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The Six Systems Thinking Steps to Solve Complex Problems

A quick overview of common problem solving techniques indicates that most of these methods focus on the problem rather than the whole eco-system where the problem exists. Along with the challenges of global economy , problems turn out to be more complicated and sometimes awakening problems. Climate change, traffic problems, and organizational problems that have developed through the years are all complex problems that we shouldn’t look at the same way as simple or linear problems. Part of the problem of thinking about a complex problem is the way we approach it, which may contribute to making the problem even more complex. As stated by Albert Einstein, “The problems cannot be solved using the same level of thinking that created them.” Systems thinking tends to focus on the broader ecosystem rather than the problem itself.

Systems thinking was developed by Jay Forrester and members of the Society for Organizational Learning at MIT. The idea is described in his book, The Fifth Discipline , as follows: “Systems thinking is a discipline for seeing wholes. It is a framework for seeing interrelationships rather than things, for seeing patterns of change rather than static ‘snapshots.’” A common example of the systems thinking method is the life around us where multiple systems interact with each other and are affected by each other. This wide perspective of systems thinking promotes it to solve complex problems that are dependent on external factors. Below are some of the stations that system thinking may contribute to solve.

• Complex problems that involve different factors, which require understanding the big picture in order to be efficiently solved
• Situations that are affecting, are being affected by, or affect the surrounding systems
• Problems that have turned more complicated by previous attempts to solve them

Concepts of Systems Thinking

In order to understand systems thinking, a number of concepts should be highlighted in order to define the relation between the problem and the other elements in the system and how to observe this relation in order to reach an effective solution. These principles include the following.

• All systems are composed of interconnected parts, and changing one part affects the entire system, including other parts.
• The structure of a system determines its behavior, which means that the system depends on the connection between parts rather that the part themselves.
• System behavior is an emergent phenomenon. System behavior is hard to predict due its continuously changing, non-linear relations and its time delay. It can’t be predicted by simply inspecting its elements or structure.
• Feedback loops control a system’s major dynamic behavior. The feedback loop is a number of connections causing an output from one part to eventually influence input to that same part. The number of feedback loops are larger than the system parts, which contributes to increasing system complicity.
• Complex social systems exhibit counterintuitive behavior. Solving complex problems can’t be achieved through everyday problem solving methods. They can be solved only through analytical methods and tools. Solving complex problems can be achieved through systems thinking, a process that fits the problem, and system dynamics , which is an approach to model systems by emphasizing their feedback loops.

Systems Thinking in Six Steps

In their paper Six Steps to Thinking Systemically , Michael Goodman and Richard Karash introduced six steps to apply systems thinking principles while solving complex problems. These steps were part of their case study to Bijou Bottling company’s problem of getting their orders shipped on time.

Set 1: Tell the Story

The first step in solving the problem is to understand it, and this can be achieved through looking deeply at the whole system rather than individual parts. This step requires meeting with the stakeholders to share their vision about the situation. One of the common tools to build this understanding is to utilize Concept Maps, which are graphical tools used to represent the organization or a structure of knowledge. Concept Maps visually present the system’s elements, concept links, proposition statements, cross-links, and examples.

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Step 2: Draw Behavior Over Time (BOT) Graphs

When thinking about a problem, we are influenced with the current situation that is reflected in our analysis, yet the problem follows a time dimension, which means that it should be tracked through the time. The Behavior Over Time graph draws a curve that presents a specific behavior (Y) through the time (X). This graph helps us to understanding whether or not the current solution is effective.

Step 3: Create a Focusing Statement

At this point, there should be a clear vision about the problem solving process, which is defined in the from of a statement that indicates the team’s target and why the problem occurs.

Step 4: Identify the Structure

After having clear vision about the problem through the proposed statement, the system structure should be described, including the behavior patterns. Building these patterns helps in understanding more about the problem, and it can be formed as a system archetype.

Step 5: Going Deeper into the Issues

After defining the problem and the system structure, this step tends to understand the underlying problems through clarifying four items: the purpose of the system (what we want), the mental models, the large system, and personal role in the situation.

Set 6: Plan an Intervention

The previously collected information is used to start the intervention phase, where modifications to the current problem relate parts to connections. This intervention attempts to reach the desirable behavior.

Practice Example of Systems Thinking

One of the direct examples of adopting the systems thinking method was presented by Daniel Aronson highlighting insects who caused damage crops. Traditional thinking to solve crop damage is to apply more pesticides to reduce the number of insects and subsequently reduce the crop damage. However, this solution solves the problem for a short term. In the long run, the problem isn’t truly solved, as the original insect eating the crops are controlling the population of another species of insect in the environment either by preying on it or competing with it. Subsequently, the crop damage increases again due to the increasing numbers of other insect species.

Observing the ecosystem that includes both the insects and the crops, systems thinking suggests exploring a solution that ensures reducing the crop damage in the long run without affecting the environmental balance, such as deploying the Integrated Pest Management that has proven success based on MIT and the National Academy of Science. This solution tends to control the number of an insect species by introducing its predators in the area.

Unlike everyday problems, complex problems can’t be solved using traditional problem solving methods due to the nature of the problems and their complexity. One of the theories that attempts to understand complex problems is systems thinking, which is defined by a number of characters. Six steps are to be used to explore and solve complex problems under the umbrella of systems thinking, which help us to observe and think in a whole eco-system rather than individual parts. Systems thinking can be deployed in multiple domains to solve organization problem, or global problems such as energy, pollution, and poverty.

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Dr Rafiq Elmansy

I'm an academic, author and design thinker, currently teaching design at the University of Leeds with a research focus on design thinking, design for health, interaction design and design for behaviour change. I developed and taught design programmes at Wrexham Glyndwr University, Northumbria University and The American University in Cairo. Additionally, I'm a published book author and founder of Designorate.com. I am a fellow for the Higher Education Academy (HEA), the Royal Society of Arts (FRSA), and an Adobe Education Leader. I write Adobe certification exams with Pearson Certiport. My design experience involves 20 years working with clients such as the UN, World Bank, Adobe, and Schneider. I worked with the Adobe team in developing many Adobe applications for more than 12 years.

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3 thoughts on “ The Six Systems Thinking Steps to Solve Complex Problems ”

“Systems thinking was developed by Jay Forrester and members of the Society for Organizational Learning at MIT. The idea is described in his book, The Fifth Discipline, as follows:” Peter Senge is the author of The Fifth Discipline

Thank you so much Misi for the helpful information.

Thank you for the valuable information. I believe that systems thinking can be applied to every aspect of our lives. When you teach yourself to spot patterns, cycles, and loops instead of individuals elements. You see behind the scenes. Understand what actually needs addressing to move forward and make progress faster with less damage.

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Framing Complex Problems with Systems Thinking Cornell Course

Select start date, framing complex problems with systems thinking, course overview.

Whether you need to tackle a complex project, communicate more effectively, rethink your organization or your job, solve world hunger, or figure out your teenager, systems thinking can help you. All of these are complex and challenging real-world problems, sometimes called wicked problems. We all confront problems, big and small, in our personal and professional lives, and most of us are searching for better ways to solve them. In this course, Professors Derek and Laura Cabrera will demonstrate how we can use systems thinking to solve everyday and wicked problems, to transform our organizations, and to increase our personal effectiveness.

At its core, systems thinking attempts to better align the way we think with how the real world works. Our thinking is based on our mental models, but these models, created from our unique perspective with its inherent biases, are usually inadequate representations of reality. The Cabreras illustrate how we can use feedback to recognize and adapt our mental models so that they better align with reality, enhancing our problem-solving capabilities.

For systems thinking to be successful, it must be adaptive. In this course, you will explore the concept of complex adaptive systems, and while these systems seem unnecessarily complicated, the Cabreras will reveal a surprising discovery. Underlying all complex adaptive systems are simple rules, and applying these rules is the key to transforming the way we frame and solve everyday problems.

Key Course Takeaways

• Identify and describe the problems you want to solve in your personal and professional lives
• Examine the mental models you have and how they differ from reality
• Determine how you can use feedback to improve your mental models
• Recognize the biases that you have that can distort your mental models
• Examine complex adaptive systems and the simple rules that underlie these systems
• Determine how systems thinking is a complex adaptive system
• Explore the four simple rules that underlie systems thinking

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How it works, course authors.

• Certificates Authored

Laura is Plectica’s Chief Research Officer.

For over 15 years, Laura has conducted translational research to increase public understanding, application, and dissemination of systems science, including for USDA, the National Academy of Sciences Institute of Medicine, HHS, and the Dept. of Justice.

She is also a senior researcher at Cabrera Research Lab, has authored five books on systems thinking and its applications, and is a member of the United States Military Academy at West Point’s Systems Engineering Advisory Board.

Laura holds a PhD in Policy Analysis and Management, a Master’s in Public Administration, and a B.A., all from Cornell.

