zurilla TJ. Problem-Solving Therapy: A Treatment Manual . New York; 2013. doi:10.1891/9780826109415.0001

Owens D, Wright-Hughes A, Graham L, et al. Problem-solving therapy rather than treatment as usual for adults after self-harm: a pragmatic, feasibility, randomised controlled trial (the MIDSHIPS trial) .  Pilot Feasibility Stud . 2020;6:119. doi:10.1186/s40814-020-00668-0

Sorsdahl K, Stein DJ, Corrigall J, et al. The efficacy of a blended motivational interviewing and problem solving therapy intervention to reduce substance use among patients presenting for emergency services in South Africa: A randomized controlled trial . Subst Abuse Treat Prev Policy . 2015;10(1):46. doi:doi.org/10.1186/s13011-015-0042-1

Margolis SA, Osborne P, Gonzalez JS. Problem solving . In: Gellman MD, ed. Encyclopedia of Behavioral Medicine . Springer International Publishing; 2020:1745-1747. doi:10.1007/978-3-030-39903-0_208

Kirkham JG, Choi N, Seitz DP. Meta-analysis of problem solving therapy for the treatment of major depressive disorder in older adults . Int J Geriatr Psychiatry . 2016;31(5):526-535. doi:10.1002/gps.4358

Garand L, Rinaldo DE, Alberth MM, et al. Effects of problem solving therapy on mental health outcomes in family caregivers of persons with a new diagnosis of mild cognitive impairment or early dementia: A randomized controlled trial . Am J Geriatr Psychiatry . 2014;22(8):771-781. doi:10.1016/j.jagp.2013.07.007

Noyes K, Zapf AL, Depner RM, et al. Problem-solving skills training in adult cancer survivors: Bright IDEAS-AC pilot study .  Cancer Treat Res Commun . 2022;31:100552. doi:10.1016/j.ctarc.2022.100552

Albert SM, King J, Anderson S, et al. Depression agency-based collaborative: effect of problem-solving therapy on risk of common mental disorders in older adults with home care needs . The American Journal of Geriatric Psychiatry . 2019;27(6):619-624. doi:10.1016/j.jagp.2019.01.002

By Arlin Cuncic, MA Arlin Cuncic, MA, is the author of "Therapy in Focus: What to Expect from CBT for Social Anxiety Disorder" and "7 Weeks to Reduce Anxiety." She has a Master's degree in psychology.

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Definition of problem

 (Entry 1 of 2)

Definition of problem  (Entry 2 of 2)

mystery , problem , enigma , riddle , puzzle mean something which baffles or perplexes.

mystery applies to what cannot be fully understood by reason or less strictly to whatever resists or defies explanation.

problem applies to a question or difficulty calling for a solution or causing concern.

enigma applies to utterance or behavior that is very difficult to interpret.

riddle suggests an enigma or problem involving paradox or apparent contradiction.

puzzle applies to an enigma or problem that challenges ingenuity for its solution.

Examples of problem in a Sentence

These examples are programmatically compiled from various online sources to illustrate current usage of the word 'problem.' Any opinions expressed in the examples do not represent those of Merriam-Webster or its editors. Send us feedback about these examples.

Word History

Middle English probleme , from Latin problema , from Greek problēma , literally, obstacle, from proballein to throw forward, from pro- forward + ballein to throw — more at pro- , devil

14th century, in the meaning defined at sense 1a

1894, in the meaning defined at sense 1

Phrases Containing problem

• attitude problem
• drink problem
• first world problem
• drinking problem
• word problem
• what's someone's problem
• therein lies the problem
• not a problem
• problem - solving
• not someone's problem
• have a problem with

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The 3-body problem is real, and it’s really unsolvable

Oh god don’t make me explain math

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Everybody seems to be talking about 3 Body Problem , the new Netflix series based on Cixin Liu’s Remembrance of Earth’s Past book trilogy . Fewer people are talking about the two series’ namesake: The unsolvable physics problem of the same name.

This makes sense, because it’s confusing . In physics, the three-body problem attempts to find a way to predict the movements of three objects whose gravity interacts with each of the others — like three stars that are close together in space. Sounds simple enough, right? Yet I myself recently pulled up the Wikipedia article on the three-body problem and closed the tab in the same manner that a person might stagger away from a bright light. Apparently the Earth, sun, and moon are a three-body system? Are you telling me we don’t know how the moon moves ? Scientists have published multiple solutions for the three-body problem? Are you telling me Cixin Liu’s books are out of date?