Her family is her favorite system…

• Digital Leadership
• Intrapreneurship
• Systems Thinking

Derek Cabrera (Ph.D., Cornell) is a systems scientist, Professor, and social entrepreneur and is internationally known for his work in systems thinking, systems leadership, and systems modeling. He is currently a lecturer at Cornell University where he teaches systems thinking and organizational leadership and design. He is senior scientist at  Cabrera Research Lab , and co-founder and Chief Science Officer of  Plectica . He has given two TED Talks, written and produced a  rap song , a children’s book on cognition, and authored numerous book chapters and peer-reviewed journal articles. His research has been profiled in peer-reviewed journals, trade magazines, and popular publications, and he is author of eight books including,  Systems Thinking Made Simple: New Hope for Solving Wicked Problems  (winner of the 2017 AECT outstanding book award),  Thinking at Every Desk: Four Simple Skills to Transform Your Classroom , and  Flock Not Clock: Align People, Processes, and Systems to Achieve your Vision . Credited with discovering the underlying rules of systems thinking, Cabrera is co-editor of the Routledge Handbook of Systems Thinking. His work in public schools was documented in the full-length documentary film,  RE:Thinking . He was Research Fellow at the Santa Fe Institute (SFI) for the Study of Complex Systems and National Science Foundation IGERT Fellow in Nonlinear Systems in the Department of Theoretical and Applied Mechanics at Cornell University. As a National Science Foundation postdoctoral fellow, he developed new techniques to model systems approaches in the evaluation of Science, Technology, Engineering, and Mathematics (STEM). Cabrera was awarded the Association of American Colleges and Universities’ K. Patricia Cross Future Educational Leaders Award. He serves on the United States Military Academy at West Point’s Systems Engineering Advisory Board. His contributions to the field of systems thinking have been integrated into NSF, NIH, and USDA-NIFA programs, K-12, higher education, NGOs, federal agencies, corporations, and business schools. His systems models are used by many of Silicon Valley’s most innovative companies. Systems Thinking Made Simple is used as an introductory text for undergraduate and graduate students in numerous colleges and universities including Cornell University and West Point Military Academy. Cabrera has developed and patented a suite of systems thinking tools for use in academia, business, and beyond. Prior to becoming a scientist, Cabrera worked for fifteen years around the world as a mountain guide and experiential educator for Outward Bound and other organizations and has climbed many of the world’s highest mountains. He holds a Ph.D. from Cornell University and lives in Ithaca, NY, with his wife, Laura Cabrera, three children, and four dogs.

Who Should Enroll

• Managers, leaders, decision makers, consultants, and anyone responsible for projects, complex processes, and the budgets and people involved with them. Learners will come from every continent and from a diverse range of organizations, including private sector companies large and small, nonprofits, governments, and NGOs.
• For people already interested in systems dynamics or soft systems methodologies, the core principles from this program can be applied to any systems-based models.

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Systems Thinking: A Deep Dive Into The Framework To Successfully Solve Complex Problems

Systems thinking, also known as systems analysis or system dynamics, looks at the world that emphasizes how things work together and interact. It’s an approach to understanding complex problems by breaking them down into their constituent parts so you can analyze them in terms of cause-and-effect relationships. This detailing helps us understand why something happens rather than just what it looks like on the surface. This article will explore the critical concepts around systems thinking.

Professor J. W. Forrester developed the concept of Systems thinking in 1956. Researchers have defined complexity as “the property of being composed of many interrelated elements.” Systems thinking is not new; philosophers have been using this concept since ancient times. But until recently, most people did not realize that their everyday lives were governed by rules similar to those found in natural phenomena. However, scientists have begun to recognize that living organisms also exhibit emergent properties and self-organization in recent years. These discoveries suggest that there may exist universal principles governing life on Earth.

Table of Contents

How Does Systems Thinking Differ from Critical Thinking?

Systems thinking is a way of looking at the world that emphasizes how things are connected. It’s about seeing patterns and relationships, not just in individual parts but also across systems as a whole. This approach can be applied to any situation or problem you encounter—from personal life to business management to global politics.

Critical thinking is an entirely different type of mindset. Instead of viewing problems through the lens of interconnectedness, it focuses on identifying what needs to change and then figuring out ways to make those changes happen. In this sense, critical thinking is more like detective work than systems thinking: You start with a hypothesis and then try to prove whether or not your theory is correct by testing it against reality.

Why are systems thinking important?

Systems thinkers are those who understand the world as a complex adaptive system. They see that everything in nature, including human society and organizations, has dynamics that one cannot comprehend by studying only one part or even looking at details from different perspectives. Instead, they look for patterns across all aspects of reality to know how things work together. This approach leads them to ask questions such as: How do we create change? What makes something successful? Why do some organizations fail while others thrive? And what can we learn about ourselves when we study other species?

What are Complex Systems?

Complex systems can be defined as a set of interacting elements that produce emergent properties. The American mathematician and philosopher John von Neumann coined the term complex system in his book “Theory of Self-Reproducing Automata.” He used it to describe self-reproducing machines or automatons. In this context, he meant an entity that can reproduce itself from its parts without any external intervention. This definition has been widely adopted since then. It is also known as autopoiesis, self-organization, self-regulation, self-maintenance, or self-production.

A simple example would be a living cell where each component interacts with other elements. These interactions lead to the production of new proteins and DNA molecules. Thus the whole process leads to the reproduction of the original molecule.

What Are Complex Systems In Business?

Complex systems are a new way of looking at the world. They’re not just about understanding how things work, but also why they do what they do and how to make them better.

The term “complex system” was coined by John P. Kotter in his book Leading Change. He defined it as: “a set of people or organizations that interact with each other more than one would expect from chance alone.”

The idea is simple – if you look closely enough at any group of people interacting together, patterns will emerge to help us understand their behavior. This insight has been used for centuries in psychology, sociology, anthropology, economics, and politics. But until recently, these insights have only applied to small groups of individuals.

What Are Adaptive Systems?

Adaptive systems are complex, dynamic, and self-organizing. They can be viewed as a collection of interacting components that continuously adapt to changing conditions in their environment. The term “adaptation” is used in the sense of an ongoing process rather than a one-time event or outcome. Adaptive systems have no fixed state, but instead, they continually change over time. In this way, they resemble living organisms that also constantly evolve through adaptation.

Adaptive systems are a way of looking at the world. You can use them to describe any system changing and adapting to its environment or apply to business processes. The term adaptive was coined by John Todd, who defined it as “a process which changes itself according to external conditions.” He also said: “The purpose of an adaptive system is not to achieve some pre-determined goal but rather to maintain stability within the context of change.” This definition has been widely adopted since then.

What are the characteristics of systems thinking?

Systems Thinking is a way to look at the world. It’s not just about looking for problems but also finding solutions and making things better. Systems Thinking helps us understand how to make our lives more sustainable by changing ourselves and our environment.

Characteristics of the Systems Thinking approach include;

1) A focus on understanding complex social-ecological interactions in their natural context. This understanding means that it considers all aspects of an issue or problem – from human behavior to physical processes, including feedback loops between these two levels.

2) An emphasis on learning through experience rather than knowledge alone. The goal is to understand what works best when applied to specific situations.

3) Emphasis on action over-analysis. We need to act now to solve current issues and create new opportunities. Analysis should be used to inform decisions, not dictate them.

4) Focus on creating positive change. Change happens if people want it to happen. If you don’t like something, then do something about it!

5) Use multiple perspectives. Each perspective provides different insights into the same situation. However, when combined, they give a fuller picture.

6) Look beyond the obvious. There may be other factors involved which you may have overlooked. 

7) Think globally, act locally. Our actions affect everyone around us. Therefore, we must think globally before acting locally.

How do you use System thinking?

Systems Thinking is a way of looking at the world. It’s not just about seeing things as they are, but also how we can change them to be better for everyone involved. Systems Thinking helps us understand that everything in our lives impacts other parts of life and vice versa. We need to think more holistically when solving problems because there isn’t always one solution or many solutions.

Here are the steps you can use to adopt systems thinking;

1) Understand what system means: A system works together with others so that all its components work towards achieving some goal. For example, if I have a car, my engine will run by itself without pushing buttons. But, if I want to start the car, I press the button, and the starter motor turns over the engine. The same thing happens inside people – their heartbeats, lungs breathe, or the stomach digest food. All these processes automatically happen unless someone stops them from doing this.

So, a system is like a machine where each part does its job independently until another component comes into action. So, when we talk about systems, we mean anything that functions with other elements to achieve a common purpose.

2) Identify the problem: Once you know what a system is, you must identify the problem within the system. The problem could be due to a lack of knowledge, skills, resources, time, money, motivation, or support. You may find yourself asking questions such as “Why did this happen? Why didn’t anyone else notice this before now? What would make this situation different next time? How can we prevent this happening again?” These types of questions help you get started identifying the problem.