All I’d wanted to know was why the problem was considered unsolvable, and now memories of my one semester of high school physics were swimming before my eyes like so many glowing doom numbers. However, despite my pains, I have readied several ways that we non-physicists can be confident that the three-body problem is, in fact, unsolvable.

Reason 1: This is a special definition of ‘unsolvable’

The three-body problem is extra confusing, because scientists are seemingly constantly finding new solutions to the three-body problem! They just don’t mean a one-solution-for-all solution. Such a formula does exist for a two-body system, and apparently Isaac Newton figured it out in 1687 . But systems with more than two bodies are, according to physicists, too chaotic (i.e., not in the sense of a child’s messy bedroom, but in the sense of “chaos theory”) to be corralled by a single solution.

When physicists say they have a new solution to the three-body problem, they mean that they’ve found a specific solution for three-body systems that have certain theoretical parameters. Don’t ask me to explain those parameters, because they’re all things like “the three masses are collinear at each instant” or “a zero angular momentum solution with three equal masses moving around a figure-eight shape.” But basically: By narrowing the focus of the problem to certain arrangements of three-body systems, physicists have been able to derive formulas that predict the movements of some of them, like in our solar system. The mass of the Earth and the sun create a “ restricted three-body problem ,” where a less-big body (in this case, the moon) moves under the influence of two massive ones (the Earth and the sun).

What physicists mean when they say the three-body problem has no solution is simply that there isn’t a one-formula-fits-all solution to every way that the gravity of three objects might cause those objects to move — which is exactly what Three-Body Problem bases its whole premise on.

Reason 2: 3 Body Problem picked an unsolved three-body system on purpose

Henri Poincaré’s research into a general solution to the three-body problem formed the basis of what would become known as chaos theory (you might know it from its co-starring role in Jurassic Park ). And 3 Body Problem itself isn’t about any old three-body system. It’s specifically about an extremely chaotic three-body system, the exact kind of arrangement of bodies that Poincaré was focused on when he showed that the problem is “unsolvable.”

[ Ed. note: The rest of this section includes some spoilers for 3 Body Problem .]

In both Liu’s books and Netflix’s 3 Body Problem , humanity faces an invasion by aliens (called Trisolarans in the English translation of the books, and San-Ti in the TV series) whose home solar system features three suns in a chaotic three-body relationship. It is a world where, unlike ours, the heavens are fundamentally unpredictable. Periods of icy cold give way to searing heat that give way to swings in gravity that turn into temporary reprieves that can never be trusted. The unpredictable nature of the San-Ti environment is the source of every detail of their physicality, their philosophy, and their desire to claim Earth for their own.

In other words, 3 Body Problem ’s three-body problem is unsolvable because Liu wanted to write a story with an unsolvable three-body system, so he chose one of the three-body systems for which we have not discovered a solution, and might never.

Reason 3: Scientists are still working on the three-body problem

Perhaps the best reason I can give you to believe that the three-body problem is real, and is really unsolvable, is that some scientists published a whole set of new solutions for specific three-body systems very recently .

If physicists are still working on the three-body problem, we can safely assume that it has not been solved. Scientists, after all, are the real experts. And I am definitely not.

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Netflix's hit sci-fi series '3 Body Problem' is based on a real math problem that is so complex it's impossible to solve

• The three-body problem is a centuries-old physics question that puzzled Isaac Newton .
• It describes the orbits of three bodies, like planets or stars, trapped in each other's gravity.
• The problem is unsolvable and led to the development of chaos theory.

While Netflix's "3 Body Problem" is a science-fiction show, its name comes from a real math problem that's puzzled scientists since the late 1600s.

In physics, the three-body problem refers to the motion of three bodies trapped in each other's gravitational grip — like a three-star system.

It might sound simple enough, but once you dig into the mathematics, the orbital paths of each object get complicated very quickly.

Two-body vs. three- and multi-body systems

A simpler version is a two-body system like binary stars. Two-body systems have periodic orbits, meaning they are mathematically predictable because they follow the same trajectory over and over. So, if you have the stars' initial positions and velocities, you can calculate where they've been or will be in space far into the past and future.