 3) Define the boundaries: Now that you have identified the problem, you should define the perimeter around the problem. In other words, you should decide who needs to take responsibility for fixing the issue. For example, who is responsible for making sure the problem doesn’t occur again? Is it only the person who made a mistake? Or is it the whole team/company? Whose fault was it? Was it the manager’s fault? Did he fail to supervise his staff correctly? Or were the employees lazy or under-skilled? Is it a training issue or lack of enough equipment? Is the environment conducive to learning new skills? There are lots of factors that contribute to creating a good working environment. Some of these might include physical space, communication channels, management style, culture.

 It depends on the context of whether you consider these issues essential or not. But once you have defined the boundaries, you can move forward to solve the problem.

4) Decide on possible actions: After defining the boundaries, you should develop several options to fix the problem. Each option should address the root cause of the problem. For instance, if you were trying to improve employee performance, you wouldn’t just focus on improving pay rates. Instead, you would look at how your organization trains employees, provides opportunities for career development, encourages feedback, rewards positive behavior, and promotes teamwork. Similarly, when you try to reduce waste in an organization, you don’t simply cut down on paper consumption. Instead, you need to think about ways to eliminate unnecessary paperwork, streamline procedures, and encourage collaboration between departments. Again, you will observe the patterns of behavior of your employees and act accordingly.

 5) Choose one solution and implement it: Finally, after deciding upon all the necessary steps to resolve the problem, choose one answer and start implementing it. If multiple solutions are available, pick the most appropriate one based on cost, complexity, risk, impact, and feasibility. The key here is to ensure that you do something rather than nothing. And remember, no matter which approach you use, you will always face challenges along the way. So be prepared!

What kind of problems do systems thinking solve?

I’m not sure if this is the right place to ask, but I’ve been reading a lot about “systems thinking” lately, and it seems like there are many different definitions. Some people say that it’s just an approach for solving complex problems, while others claim that it can be used as a tool for understanding any system or process. So what exactly do you mean when you talk about systems thinking? What kinds of problems does it help you solve? Is it only applicable in specific fields? Or could anyone use it to understand their own life better? Let’s explore the application of systems thinking in detail.

Systems Thinking is a way of looking at things from multiple perspectives simultaneously. The problems may represent complex systems or not. It helps us see how all parts fit together into one whole picture. For example, we might look at our body and think about its functions separately, such as digestion or respiration, and consider them holistically by seeing how they work together to keep us alive. 

These two approaches allow us to apply systems thinking to other areas of our lives. We can learn more about ourselves through systems thinking than we ever thought possible!

For instance, let’s imagine your car breaks down on the side of the road. You have no idea where to go, so you call AAA. They send out someone who will come pick up your vehicle and bring it back to the shop. The mechanic tells you that he needs to replace some parts because something went wrong during the repair. He says he has to order new parts online since his store doesn’t carry those particular items anymore. While waiting for him to return, you start wondering why you need to buy another set of tires. Why don’t you already have good ones? After all, you drive every day. Then you remember that you haven’t changed the oil in over a year. That means you should probably get a tune-up soon. And maybe you should change the air filter too. Perhaps even clean the windows.

All of these tasks seem simple enough, but now you’re starting to realize that each job requires several steps before it gets done. If you could view everything around you in terms of systems, you would notice that the entire situation was much bigger than you initially realized. 

Examples of systems thinking in everyday life/Business.

In this section, we will describe some examples of how the concept of Systems Thinking can be applied to real-life situations. We have chosen these cases because they represent many other similar problems people face every day and could benefit from a more systemic approach. The first example is about an organization with no clear vision or strategy; the second one shows how a company has created value using Systemic Thinking. In both cases, it is essential to understand what kind of system you want to build.

Example 1: A lack of strategic direction

The following case study describes a large international corporation where several departments had strategies without any overall plan. Each department had its own goals and objectives, but none knew anything about the others’ activities. This disparity resulted in much confusion among employees who did not understand why they should do certain things. There was also a high turnover rate within each department as well as between departments. It took years before anyone realized that all parts needed to work together towards achieving the same goal.

The solution? Create a shared vision and common values across the whole organization. Once everyone understood the big picture, everything became much clearer. Employees started working on projects that made sense and helped achieve the desired results. They felt part of something bigger than themselves. And most importantly, the company’s performance improved significantly.

Example 2: Creating value through systemic thinking

This story illustrates how a small business used Systemic Thinking to improve its operations. When the owner decided to sell his business, he wanted to ensure that the new owners would continue running it successfully after him. So he asked himself, “What does my business need?” After answering this question, he came up with three primary needs: Generating revenue, providing exemplary service to customers, and keeping costs low. These three requirements formed the basis of his business plan.

He then looked into the market and discovered that two companies were already providing services very close to his business. However, neither of them met all three criteria mentioned above. So he set out to find another way to meet these needs. By doing so, he discovered that four distinct markets existed in his area. He created a marketing mix that included advertising campaigns targeting specific groups of potential clients with this knowledge. As a result, his sales increased dramatically. His profits went down slightly due to higher production costs, but he still raised his net income substantially.

Systems Thinking helps us see our world differently. We can use it to help solve the problems we are facing today and prepare for future challenges.

     Systems Thinking is an approach to problem-solving based on understanding systems instead of focusing only on individual elements. The idea behind this concept is simple: if you look at your environment from a broader perspective, you will be better prepared to deal with unexpected events. In addition, you will have more options available when making decisions because you will consider many factors simultaneously rather than just looking at a single aspect.

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Taking a systems thinking approach to problem solving

Systems thinking is an approach that considers a situation or problem holistically and as part of an overall system which is more than the sum of its parts. Taking the big picture perspective, and looking more deeply at underpinnings, systems thinking seeks and offers long-term and fundamental solutions rather than quick fixes and surface change.

Whether in environmental science, organizational change management, or geopolitics, some problems are so large, so complicated and so enduring that it’s hard to know where to begin when seeking a solution.

A systems thinking approach might be the ideal way to tackle essentially systemic problems. Our article sets out the basic concepts and ideas.

What is systems thinking?

Systems thinking is an approach that views an issue or problem as part of a wider, dynamic system. It entails accepting the system as an entity in its own right rather than just the sum of its parts, as well as understanding how individual elements of a system influence one another.

When we consider the concepts of a car, or a human being we are using a systems thinking perspective. A car is not just a collection of nuts, bolts, panels and wheels. A human being is not simply an assembly of bones, muscles, organs and blood.

In a systems thinking approach, as well as the specific issue or problem in question, you must also look at its wider place in an overall system, the nature of relationships between that issue and other elements of the system, and the tensions and synergies that arise from the various elements and their interactions.

The history of systems thinking is itself innately complex, with roots in many important disciplines of the 20th century including biology, computing and data science. As a discipline, systems thinking is still evolving today.

How can systems thinking be applied to problem solving?

A systems thinking approach to problem solving recognizes the problem as part of a wider system and addresses the whole system in any solution rather than just the problem area.

A popular way of applying a systems thinking lens is to examine the issue from multiple perspectives, zooming out from single and visible elements to the bigger and broader picture (e.g. via considering individual events, and then the patterns, structures and mental models which give rise to them).

Systems thinking is best applied in fields where problems and solutions are both high in complexity. There are a number of characteristics that can make an issue particularly compatible with a systems thinking approach:

• The issue has high impact for many people.
• The issue is long-term or chronic rather than a one-off incident.
• There is no obvious solution or answer to the issue and previous attempts to solve it have failed.
• We have a good knowledge of the issue’s environment and history through which we can sensibly place it in a systems context.

If your problem does not have most of these characteristics, systems thinking analysis may not work well in solving it.

Areas where systems thinking is often useful include health, climate change, urban planning, transport or ecology.

What is an example of a systems thinking approach to problem solving?

A tool called the iceberg mode l can be useful in learning to examine issues from a systems thinking perspective. This model frames an issue as an iceberg floating in a wider sea, with one small section above the water and three large sections unseen below.

The very tip of the iceberg, visible above the waterline, shows discrete events or occurrences which are easily seen and understood. For example, successive failures of a political party to win national elections.

Beneath the waterline and invisible, lie deeper and longer-term trends or patterns of behavior. In our example this might be internal fighting in the political party which overshadows and obstructs its public campaigning and weakens its leadership and reputation.

Even deeper under the water we can find underlying causes and supporting structures which underpin the patterns and trends.

For our failing political party, this could mean party rules and processes which encourage internal conflict and division rather than resolving them, and put off the best potential candidates from standing for the party in elections.

The electoral system in the country may also be problematic or unfair, making the party so fearful and defensive against losing its remaining support base, that it has no energy or cash to campaign on a more positive agenda and win new voters.

Mental models

At the very base of the iceberg, deepest under the water, lie the mental models that allow the rest of the iceberg to persist in this shape. These include the assumptions, attitudes, beliefs and motivations which drive the behaviors, patterns and events seen further up in the iceberg.

In this case, this could be the belief amongst senior party figures that they’ve won in the past and can therefore win again someday by repeating old campaigns. Or a widespread attitude amongst activists in all party wings that with the right party leader, all internal problems will melt away and voter preferences will turn overnight.

When is a systems thinking approach not helpful?

If you are looking for a quick answer to a simple question, or an immediate response to a single event, then systems thinking may overcomplicate the process of solving your problem and provide you with more information than is helpful, and in slower time than you need.

For example, if a volcano erupts and the local area needs to be immediately evacuated, applying a thorough systems thinking approach to life in the vicinity of an active volcano is unlikely to result in a more efficient crisis response or save more lives. After the event, systems thinking might be more constructive when considering town rebuilding, local logistics and transport links.

In general, if a problem is short-term, narrow and/or linear, systems thinking may not be the right model of thinking to use.

A final word…

The biggest problems in the real world are rarely simple in nature and expecting a quick and simple solution to something like climate change or cancer would be naive.

If you’d like to know more about applying systems thinking in real life there are many online resources, books and courses you can access, including in specific fields (e.g. FutureLearn’s course on Understanding Systems Thinking in Healthcare ).

Whether you think of it as zooming out to the big picture while retaining a focus on the small, or looking deeper under the water at the full shape of the iceberg, systems thinking can be a powerful tool for finding solutions that recognize the interactions and interdependence of individual elements in the real world.

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So you have what appears to be an unsolvable problem on your hands. It’s an important issue that’s proven to be chronic, its recurrence has made it familiar enough to be identified with a known history, and many have unsuccessfully tried to solve it before.

What you have is a complex problem. Fortunately, a tested strategic approach already exists for solving complex problems - systems thinking .

What is Systems Thinking?

Founded in 1956 by MIT professor Jay Forrester, systems thinking is an approach to solving complex problems by understanding the systems that allow the problems to exist. You have a complex problem when:

• There’s no clear cut agreement on what the problem really is because the context it depends on evolves over time.
• It’s difficult to assess what the real causes are behind the problem due to many factors and feedback loops influencing each other.
• It’s not certain what the best steps are to solve the problem because there are many potential and / or partial solutions that may require incompatible and even conflicting steps.
• It’s hard to pinpoint who has sufficient - ownership, accountability, and authority to solve the problem, or if there even is just a single individual that suits the criteria — and it’s challenging to keep various stakeholders from getting in each others' way.

Where traditional analysis zooms into a smaller piece of a whole, systems thinking zooms out to view not just the whole, but other wholes that are affecting each other. Through this approach, systems thinking formalizes methods, tools, and patterns that allow practitioners to understand and manage complex settings and environments. This is why systems thinking is important — and effective — in solving complex problems.

3 Unique Systems Thinking Benefits

Like other established approaches to solving different kinds of problems, systems thinking can prove insightful and effective when used properly. Beyond those general benefits, systems thinking also presents some unique advantages:

Systems Thinking Allows Meaningful Failure

Failure is a discovery mechanism in properly applied systems thinking. It allows you to learn and improve the design or implementation of your solution. Failure in systems thinking can:

• Allow you to learn and adapt from small missteps quickly.
• Shows you the right option, or at least reduces the wrong ones, when it comes time to test hypotheses.
• Only temporarily hamper a system, not completely jeopardize it, in exchange for meaningful input.

Systems Thinking is Inclusive and Collaborative

Because of the holistic viewpoint taken in systems thinking, it inherently opens up levers for collaboration across involved parties. It isn’t just nice to gain input from diverse stakeholders with dynamically interrelated roles and interests — it's required.

Implemented properly, systems thinking encourages a culture of inclusiveness and collaboration to fix systemic problems that in turn benefit multiple stakeholder teams simultaneously.

Systems Thinking Provides Actionable Foresight

Part of why complex problems are hard to solve is because each involved party only ever sees their portion of the issue. Therefore, they typically execute solutions that resolve parts of the constantly evolving problem, which in the holistic view may even lead to other issues or complications.

Systems thinking allows you to predict how systems change and how steps within parts of the system will impact the whole. In applying systems thinking, you analyze causal structure and system dynamics, assess policies and scenarios, and test action steps and hypotheses to foresee consequences in order to synthesize long-term strategies.

Solving Complex Problems with System Thinking Frameworks and Methodologies

So how do you use systems thinking and its frameworks and methodologies in your organization? Systems thinking is not an instant panacea. Implementing its methods and frameworks isn’t like applying smart charts to raw data on spreadsheets. Those aren’t complex problems.

The implementation of systems thinking involves the application of frameworks that illustrate levels of thinking, and the use of tools to allow people to better understand the behaviors of systems.

The Iceberg Framework

At a primary level, systems thinking takes a holistic view to try and understand the connectedness and interactions of various system components, which themselves could be sub-systems. You can start by focusing on points that people gloss over, and attempt to explore these issues by focusing on aspects you don’t understand. The iceberg framework in systems thinking can guide you through this.

The iceberg framework illustrates four levels of thinking about a problem, arranged thus:

• “Events” - Events form the tip of the iceberg. Events that characterize a complex problem are the most visible, and therefore also the ones that appear to require being addressed in an immediate, reactionary way. This level of thinking is the “shallowest,” as typically events are only symptoms of underlying issues.
• “Patterns and trends” - Directly below the tip of the iceberg, the Patterns level is the first one hidden from view. Thinking deeper about events can lead problem solvers to more insight into patterns and trends that lead to them. Any approaches to solving patterns and trends will more effectively resolve events.
• “Underlying structure” - Even deeper below the surface, you’ll find there are underlying structures that influence the patterns and trends that lead to the visible symptoms of complex problems. This is where the interaction between system components produces the problematic patterns that in turn cause the visible events.
• “Mental models” - Finally, the bottom of the iceberg that props everything up are the assumptions, beliefs, and values held about a system culminating in the inadvertent creation and maintenance of underlying structures that result in unfavorable patterns within systems, which in turn bubble up to the surface as symptomatic events.

Once systems thinking practitioners understand this framework, they can employ tools and technology that allow human perception to genuinely digest the behavior of complex systems. At this level of systems thinking, qualitative tools generate knowledge to unravel complex problems.

Causal Loop Diagrams and System Archetypes

Some of the most common and flexible tools in systems thinking are causal loop diagrams that demonstrate system feedback structures. They show causal links between system components with directional cause and effect. Causal loop diagrams display the interconnectedness of system components to serve as a starting point for further discussion and policy formulation. Naturally, these diagrams can also help problem solvers identify in which parts of the system they can assert a positive influence to impact the entire loop favorably. In effect, these diagrams can help prevent poor decisions such as quick fixes.

Another important tool in systems thinking are the system archetypes that generally describe how complex systems work. They are generic models or templates representing broad situations to provide a high-level map of complex system behavior. Because they have been well-studied and mapped, these models can identify valuable areas where steps can be taken to resolve complex problems through interventions that are called leverages.

In general, there are two basic feedback loops (reinforcing and balancing) that identify nine system archetypes (or eight or ten, depending on who you ask):

• Balancing loops with delays
• Drifting goals
• Fixes that fail
• Growth and underinvestment
• Limits to success
• Shifting the burden
• Success to the successful
• Tragedy of the commons

Each of these archetypes are rarely sufficient models on their own — they merely offer insight into possible, common underlying problems. They can of course also be used as a basic structure upon which you can develop a more detailed model specific to your complex systems.

Adding Advanced Tools into Your Systems Thinking Toolbox

There are several dynamic and structural thinking tools in the systems thinking repertoire. Causal loop diagrams and system archetypes are dynamic thinking tools. Graphical function diagrams and policy structure diagrams are structural thinking tools. All of these can be mapped or used in computer-based tools like a management flight simulator or learning lab.

Of course, there are tools to what you can achieve with your toolbox.

Causal loop diagrams, for example, are static — they cannot describe the evolving properties of a system over time. To overcome such limitations, you need to simulate management issues quantitatively through system dynamics modeling.

Computer models of system dynamics allow you to explore time-dependent complex system behavior under different states. They essentially enable you to simulate how a causal loop diagram evolves as it is affected by different assumptions over time.

Solving Complex Problems in Project Management

So should you start learning about causal loop diagrams and begin shopping for the best systems dynamics computer modeling tools in the market as soon as you find a project management problem you can’t seem to solve? Don’t jump the gun.

You can implement systems thinking in inquiry and problem diagnosis to great effect without needing diagrams and computer models. Apply the concept of the iceberg model and you might already find you’re asking better questions than before, or you’re catching common quick fix solutions — like needing more budget or hiring more people — that don’t address deeper problems.

Once you realize that you’ve got a complex problem that requires an in-depth systems thinking approach, you can then explore your options with your team. The important part is to embrace the mental models that make systems thinking invaluable for understanding complex systems and resolving the complex problems that arise from them.

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• Discover MIT's unique, powerful, and integrative System Dynamics approach to assess problems that will not go away
• Experience the Beer Game, which simulates the supply chain of the beer industry
• Learn a new way of thinking about and resolving complex, persistent problems that emerge from change
• Earn a certificate of course completion from the MIT Sloan School of Management

Why attend Understanding and Solving Complex Business Problems?