However, "throwing in a third body that's close enough to interact leads to chaos," Shane Ross, an aerospace and ocean engineering professor at Virginia Tech, told Business Insider. In fact, it's nearly impossible to precisely predict the orbital paths of any system with three bodies or more.

While two orbiting planets might look like a ven diagram with ovular paths overlapping, the paths of three bodies interacting often resemble tangled spaghetti. Their trajectories usually aren't as stable as systems with only two bodies.

All that uncertainty makes what's known as the three-body problem largely unsolvable, Ross said. But there are certain exceptions.

The three-body problem is over 300 years old

The three-body problem dates back to Isaac Newton , who published his "Principia" in 1687.

In the book, the mathematician noted that the planets move in elliptical orbits around the sun. Yet the gravitational pull from Jupiter seemed to affect Saturn's orbital path.

Related stories

The three-body problem didn't just affect distant planets. Trying to understand the variations in the moon's movements caused Newton literal headaches, he complained.

But Newton never fully figured out the three-body problem. And it remained a mathematical mystery for nearly 200 years.

In 1889, a Swedish journal awarded mathematician Henri Poincaré a gold medal and 2,500 Swedish crowns, roughly half a year's salary for a professor at the time, for his essay about the three-body problem that outlined the basis for an entirely new mathematical theory called chaos theory .

According to chaos theory, when there is uncertainty about a system's initial conditions, like an object's mass or velocity, that uncertainty ripples out, making the future more and more unpredictable.

Think of it like taking a wrong turn on a trip. If you make a left instead of a right at the end of your journey, you're probably closer to your destination than if you made the mistake at the very beginning.

Can you solve the three-body problem?

Cracking the three-body problem would help scientists chart the movements of meteors and planets, including Earth, into the extremely far future. Even comparatively small movements of our planet could have large impacts on our climate, Ross said.

Though the three-body problem is considered mathematically unsolvable, there are solutions to specific scenarios. In fact, there are a few that mathematicians have found.

For example, three bodies could stably orbit in a figure eight or equally spaced around a ring. Both are possible depending on the initial positions and velocities of the bodies.

One way researchers look for solutions is with " restricted " three-body problems, where two main bodies (like the sun and Earth) interact and a third object with much smaller mass (like the moon) offers less gravitational interference. In this case, the three-body problem looks a lot like a two-body problem since the sun and Earth comprise the majority of mass in the system.

However, if you're looking at a three-star system, like the one in Netflix's show "3 Body Problem," that's a lot more complicated.

Computers can also run simulations far more efficiently than humans, though due to the inherent uncertainties, the results are typically approximate orbits instead of exact.

Finding solutions to three-body problems is also essential to space travel, Ross said. For his work, he inputs data about the Earth, moon, and spacecraft into a computer. "We can build up a whole library of possible trajectories," he said, "and that gives us an idea of the types of motion that are possible."

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The tech industry can’t agree on what open-source AI means. That’s a problem.

The answer could determine who gets to shape the future of the technology.

• Edd Gent archive page

Suddenly, “open source” is the latest buzzword in AI circles. Meta has pledged to create open-source artificial general intelligence. And Elon Musk is suing OpenAI over its lack of open-source AI models.

Meanwhile, a growing number of tech leaders and companies are setting themselves up as open-source champions. 

But there’s a fundamental problem—no one can agree on what “open-source AI” means. 

On the face of it, open-source AI promises a future where anyone can take part in the technology’s development. That could accelerate innovation, boost transparency, and give users greater control over systems that could soon reshape many aspects of our lives. But what even is it? What makes an AI model open source, and what disqualifies it?

The answers could have significant ramifications for the future of the technology. Until the tech industry has settled on a definition, powerful companies can easily bend the concept to suit their own needs, and it could become a tool to entrench the dominance of today’s leading players.

Entering this fray is the Open Source Initiative (OSI), the self-appointed arbiters of what it means to be open source. Founded in 1998, the nonprofit is the custodian of the Open Source Definition, a widely accepted set of rules that determine whether a piece of software can be considered open source. 

Now, the organization has assembled a 70-strong group of researchers, lawyers, policymakers, activists, and representatives from big tech companies like Meta, Google, and Amazon to come up with a working definition of open-source AI. 

The open-source community is a big tent, though, encompassing everything from hacktivists to Fortune 500 companies. While there’s broad agreement on the overarching principles, says Stefano Maffulli, OSI’s executive director, it’s becoming increasingly obvious that the devil is in the details. With so many competing interests to consider, finding a solution that satisfies everyone while ensuring that the biggest companies play along is no easy task.