Systems thinking was designed to improve people's ability to manage organizations comprehensively in a volatile global environment. It offers managers a framework for understanding complex situations and the dynamics those situations produce. Systems thinking is a response to the rapid changes in technology, population, and economic activity that are transforming the world, and as a way to deal with the ever-increasing complexity of today's business.

Senior managers can use systems thinking to design policies that lead their organizations to high performance. The program is intended to give participants the tools and confidence to manage organizations with full understanding and solid strategy.

Course experience

This complex problem-solving course introduces participants to MIT's unique, powerful, and integrative System Dynamics approach to assess problems that will not go away and to produce the results they want. Through exercises and simulation models, participants experience the long-term side effects and impacts of decisions and understand the ways in which performance is tied to structures and policies.

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This program is designed to empower you to analyze complex problems in any area by using powerful yet very simple tools which are also very easy to use in real world, I enjoyed it a lot.

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How To Solve Complex Problems

In today’s increasingly complex world, we are constantly faced with ill-defined problems that don’t have a clear solution. From poverty and climate change to crime and addiction, complex situations surround us. Unlike simple problems with a pre-defined or “right” answer, complex problems share several basic characteristics that make them hard to solve. While these problems can be frustrating and overwhelming, they also offer an opportunity for growth and creativity. Complex problem-solving skills are the key to addressing these tough issues.

In this article, I will discuss simple versus complex problems, define complex problem solving, and describe why it is so important in complex dynamic environments. I will also explain how to develop problem-solving skills and share some tips for effectively solving complex problems.

How is simple problem-solving different from complex problem-solving?

Solving problems is about getting from a currently undesirable state to an intended goal state. In other words, about bridging the gap between “what is” and “what ought to be”. However, the challenge of reaching a solution varies based on the kind of problem that is being solved. There are generally three different kinds of problems you should consider.

Simple problems have one problem solution. The goal is to find that answer as quickly and efficiently as possible. Puzzles are classic examples of simple problem solving. The objective is to find the one correct solution out of many possibilities.

Problems are different from puzzles in that they don’t have a known problem solution. As such, many people may agree that there is an issue to be solved, but they may not agree on the intended goal state or how to get there. In this type of problem, people spend a lot of time debating the best solution and the optimal way to achieve it.

Messes are collections of interrelated problems where many stakeholders may not even agree on what the issue is. Unlike problems where there is agreement about what the problem is, in messes, there isn’t agreement amongst stakeholders. In other words, even “what is” can’t be taken for granted. Most complex social problems are messes, made up of interrelated social issues with ill-defined boundaries and goals.

Problems and messes can be complicated or complex

Puzzles are simple, but problems and messes exist on a continuum between complicated and complex. Complicated problems are technical in nature. There may be many involved variables, but the relationships are linear. As a result, complicated problems have step-by-step, systematic solutions. Repairing an engine or building a rocket may be difficult because of the many parts involved, but it is a technical problem we call complicated.

On the other hand, solving a complex problem is entirely different. Unlike complicated problems that may have many variables with linear relationships, a complex problem is characterized by connectivity patterns that are harder to understand and predict.

Characteristics of complex problems and messes

So what else makes a problem complex? Here are seven additional characteristics (from Funke and Hester and Adams ).

• Lack of information. There is often a lack of data or information about the problem itself. In some cases, variables are unknown or cannot be measured.
• Many goals. A complex problem has a mix of conflicting objectives. In some sense, every stakeholder involved with the problem may have their own goals. However, with limited resources, not all goals can be simultaneously satisfied.
• Unpredictable feedback loops. In part due to many variables connected by a range of different relationships, a change in one variable is likely to have effects on other variables in the system. However, because we do not know all of the variables it will affect, small changes can have disproportionate system-wide effects. These unexpected events that have big, unpredictable effects are sometimes called Black Swans.
• Dynamic. A complex problem changes over time and there is a significant impact based on when you act. In other words, because the problem and its parts and relationships are constantly changing, an action taken today won’t have the same effects as the same action taken tomorrow.
• Time-delayed. It takes a while for cause and effect to be realized. Thus it is very hard to know if any given intervention is working.
• Unknown unknowns. Building off the previous point about a lack of information, in a complex problem you may not even know what you don’t know. In other words, there may be very important variables that you are not even aware of.
• Affected by (error-prone) humans. Simply put, human behavior tends to be illogical and unpredictable. When humans are involved in a problem, avoiding error may be impossible.

What is complex problem-solving?

“Complex problem solving” is the term for how to address a complex problem or messes that have the characteristics listed above.

Since a complex problem is a different phenomenon than a simple or complicated problem, solving them requires a different approach. Methods designed for simple problems, like systematic organization, deductive logic, and linear thinking don’t work well on their own for a complex problem.

And yet, despite its importance, there isn’t complete agreement about what exactly it is.

How is complex problem solving defined by experts?

Let’s look at what scientists, researchers, and system thinkers have come up with in terms of a definition for solving a complex problem. 

As a series of observations and informed decisions

For many employers, the focus is on making smart decisions. These must weigh the future effects to the company of any given solution. According to Indeed.com , it is defined as “a series of observations and informed decisions used to find and implement a solution to a problem. Beyond finding and implementing a solution, complex problem solving also involves considering future changes to circumstance, resources, and capabilities that may affect the trajectory of the process and success of the solution. Complex problem solving also involves considering the impact of the solution on the surrounding environment and individuals.”

As using information to review options and develop solutions

For others, it is more of a systematic way to consider a range of options. According to O*NET ,  the definition focuses on “identifying complex problems and reviewing related information to develop and evaluate options and implement solutions.”

As a self-regulated psychological process

Others emphasize the broad range of skills and emotions needed for change. In addition, they endorse an inspired kind of pragmatism. For example, Dietrich Dorner and Joachim Funke define it as “a collection of self-regulated psychological processes and activities necessary in dynamic environments to achieve ill-defined goals that cannot be reached by routine actions. Creative combinations of knowledge and a broad set of strategies are needed. Solutions are often more bricolage than perfect or optimal. The problem-solving process combines cognitive, emotional, and motivational aspects, particularly in high-stakes situations. Complex problems usually involve knowledge-rich requirements and collaboration among different persons.”

As a novel way of thinking and reasoning

Finally, some emphasize the multidisciplinary nature of knowledge and processes needed to tackle a complex problem. Patrick Hester and Kevin MacG. Adams have stated that “no single discipline can solve truly complex problems. Problems of real interest, those vexing ones that keep you up at night, require a discipline-agnostic approach…Simply they require us to think systemically about our problem…a novel way of thinking and reasoning about complex problems that encourages increased understanding and deliberate intervention.”

A synthesis definition

By pulling the main themes of these definitions together, we can get a sense of what complex problem-solvers must do:

Gain a better understanding of the phenomena of a complex problem or mess. Use a discipline-agnostic approach in order to develop deliberate interventions. Take into consideration future impacts on the surrounding environment.

Why is complex problem solving important?

Many efforts aimed at complex social problems like reducing homelessness and improving public health – despite good intentions giving more effort than ever before – are destined to fail because their approach is based on simple problem-solving. And some efforts might even unwittingly be contributing to the problems they’re trying to solve. 

Einstein said that “We can’t solve problems by using the same kind of thinking we used when we created them.” I think he could have easily been alluding to the need for more complex problem solvers who think differently. So what skills are required to do this?

What are complex problem-solving skills?

The skills required to solve a complex problem aren’t from one domain, nor are they an easily-packaged bundle. Rather, I like to think of them as a balancing act between a series of seemingly opposite approaches but synthesized. This brings a sort of cognitive dissonance into the process, which is itself informative.

It brings F. Scott Fitzgerald’s maxim to mind: 

“The test of a first-rate intelligence is the ability to hold two opposing ideas in mind at the same time and still retain the ability to function. One should, for example, be able to see that things are hopeless yet be determined to make them otherwise.” 

To see the problem situation clearly, for example, but also with a sense of optimism and possibility.

Here are the top three dialectics to keep in mind:

Thinking and reasoning

Reasoning is the ability to make logical deductions based on evidence and counterevidence. On the other hand, thinking is more about imagining an unknown reality based on thoughts about the whole picture and how the parts could fit together. By thinking clearly, one can have a sense of possibility that prepares the mind to deduce the right action in the unique moment at hand.

As Dorner and Funke explain: “Not every situation requires the same action,  and we may want to act this way or another to reach this or that goal. This appears logical, but it is a logic based on constantly shifting grounds: We cannot know whether necessary conditions are met, sometimes the assumptions we have made later turn out to be incorrect, and sometimes we have to revise our assumptions or make completely new ones. It is necessary to constantly switch between our sense of possibility and our sense of reality, that is, to switch between thinking and reasoning. It is an arduous process, and some people handle it well, while others do not.”