Fuzzy criteria

The lack of a settled definition has done little to prevent tech companies from adopting the term.

Last July, Meta made its Llama 2 model, which it referred to as open source, freely available, and it has a track record of publicly releasing AI technologies. “We support the OSI’s effort to define open-source AI and look forward to continuing to participate in their process for the benefit of the open source community across the world,” Jonathan Torres, Meta’s associate general counsel for AI, open source, and licensing told us. 

That stands in marked contrast to rival OpenAI, which has shared progressively fewer details about its leading models over the years, citing safety concerns. “We only open-source powerful AI models once we have carefully weighed the benefits and risks, including misuse and acceleration,” a spokesperson said. 

Other leading AI companies, like Stability AI and Aleph Alpha, have also released models described as open source, and Hugging Face hosts a large library of freely available AI models.

While Google has taken a more locked-down approach with its most powerful models, like Gemini and PaLM 2, the Gemma models released last month are freely accessible and designed to go toe-to-toe with Llama 2, though the company described them as “open” rather than “open source.”  

But there’s considerable disagreement about whether any of these models can really be described as open source. For a start, both Llama 2 and Gemma come with licenses that restrict what users can do with the models. That’s anathema to open-source principles: one of the key clauses of the Open Source Definition outlaws the imposition of any restrictions based on use cases.

The criteria are fuzzy even for models that don’t come with these kinds of conditions. The concept of open source was devised to ensure developers could use, study, modify, and share software without restrictions. But AI works in fundamentally different ways, and key concepts don’t translate from software to AI neatly, says Maffulli.

One of the biggest hurdles is the sheer number of ingredients that go into today’s AI models. All you need to tinker with a piece of software is the underlying source code, says Maffulli. But depending on your goal, dabbling with an AI model could require access to the trained model, its training data, the code used to preprocess this data, the code governing the training process, the underlying architecture of the model, or a host of other, more subtle details.

Which ingredients you need to meaningfully study and modify models remains open to interpretation. “We have identified what basic freedoms or basic rights we want to be able to exercise,” says Maffulli. “The mechanics of how to exercise those rights are not clear.”

Settling this debate will be essential if the AI community wants to reap the same benefits software developers gained from open source, says Maffulli, which was built on broad consensus about what the term meant. “Having [a definition] that is respected and adopted by a large chunk of the industry provides clarity,” he says. “And with clarity comes lower costs for compliance, less friction, shared understanding.”

By far the biggest sticking point is data. All the major AI companies have simply released pretrained models, without the data sets on which they were trained. For people pushing for a stricter definition of open-source AI, Maffulli says, this seriously constrains efforts to modify and study models, automatically disqualifying them as open source.

Others have argued that a simple description of the data is often enough to probe a model, says Maffulli, and you don’t necessarily need to retrain from scratch to make modifications. Pretrained models are routinely adapted through a process known as fine-tuning, in which they are partially retrained on a smaller, often application-specific, dataset.

Meta’s Llama 2 is a case in point, says Roman Shaposhnik , CEO of open-source AI company Ainekko and vice president of legal affairs for the Apache Software Foundation, who is involved in the OSI process. While Meta only released a pretrained model, a flourishing community of developers has been downloading and adapting it, and sharing their modifications.

“People are using it in all sorts of projects. There’s a whole ecosystem around it,” he says. “We therefore must call it something. Is it half-open? Is it ajar?”

While it may be technically possible to modify a model without its original training data, restricting access to a key ingredient is not really in the spirit of open source, says Zuzanna Warso, director of research at nonprofit Open Future, who is taking part in the OSI’s discussions. It’s also debatable whether it’s possible to truly exercise the freedom to study a model without knowing what information it was trained on.

“It’s a crucial component of this whole process,” she says. “If we care about openness, we should also care about the openness of the data.”

Have your cake and eat it

It’s important to understand why companies setting themselves up as open-source champions are reluctant to hand over training data. Access to high-quality training data is a major bottleneck for AI research and a competitive advantage for bigger firms that they’re eager to maintain, says Warso.

At the same time, open source carries a host of benefits that these companies would like to see translated to AI. At a superficial level, the term “open source” carries positive connotations for a lot of people, so engaging in so-called “open washing” can be an easy PR win, says Warso.