Analysis and reductionism combined with synthesis and holism

It’s important to be able to use scientific processes to break down a complex problem into its parts and analyze them. But at the same time, a complex problem is more than the sum of its parts. In most cases, the relationships between the parts are more important than the parts themselves. Therefore, decomposing problems with rigor isn’t enough. What’s needed, once problems are reduced and understood, is a way of understanding the relationships between various components as well as putting the pieces back together. However, synthesis and holism on their own without deductive analysis can often miss details and relationships that matter.  

What makes this balancing act more difficult is that certain professions tend to be trained in and prefer one domain over the other. Scientists prefer analysis and reductionism whereas most social scientists and practitioners default to synthesis and holism. Unfortunately, this divide of preferences results in people working in their silos at the expense of multi-disciplinary approaches that together can better “see” complexity.

Situational awareness and self-awareness 

Dual awareness is the ability to pay attention to two experiences simultaneously. In the case of complex problems, context really matters. In other words, problem-solving exists in an ecosystem of environmental factors that are not incidental. Personal and cultural preferences play a part as do current events unfolding over time. But as a problem solver, knowing the environment is only part of the equation. 

The other crucial part is the internal psychological process unique to every individual who also interacts with the problem and the environment. Problem solvers inevitably come into contact with others who may disagree with them, or be advancing seemingly counterproductive solutions, and these interactions result in emotions and motivations. Without self-awareness, we can become attached to our own subjective opinions, fall in love with “our” solutions, and generally be driven by the desire to be seen as problem solvers at the expense of actually solving the problem.

By balancing these three dialectics, practitioners can better deal with uncertainty as well as stay motivated despite setbacks. Self-regulation among these seemingly opposite approaches also reminds one to stay open-minded.

How do you develop complex problem-solving skills?

There is no one answer to this question, as the best way to develop them will vary depending on your strengths and weaknesses. However, there are a few general things that you can do to improve your ability to solve problems.

Ground yourself in theory and knowledge

First, it is important to learn about systems thinking and complexity theories. These frameworks will help you understand how complex systems work, and how different parts of a system interact with each other. This conceptual understanding will allow you to identify potential solutions to problems more quickly and effectively.

Practice switching between approaches

Second, practice switching between the dialectics mentioned above. For example, in your next meeting try to spend roughly half your time thinking and half your time reasoning. The important part is trying to get habituated to regularly switching lenses. It may seem disjointed at first, but after a while, it becomes second nature to simultaneously see how the parts interact and the big picture.

Focus on the specific problem phenomena

Third, it may sound obvious, but people often don’t spend very much time studying the problem itself and how it functions. In some sense, becoming a good problem-solver involves becoming a problem scientist. Your time should be spent regularly investigating the phenomena of “what is” rather than “what ought to be”. A holistic understanding of the problem is the required prerequisite to coming up with good solutions.

Stay curious

Finally, after we have worked on a problem for a while, we tend to think we know everything about it, including how to solve it. Even if we’re working on a problem, which may change dynamically from day to day, we start treating it more like a puzzle with a definite solution. When that happens, we can lose our motivation to continue learning about the problem. This is very risky because it closes the door to learning from others, regardless of whether we completely agree with them or not.

As Neils Bohr said, “Two different perspectives or models about a system will reveal truths regarding the system that are neither entirely independent nor entirely compatible.”

By staying curious, we can retain our ability to learn on a daily basis.

Tips for how to solve complex problems

Focus on processes over results.

It’s easy to get lost in utopian thinking. Many people spend so much time on “what ought to be” that they forget that problem solving is about the gap between “what is” and “what ought to be”. It is said that “life is a journey, not a destination.” The same is true for complex problem-solving. To do it well, a problem solver must focus on enjoying the process of gaining a holistic understanding of the problem. 

Adaptive and iterative methods and tools

A variety of adaptive and iterative methods have been developed to address complexity. They share a laser focus on gaining holistic understanding with tools that best match the phenomena of complexity. They are also non-ideological, trans-disciplinary, and flexible. In most cases, your journey through a set of steps won’t be linear. Rather, as you think and reason, analyze and synthesize, you’ll jump around to get a holistic picture.

In my online course , we generally follow a seven-step method:

• Get clear sight with a complex problem-solving frame
• Establish a secure base of operation
• Gain a deep understanding of the problem
• Create an interactive model of the problem
• Develop an impact strategy
• Create an action plan and implement
• Embed systemic solutions

Of course, each of these steps involves testing to see what works and consistently evaluating our process and progress.

Resolution is about systematically managing a problem over time

One last thing to keep in mind. Most social problems are not just solved one day, never to return. In reality,  most complex problems are managed, not solved. For all practical purposes, what this means is that “the solution” is a way of systematically dealing with the problem over time. Some find this disappointing, but it’s actually a pragmatic pointer to think about resolution – a way move problems in the right direction – rather than final solutions.

Problem solvers regularly train and practice

If you need help developing your complex problem-solving skills, I have an online class where you can learn everything you need to know. 

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Solving Complex Problems: Structured Thinking, Design Principles, and AI

Sang-Gook Kim

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How do you solve important, large-scale challenges with evolving and contradictory constraints? In this 5-day course, transform your approach to large-scale problem solving, from multi-stakeholder engineering projects to the online spread of misinformation. Alongside engineers and leaders from diverse industries, you’ll explore actionable innovative frameworks for assessing, communicating, and implementing complex systems—and significantly increase your likelihood of success.

THIS COURSE MAY BE TAKEN INDIVIDUALLY OR AS PART OF THE  PROFESSIONAL CERTIFICATE PROGRAM IN INNOVATION & TECHNOLOGY  OR THE  PROFESSIONAL CERTIFICATE PROGRAM IN DESIGN & MANUFACTURING .

Engineering projects with shifting goals. Inefficient national healthcare systems. The online spread of misinformation. Every day, professionals are tasked with addressing major challenges that present opportunities for great triumph—or significant failure. How do you approach an important, large-scale challenge with evolving and contradictory constraints? Is the solution a new technology, a new policy, or something else altogether? In our new course Solving Complex Problems: Structured Thinking, Design Principles, and AI , you’ll acquire core principles that will change the way you approach and solve large-scale challenges—increasing your likelihood of success. Over the course of five days, you will explore proven design principles, heuristic-based insights, and problem-solving approaches, and learn how to persuasively present concepts and system architectures to stakeholders. Methods utilize recent developments in AI and Big Data, as well as innovative strategies from MIT Lincoln Laboratory that have been successfully applied to large and complex national defense systems. By taking part in interactive lectures and hands-on projects, you will learn to think through and leverage important steps, including problem abstraction, idea generation, concept development and refinement, system-level thinking, and proposal generation. Alongside an accomplished group of global peers, you will explore the strategies and frameworks you need to implement large-scale systems that can have a significant positive impact—and minimize the probability of failure.

Certificate of Completion from MIT Professional Education  

• Approach and solve large and complex problems.
• Assess end-to-end processes and associated challenges, in order to significantly increase the likelihood of success in developing more complex systems.
• Implement effective problem-solving techniques, including abstracting the problem, idea generation, concept development and refinement, system-level thinking, and proposal generation.
• Utilize system-level thinking skills to evaluate, refine, down select, and evaluate best ideas and concepts.
• Apply the Axiomatic Design methodology to a broad range of applications in manufacturing, product design, software, and architecture.
• Generate and present proposals that clearly articulate innovative ideas, clarify the limits of current strategies, define potential customers and impact, and outline a success-oriented system development and risk mitigation plan.
• Effectively communicate ideas and persuade others, and provide valuable feedback.
• Confidently develop and execute large-scale system concepts that will drive significant positive impact.

Edwin F. David Head of the Engineering Division, MIT Lincoln Laboratory

Jonathan E. Gans Group Leader of the Systems and Architectures Group, MIT Lincoln Laboratory

Robert T-I. Shin Principal Staff in the Intelligence, Surveillance, and Reconnaissance (ISR) and Tactical Systems Division, MIT Lincoln Laboratory Director, MIT Beaver Works

This course is appropriate for professionals who design or manage complex systems with shifting needs and goals. It is also well suited to those who want to improve the quality and performance of their operations and decision-making in a large-scale system environment. Potential participants include engineers, group leaders, and senior managers in government and industries including automotive, aerospace, semiconductors, engineering, manufacturing, healthcare, bio-medical, finance, architecture, public policy, education, and military.

Computer Requirements

A laptop with PowerPoint is required.

CONCEPTUAL ANALYSIS article

Complex problem solving: what it is and what it is not.

• 1 Department of Psychology, University of Bamberg, Bamberg, Germany
• 2 Department of Psychology, Heidelberg University, Heidelberg, Germany

Computer-simulated scenarios have been part of psychological research on problem solving for more than 40 years. The shift in emphasis from simple toy problems to complex, more real-life oriented problems has been accompanied by discussions about the best ways to assess the process of solving complex problems. Psychometric issues such as reliable assessments and addressing correlations with other instruments have been in the foreground of these discussions and have left the content validity of complex problem solving in the background. In this paper, we return the focus to content issues and address the important features that define complex problems.