It can also have a significant impact on their bottom line. Economists at Harvard Business School recently found that open-source software has saved companies almost $9 trillion in development costs by allowing them to build their products on top of high-quality free software rather than writing it themselves.

For larger companies, open-sourcing their software so that it can be reused and modified by other developers can help build a powerful ecosystem around their products, says Warso. The classic example is Google’s open-sourcing of its Android mobile operating system , which cemented its dominant position at the heart of the smartphone revolution. Meta’s Mark Zuckerberg has been explicit about this motivation in earnings calls , saying “open-source software often becomes an industry standard, and when companies standardize on building with our stack, that then becomes easier to integrate new innovations into our products.”

Crucially, it also appears that open-source AI may receive favorable regulatory treatment in some places, Warso says, pointing to the EU’s newly passed AI Act, which exempts certain open-source projects from some of its more stringent requirements.

Taken together, it’s clear why sharing pretrained models but restricting access to the data required to build them makes good business sense, says Warso. But it does smack of companies trying to have their cake and eat it too, she adds. And if the strategy helps entrench the already dominant positions of large tech companies, it’s hard to see how that fits with the underlying ethos of open source.

“We see openness as one of the tools to challenge the concentration of power,” says Warso. “If the definition is supposed to help in challenging these concentrations of power, then the question of data becomes even more important.”

Shaposhnik thinks a compromise is possible. A significant amount of data used to train the largest models already comes from open repositories like Wikipedia or Common Crawl, which scrapes data from the web and shares it freely. Companies could simply share the open resources used to train their models, he says, making it possible to recreate a reasonable approximation that should allow people to study and understand models.

The lack of clarity regarding whether training on art or writing scraped from the internet infringes on the creator’s property rights can cause legal complications though, says Aviya Skowron, head of policy and ethics at the nonprofit AI research group EleutherAI, also involved in the OSI process. That makes developers wary of being open about their data.

Stefano Zacchiroli, a professor of computer science at the Polytechnic Institute of Paris who is also contributing to the OSI definition, appreciates the need for pragmatism. His personal view is that a full description of a model’s training data is the bare minimum for it to be described as open source, but he recognizes that stricter definitions of open-source AI might not have broad appeal.

Ultimately, the community needs to decide what it’s trying to achieve, says Zacchiroli: “Are you just following where the market is going so that they don’t essentially co-opt the term ‘open-source AI,’ or are you trying to pull the market toward being more open and providing more freedoms to the users?”

What’s the point of open source?

It’s debatable how much any definition of open-source AI will level the playing field anyway, says Sarah Myers West, co–executive director of the AI Now Institute. She coauthored a paper published in August 2023 exposing the lack of openness in many open-source AI projects. But it also highlighted that the vast amounts of data and computing power needed to train cutting-edge AI creates deeper structural barriers for smaller players, no matter how open models are.

Myers West thinks there’s also a lack of clarity regarding what people hope to achieve by making AI open source. “Is it safety, is it the ability to conduct academic research, is it trying to foster greater competition?” she asks. “We need to be way more precise about what the goal is, and then how opening up a system changes the pursuit of that goal.”

The OSI seems keen to avoid those conversations. The draft definition mentions autonomy and transparency as key benefits, but Maffulli demurred when pressed to explain why the OSI values those concepts. The document also contains a section labeled “out of scope issues” that makes clear the definition won’t wade into questions around “ethical, trustworthy, or responsible” AI.

Maffulli says historically the open-source community has focused on enabling the frictionless sharing of software and avoided getting bogged down in debates about what that software should be used for. “It’s not our job,” he says.

But those questions can’t be dismissed, says Warso, no matter how hard people have tried over the decades. The idea that technology is neutral and that topics like ethics are “out of scope” is a myth, she adds. She suspects it’s a myth that needs to be upheld to prevent the open-source community’s loose coalition from fracturing. “I think people realize it’s not real [the myth], but we need this to move forward,” says Warso.

Beyond the OSI, others have taken a different approach. In 2022, a group of researchers introduced Responsible AI Licenses (RAIL), which are similar to open-source licenses but include clauses that can restrict specific use cases. The goal, says Danish Contractor, an AI researcher who co-created the license, is to let developers prevent their work from being used for things they consider inappropriate or unethical.

“As a researcher, I would hate for my stuff to be used in ways that would be detrimental,” he says. And he’s not alone: a recent analysis he and colleagues conducted on AI startup Hugging Face’s popular model-hosting platform found that 28% of models use RAIL. 

The license Google attached to its Gemma follows a similar approach. Its terms of use list various prohibited use cases considered “harmful,” which reflects its “commitment to developing AI responsibly,” the company said in a recent blog post .The Allen Institute for AI has also developed its own take on open licensing. Its ImpACT Licenses restrict redistribution of models and data based on their potential risks.

Given how different AI is from conventional software, some level of experimentation with different degrees of openness is inevitable and probably good for the field, says Luis Villa , cofounder and legal lead at open-source software management company Tidelift. But he worries that a proliferation of “open-ish” licenses that are mutually incompatible could negate the frictionless collaboration that made open source so successful, slowing down innovation in AI, reducing transparency, and making it harder for smaller players to build on each other’s work.

Ultimately, Villa thinks the community needs to coalesce around a single standard, otherwise industry will simply ignore it and decide for itself what “open” means. He doesn’t envy the OSI’s job, though. When it came up with the open-source software definition it had the luxury of time and little outside scrutiny. Today, AI is firmly in the crosshairs of both big business and regulators.

Artificial intelligence

Large language models can do jaw-dropping things. but nobody knows exactly why..

And that's a problem. Figuring it out is one of the biggest scientific puzzles of our time and a crucial step towards controlling more powerful future models.

• Will Douglas Heaven archive page

OpenAI teases an amazing new generative video model called Sora

The firm is sharing Sora with a small group of safety testers but the rest of us will have to wait to learn more.

Google’s Gemini is now in everything. Here’s how you can try it out.

Gmail, Docs, and more will now come with Gemini baked in. But Europeans will have to wait before they can download the app.

Google DeepMind’s new generative model makes Super Mario–like games from scratch

Genie learns how to control games by watching hours and hours of video. It could help train next-gen robots too.

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​Why School Absences Have ‘Exploded’ Almost Everywhere

The pandemic changed families’ lives and the culture of education: “Our relationship with school became optional.”

By Sarah Mervosh and Francesca Paris

Sarah Mervosh reports on K-12 education, and Francesca Paris is a data reporter.

In Anchorage, affluent families set off on ski trips and other lengthy vacations, with the assumption that their children can keep up with schoolwork online.

In a working-class pocket of Michigan, school administrators have tried almost everything, including pajama day, to boost student attendance.

And across the country, students with heightened anxiety are opting to stay home rather than face the classroom.

In the four years since the pandemic closed schools, U.S. education has struggled to recover on a number of fronts, from learning loss , to enrollment , to student behavior .

But perhaps no issue has been as stubborn and pervasive as a sharp increase in student absenteeism, a problem that cuts across demographics and has continued long after schools reopened.

Nationally, an estimated 26 percent of public school students were considered chronically absent last school year, up from 15 percent before the pandemic, according to the most recent data, from 40 states and Washington, D.C., compiled by the conservative-leaning American Enterprise Institute . Chronic absence is typically defined as missing at least 10 percent of the school year, or about 18 days, for any reason.

Source: Upshot analysis of data from Nat Malkus, American Enterprise Institute. Districts are grouped into highest, middle and lowest third.

The increases have occurred in districts big and small, and across income and race. For districts in wealthier areas, chronic absenteeism rates have about doubled, to 19 percent in the 2022-23 school year from 10 percent before the pandemic, a New York Times analysis of the data found.

Poor communities, which started with elevated rates of student absenteeism, are facing an even bigger crisis: Around 32 percent of students in the poorest districts were chronically absent in the 2022-23 school year, up from 19 percent before the pandemic.

Even districts that reopened quickly during the pandemic, in fall 2020, have seen vast increases.

“The problem got worse for everybody in the same proportional way,” said Nat Malkus, a senior fellow at the American Enterprise Institute, who collected and studied the data.

Victoria, Texas reopened schools in August 2020, earlier than many other districts. Even so, student absenteeism in the district has doubled.

Kaylee Greenlee for The New York Times

The trends suggest that something fundamental has shifted in American childhood and the culture of school, in ways that may be long lasting. What was once a deeply ingrained habit — wake up, catch the bus, report to class — is now something far more tenuous.

“Our relationship with school became optional,” said Katie Rosanbalm, a psychologist and associate research professor with the Center for Child and Family Policy at Duke University.