Succeeding in the 21st century requires many competencies, including creativity, life-long learning, and collaboration skills (e.g., National Research Council, 2011 ; Griffin and Care, 2015 ), to name only a few. One competence that seems to be of central importance is the ability to solve complex problems ( Mainzer, 2009 ). Mainzer quotes the Nobel prize winner Simon (1957) who wrote as early as 1957:

The capacity of the human mind for formulating and solving complex problems is very small compared with the size of the problem whose solution is required for objectively rational behavior in the real world or even for a reasonable approximation to such objective rationality. (p. 198)

The shift from well-defined to ill-defined problems came about as a result of a disillusion with the “general problem solver” ( Newell et al., 1959 ): The general problem solver was a computer software intended to solve all kind of problems that can be expressed through well-formed formulas. However, it soon became clear that this procedure was in fact a “special problem solver” that could only solve well-defined problems in a closed space. But real-world problems feature open boundaries and have no well-determined solution. In fact, the world is full of wicked problems and clumsy solutions ( Verweij and Thompson, 2006 ). As a result, solving well-defined problems and solving ill-defined problems requires different cognitive processes ( Schraw et al., 1995 ; but see Funke, 2010 ).

Well-defined problems have a clear set of means for reaching a precisely described goal state. For example: in a match-stick arithmetic problem, a person receives a false arithmetic expression constructed out of matchsticks (e.g., IV = III + III). According to the instructions, moving one of the matchsticks will make the equations true. Here, both the problem (find the appropriate stick to move) and the goal state (true arithmetic expression; solution is: VI = III + III) are defined clearly.

Ill-defined problems have no clear problem definition, their goal state is not defined clearly, and the means of moving towards the (diffusely described) goal state are not clear. For example: The goal state for solving the political conflict in the near-east conflict between Israel and Palestine is not clearly defined (living in peaceful harmony with each other?) and even if the conflict parties would agree on a two-state solution, this goal again leaves many issues unresolved. This type of problem is called a “complex problem” and is of central importance to this paper. All psychological processes that occur within individual persons and deal with the handling of such ill-defined complex problems will be subsumed under the umbrella term “complex problem solving” (CPS).

Systematic research on CPS started in the 1970s with observations of the behavior of participants who were confronted with computer simulated microworlds. For example, in one of those microworlds participants assumed the role of executives who were tasked to manage a company over a certain period of time (see Brehmer and Dörner, 1993 , for a discussion of this methodology). Today, CPS is an established concept and has even influenced large-scale assessments such as PISA (“Programme for International Student Assessment”), organized by the Organization for Economic Cooperation and Development ( OECD, 2014 ). According to the World Economic Forum, CPS is one of the most important competencies required in the future ( World Economic Forum, 2015 ). Numerous articles on the subject have been published in recent years, documenting the increasing research activity relating to this field. In the following collection of papers we list only those published in 2010 and later: theoretical papers ( Blech and Funke, 2010 ; Funke, 2010 ; Knauff and Wolf, 2010 ; Leutner et al., 2012 ; Selten et al., 2012 ; Wüstenberg et al., 2012 ; Greiff et al., 2013b ; Fischer and Neubert, 2015 ; Schoppek and Fischer, 2015 ), papers about measurement issues ( Danner et al., 2011a ; Greiff et al., 2012 , 2015a ; Alison et al., 2013 ; Gobert et al., 2015 ; Greiff and Fischer, 2013 ; Herde et al., 2016 ; Stadler et al., 2016 ), papers about applications ( Fischer and Neubert, 2015 ; Ederer et al., 2016 ; Tremblay et al., 2017 ), papers about differential effects ( Barth and Funke, 2010 ; Danner et al., 2011b ; Beckmann and Goode, 2014 ; Greiff and Neubert, 2014 ; Scherer et al., 2015 ; Meißner et al., 2016 ; Wüstenberg et al., 2016 ), one paper about developmental effects ( Frischkorn et al., 2014 ), one paper with a neuroscience background ( Osman, 2012 ) 1 , papers about cultural differences ( Güss and Dörner, 2011 ; Sonnleitner et al., 2014 ; Güss et al., 2015 ), papers about validity issues ( Goode and Beckmann, 2010 ; Greiff et al., 2013c ; Schweizer et al., 2013 ; Mainert et al., 2015 ; Funke et al., 2017 ; Greiff et al., 2017 , 2015b ; Kretzschmar et al., 2016 ; Kretzschmar, 2017 ), review papers and meta-analyses ( Osman, 2010 ; Stadler et al., 2015 ), and finally books ( Qudrat-Ullah, 2015 ; Csapó and Funke, 2017b ) and book chapters ( Funke, 2012 ; Hotaling et al., 2015 ; Funke and Greiff, 2017 ; Greiff and Funke, 2017 ; Csapó and Funke, 2017a ; Fischer et al., 2017 ; Molnàr et al., 2017 ; Tobinski and Fritz, 2017 ; Viehrig et al., 2017 ). In addition, a new “Journal of Dynamic Decision Making” (JDDM) has been launched ( Fischer et al., 2015 , 2016 ) to give the field an open-access outlet for research and discussion.

This paper aims to clarify aspects of validity: what should be meant by the term CPS and what not? This clarification seems necessary because misunderstandings in recent publications provide – from our point of view – a potentially misleading picture of the construct. We start this article with a historical review before attempting to systematize different positions. We conclude with a working definition.

Historical Review

The concept behind CPS goes back to the German phrase “komplexes Problemlösen” (CPS; the term “komplexes Problemlösen” was used as a book title by Funke, 1986 ). The concept was introduced in Germany by Dörner and colleagues in the mid-1970s (see Dörner et al., 1975 ; Dörner, 1975 ) for the first time. The German phrase was later translated to CPS in the titles of two edited volumes by Sternberg and Frensch (1991) and Frensch and Funke (1995a) that collected papers from different research traditions. Even though it looks as though the term was coined in the 1970s, Edwards (1962) used the term “dynamic decision making” to describe decisions that come in a sequence. He compared static with dynamic decision making, writing:

In dynamic situations, a new complication not found in the static situations arises. The environment in which the decision is set may be changing, either as a function of the sequence of decisions, or independently of them, or both. It is this possibility of an environment which changes while you collect information about it which makes the task of dynamic decision theory so difficult and so much fun. (p. 60)

The ability to solve complex problems is typically measured via dynamic systems that contain several interrelated variables that participants need to alter. Early work (see, e.g., Dörner, 1980 ) used a simulation scenario called “Lohhausen” that contained more than 2000 variables that represented the activities of a small town: Participants had to take over the role of a mayor for a simulated period of 10 years. The simulation condensed these ten years to ten hours in real time. Later, researchers used smaller dynamic systems as scenarios either based on linear equations (see, e.g., Funke, 1993 ) or on finite state automata (see, e.g., Buchner and Funke, 1993 ). In these contexts, CPS consisted of the identification and control of dynamic task environments that were previously unknown to the participants. Different task environments came along with different degrees of fidelity ( Gray, 2002 ).

According to Funke (2012) , the typical attributes of complex systems are (a) complexity of the problem situation which is usually represented by the sheer number of involved variables; (b) connectivity and mutual dependencies between involved variables; (c) dynamics of the situation, which reflects the role of time and developments within a system; (d) intransparency (in part or full) about the involved variables and their current values; and (e) polytely (greek term for “many goals”), representing goal conflicts on different levels of analysis. This mixture of features is similar to what is called VUCA (volatility, uncertainty, complexity, ambiguity) in modern approaches to management (e.g., Mack et al., 2016 ).

In his evaluation of the CPS movement, Sternberg (1995) compared (young) European approaches to CPS with (older) American research on expertise. His analysis of the differences between the European and American traditions shows advantages but also potential drawbacks for each side. He states (p. 301): “I believe that although there are problems with the European approach, it deals with some fundamental questions that American research scarcely addresses.” So, even though the echo of the European approach did not enjoy strong resonance in the US at that time, it was valued by scholars like Sternberg and others. Before attending to validity issues, we will first present a short review of different streams.

Different Approaches to CPS

In the short history of CPS research, different approaches can be identified ( Buchner, 1995 ; Fischer et al., 2017 ). To systematize, we differentiate between the following five lines of research:

(a) The search for individual differences comprises studies identifying interindividual differences that affect the ability to solve complex problems. This line of research is reflected, for example, in the early work by Dörner et al. (1983) and their “Lohhausen” study. Here, naïve student participants took over the role of the mayor of a small simulated town named Lohhausen for a simulation period of ten years. According to the results of the authors, it is not intelligence (as measured by conventional IQ tests) that predicts performance, but it is the ability to stay calm in the face of a challenging situation and the ability to switch easily between an analytic mode of processing and a more holistic one.