The habit of daily attendance — and many families’ trust — was severed when schools shuttered in spring 2020. Even after schools reopened, things hardly snapped back to normal. Districts offered remote options, required Covid-19 quarantines and relaxed policies around attendance and grading .

Source: Nat Malkus, American Enterprise Institute . Includes districts with at least 1,500 students in 2019. Numbers are rounded. U.S. average is estimated.

Today, student absenteeism is a leading factor hindering the nation’s recovery from pandemic learning losses , educational experts say. Students can’t learn if they aren’t in school. And a rotating cast of absent classmates can negatively affect the achievement of even students who do show up, because teachers must slow down and adjust their approach to keep everyone on track.

“If we don’t address the absenteeism, then all is naught,” said Adam Clark, the superintendent of Mt. Diablo Unified, a socioeconomically and racially diverse district of 29,000 students in Northern California, where he said absenteeism has “exploded” to about 25 percent of students. That’s up from 12 percent before the pandemic.

U.S. students, overall, are not caught up from their pandemic losses. Absenteeism is one key reason.

Why Students Are Missing School

Schools everywhere are scrambling to improve attendance, but the new calculus among families is complex and multifaceted.

At South Anchorage High School in Anchorage, where students are largely white and middle-to-upper income, some families now go on ski trips during the school year, or take advantage of off-peak travel deals to vacation for two weeks in Hawaii, said Sara Miller, a counselor at the school.

For a smaller number of students at the school who qualify for free or reduced-price lunch, the reasons are different, and more intractable. They often have to stay home to care for younger siblings, Ms. Miller said. On days they miss the bus, their parents are busy working or do not have a car to take them to school.

And because teachers are still expected to post class work online, often nothing more than a skeleton version of an assignment, families incorrectly think students are keeping up, Ms. Miller said.

Sara Miller, a counselor at South Anchorage High School for 20 years, now sees more absences from students across the socioeconomic spectrum.

Ash Adams for The New York Times

Across the country, students are staying home when sick , not only with Covid-19, but also with more routine colds and viruses.

And more students are struggling with their mental health, one reason for increased absenteeism in Mason, Ohio, an affluent suburb of Cincinnati, said Tracey Carson, a district spokeswoman. Because many parents can work remotely, their children can also stay home.

For Ashley Cooper, 31, of San Marcos, Texas, the pandemic fractured her trust in an education system that she said left her daughter to learn online, with little support, and then expected her to perform on grade level upon her return. Her daughter, who fell behind in math, has struggled with anxiety ever since, she said.

“There have been days where she’s been absolutely in tears — ‘Can’t do it. Mom, I don’t want to go,’” said Ms. Cooper, who has worked with the nonprofit Communities in Schools to improve her children’s school attendance. But she added, “as a mom, I feel like it’s OK to have a mental health day, to say, ‘I hear you and I listen. You are important.’”

Experts say missing school is both a symptom of pandemic-related challenges, and also a cause. Students who are behind academically may not want to attend, but being absent sets them further back. Anxious students may avoid school, but hiding out can fuel their anxiety.

And schools have also seen a rise in discipline problems since the pandemic, an issue intertwined with absenteeism.

Dr. Rosanbalm, the Duke psychologist, said both absenteeism and behavioral outbursts are examples of the human stress response, now playing out en masse in schools: fight (verbal or physical aggression) or flight (absenteeism).

“If kids are not here, they are not forming relationships,” said Quintin Shepherd, the superintendent in Victoria, Texas.

Quintin Shepherd, the superintendent in Victoria, Texas, first put his focus on student behavior, which he described as a “fire in the kitchen” after schools reopened in August 2020.

The district, which serves a mostly low-income and Hispanic student body of around 13,000, found success with a one-on-one coaching program that teaches coping strategies to the most disruptive students. In some cases, students went from having 20 classroom outbursts per year to fewer than five, Dr. Shepherd said.

But chronic absenteeism is yet to be conquered. About 30 percent of students are chronically absent this year, roughly double the rate before the pandemic.

Dr. Shepherd, who originally hoped student absenteeism would improve naturally with time, has begun to think that it is, in fact, at the root of many issues.

“If kids are not here, they are not forming relationships,” he said. “If they are not forming relationships, we should expect there will be behavior and discipline issues. If they are not here, they will not be academically learning and they will struggle. If they struggle with their coursework, you can expect violent behaviors.”

Teacher absences have also increased since the pandemic, and student absences mean less certainty about which friends and classmates will be there. That can lead to more absenteeism, said Michael A. Gottfried, a professor at the University of Pennsylvania Graduate School of Education. His research has found that when 10 percent of a student’s classmates are absent on a given day, that student is more likely to be absent the following day.

Absent classmates can have a negative impact on the achievement and attendance of even the students who do show up.

Is This the New Normal?

In many ways, the challenge facing schools is one felt more broadly in American society: Have the cultural shifts from the pandemic become permanent?

In the work force, U.S. employees are still working from home at a rate that has remained largely unchanged since late 2022 . Companies have managed to “put the genie back in the bottle” to some extent by requiring a return to office a few days a week, said Nicholas Bloom, an economist at Stanford University who studies remote work. But hybrid office culture, he said, appears here to stay.

Some wonder whether it is time for schools to be more pragmatic.

Lakisha Young, the chief executive of the Oakland REACH, a parent advocacy group that works with low-income families in California, suggested a rigorous online option that students could use in emergencies, such as when a student misses the bus or has to care for a family member. “The goal should be, how do I ensure this kid is educated?” she said.

Relationships with adults at school and other classmates are crucial for attendance.

In the corporate world, companies have found some success appealing to a sense of social responsibility, where colleagues rely on each other to show up on the agreed-upon days.

A similar dynamic may be at play in schools, where experts say strong relationships are critical for attendance.

There is a sense of: “If I don’t show up, would people even miss the fact that I’m not there?” said Charlene M. Russell-Tucker, the commissioner of education in Connecticut.

In her state, a home visit program has yielded positive results , in part by working with families to address the specific reasons a student is missing school, but also by establishing a relationship with a caring adult. Other efforts — such as sending text messages or postcards to parents informing them of the number of accumulated absences — can also be effective.

Regina Murff has worked to re-establish the daily habit of school attendance for her sons, who are 6 and 12.

Sylvia Jarrus for The New York Times

In Ypsilanti, Mich., outside of Ann Arbor, a home visit helped Regina Murff, 44, feel less alone when she was struggling to get her children to school each morning.

After working at a nursing home during the pandemic, and later losing her sister to Covid-19, she said, there were days she found it difficult to get out of bed. Ms. Murff was also more willing to keep her children home when they were sick, for fear of accidentally spreading the virus.

But after a visit from her school district, and starting therapy herself, she has settled into a new routine. She helps her sons, 6 and 12, set out their outfits at night and she wakes up at 6 a.m. to ensure they get on the bus. If they are sick, she said, she knows to call the absence into school. “I’ve done a huge turnaround in my life,” she said.

But bringing about meaningful change for large numbers of students remains slow, difficult work .

Nationally, about 26 percent of students were considered chronically absent last school year, up from 15 percent before the pandemic.

The Ypsilanti school district has tried a bit of everything, said the superintendent, Alena Zachery-Ross. In addition to door knocks, officials are looking for ways to make school more appealing for the district’s 3,800 students, including more than 80 percent who qualify for free or reduced-price lunch. They held themed dress-up days — ’70s day, pajama day — and gave away warm clothes after noticing a dip in attendance during winter months.

“We wondered, is it because you don’t have a coat, you don’t have boots?” said Dr. Zachery-Ross.

Still, absenteeism overall remains higher than it was before the pandemic. “We haven’t seen an answer,” she said.

Data provided by Nat Malkus, with the American Enterprise Institute. The data was originally published on the Return to Learn tracker and used for the report “ Long COVID for Public Schools: Chronic Absenteeism Before and After the Pandemic .”

The analysis for each year includes all districts with available data for that year, weighted by district size. Data are sourced from states, where available, and the U.S. Department of Education and NCES Common Core of Data.

For the 2018-19 school year, data was available for all 50 states and the District of Columbia. For 2022-23, it was available for 40 states and D.C., due to delays in state reporting.

Closure length status is based on the most in-person learning option available. Poverty is measured using the Census Bureau’s Small Area Income and Poverty Estimates. School size and minority population estimates are from NCES CCD.

How absenteeism is measured can vary state by state, which means comparisons across state lines may not be reliable.

An earlier version of this article misnamed a research center at Duke University. It is the Center for Child and Family Policy, not the Center of Child and Family Policy.