(b) The search for cognitive processes deals with the processes behind understanding complex dynamic systems. Representative of this line of research is, for example, Berry and Broadbent’s (1984) work on implicit and explicit learning processes when people interact with a dynamic system called “Sugar Production”. They found that those who perform best in controlling a dynamic system can do so implicitly, without explicit knowledge of details regarding the systems’ relations.

(c) The search for system factors seeks to identify the aspects of dynamic systems that determine the difficulty of complex problems and make some problems harder than others. Representative of this line of research is, for example, work by Funke (1985) , who systematically varied the number of causal effects within a dynamic system or the presence/absence of eigendynamics. He found, for example, that solution quality decreases as the number of systems relations increases.

(d) The psychometric approach develops measurement instruments that can be used as an alternative to classical IQ tests, as something that goes “beyond IQ”. The MicroDYN approach ( Wüstenberg et al., 2012 ) is representative for this line of research that presents an alternative to reasoning tests (like Raven matrices). These authors demonstrated that a small improvement in predicting school grade point average beyond reasoning is possible with MicroDYN tests.

(e) The experimental approach explores CPS under different experimental conditions. This approach uses CPS assessment instruments to test hypotheses derived from psychological theories and is sometimes used in research about cognitive processes (see above). Exemplary for this line of research is the work by Rohe et al. (2016) , who test the usefulness of “motto goals” in the context of complex problems compared to more traditional learning and performance goals. Motto goals differ from pure performance goals by activating positive affect and should lead to better goal attainment especially in complex situations (the mentioned study found no effect).

To be clear: these five approaches are not mutually exclusive and do overlap. But the differentiation helps to identify different research communities and different traditions. These communities had different opinions about scaling complexity.

The Race for Complexity: Use of More and More Complex Systems

In the early years of CPS research, microworlds started with systems containing about 20 variables (“Tailorshop”), soon reached 60 variables (“Moro”), and culminated in systems with about 2000 variables (“Lohhausen”). This race for complexity ended with the introduction of the concept of “minimal complex systems” (MCS; Greiff and Funke, 2009 ; Funke and Greiff, 2017 ), which ushered in a search for the lower bound of complexity instead of the higher bound, which could not be defined as easily. The idea behind this concept was that whereas the upper limits of complexity are unbound, the lower limits might be identifiable. Imagine starting with a simple system containing two variables with a simple linear connection between them; then, step by step, increase the number of variables and/or the type of connections. One soon reaches a point where the system can no longer be considered simple and has become a “complex system”. This point represents a minimal complex system. Despite some research having been conducted in this direction, the point of transition from simple to complex has not been identified clearly as of yet.

Some years later, the original “minimal complex systems” approach ( Greiff and Funke, 2009 ) shifted to the “multiple complex systems” approach ( Greiff et al., 2013a ). This shift is more than a slight change in wording: it is important because it taps into the issue of validity directly. Minimal complex systems have been introduced in the context of challenges from large-scale assessments like PISA 2012 that measure new aspects of problem solving, namely interactive problems besides static problem solving ( Greiff and Funke, 2017 ). PISA 2012 required test developers to remain within testing time constraints (given by the school class schedule). Also, test developers needed a large item pool for the construction of a broad class of problem solving items. It was clear from the beginning that MCS deal with simple dynamic situations that require controlled interaction: the exploration and control of simple ticket machines, simple mobile phones, or simple MP3 players (all of these example domains were developed within PISA 2012) – rather than really complex situations like managerial or political decision making.

As a consequence of this subtle but important shift in interpreting the letters MCS, the definition of CPS became a subject of debate recently ( Funke, 2014a ; Greiff and Martin, 2014 ; Funke et al., 2017 ). In the words of Funke (2014b , p. 495):

It is funny that problems that nowadays come under the term ‘CPS’, are less complex (in terms of the previously described attributes of complex situations) than at the beginning of this new research tradition. The emphasis on psychometric qualities has led to a loss of variety. Systems thinking requires more than analyzing models with two or three linear equations – nonlinearity, cyclicity, rebound effects, etc. are inherent features of complex problems and should show up at least in some of the problems used for research and assessment purposes. Minimal complex systems run the danger of becoming minimal valid systems.

Searching for minimal complex systems is not the same as gaining insight into the way how humans deal with complexity and uncertainty. For psychometric purposes, it is appropriate to reduce complexity to a minimum; for understanding problem solving under conditions of overload, intransparency, and dynamics, it is necessary to realize those attributes with reasonable strength. This aspect is illustrated in the next section.

Importance of the Validity Issue

The most important reason for discussing the question of what complex problem solving is and what it is not stems from its phenomenology: if we lose sight of our phenomena, we are no longer doing good psychology. The relevant phenomena in the context of complex problems encompass many important aspects. In this section, we discuss four phenomena that are specific to complex problems. We consider these phenomena as critical for theory development and for the construction of assessment instruments (i.e., microworlds). These phenomena require theories for explaining them and they require assessment instruments eliciting them in a reliable way.

The first phenomenon is the emergency reaction of the intellectual system ( Dörner, 1980 ): When dealing with complex systems, actors tend to (a) reduce their intellectual level by decreasing self-reflections, by decreasing their intentions, by stereotyping, and by reducing their realization of intentions, (b) they show a tendency for fast action with increased readiness for risk, with increased violations of rules, and with increased tendency to escape the situation, and (c) they degenerate their hypotheses formation by construction of more global hypotheses and reduced tests of hypotheses, by increasing entrenchment, and by decontextualizing their goals. This phenomenon illustrates the strong connection between cognition, emotion, and motivation that has been emphasized by Dörner (see, e.g., Dörner and Güss, 2013 ) from the beginning of his research tradition; the emergency reaction reveals a shift in the mode of information processing under the pressure of complexity.

The second phenomenon comprises cross-cultural differences with respect to strategy use ( Strohschneider and Güss, 1999 ; Güss and Wiley, 2007 ; Güss et al., 2015 ). Results from complex task environments illustrate the strong influence of context and background knowledge to an extent that cannot be found for knowledge-poor problems. For example, in a comparison between Brazilian and German participants, it turned out that Brazilians accept the given problem descriptions and are more optimistic about the results of their efforts, whereas Germans tend to inquire more about the background of the problems and take a more active approach but are less optimistic (according to Strohschneider and Güss, 1998 , p. 695).

The third phenomenon relates to failures that occur during the planning and acting stages ( Jansson, 1994 ; Ramnarayan et al., 1997 ), illustrating that rational procedures seem to be unlikely to be used in complex situations. The potential for failures ( Dörner, 1996 ) rises with the complexity of the problem. Jansson (1994) presents seven major areas for failures with complex situations: acting directly on current feedback; insufficient systematization; insufficient control of hypotheses and strategies; lack of self-reflection; selective information gathering; selective decision making; and thematic vagabonding.

The fourth phenomenon describes (a lack of) training and transfer effects ( Kretzschmar and Süß, 2015 ), which again illustrates the context dependency of strategies and knowledge (i.e., there is no strategy that is so universal that it can be used in many different problem situations). In their own experiment, the authors could show training effects only for knowledge acquisition, not for knowledge application. Only with specific feedback, performance in complex environments can be increased ( Engelhart et al., 2017 ).

These four phenomena illustrate why the type of complexity (or degree of simplicity) used in research really matters. Furthermore, they demonstrate effects that are specific for complex problems, but not for toy problems. These phenomena direct the attention to the important question: does the stimulus material used (i.e., the computer-simulated microworld) tap and elicit the manifold of phenomena described above?

Dealing with partly unknown complex systems requires courage, wisdom, knowledge, grit, and creativity. In creativity research, “little c” and “BIG C” are used to differentiate between everyday creativity and eminent creativity ( Beghetto and Kaufman, 2007 ; Kaufman and Beghetto, 2009 ). Everyday creativity is important for solving everyday problems (e.g., finding a clever fix for a broken spoke on my bicycle), eminent creativity changes the world (e.g., inventing solar cells for energy production). Maybe problem solving research should use a similar differentiation between “little p” and “BIG P” to mark toy problems on the one side and big societal challenges on the other. The question then remains: what can we learn about BIG P by studying little p? What phenomena are present in both types, and what phenomena are unique to each of the two extremes?

Discussing research on CPS requires reflecting on the field’s research methods. Even if the experimental approach has been successful for testing hypotheses (for an overview of older work, see Funke, 1995 ), other methods might provide additional and novel insights. Complex phenomena require complex approaches to understand them. The complex nature of complex systems imposes limitations on psychological experiments: The more complex the environments, the more difficult is it to keep conditions under experimental control. And if experiments have to be run in labs one should bring enough complexity into the lab to establish the phenomena mentioned, at least in part.

There are interesting options to be explored (again): think-aloud protocols , which have been discredited for many years ( Nisbett and Wilson, 1977 ) and yet are a valuable source for theory testing ( Ericsson and Simon, 1983 ); introspection ( Jäkel and Schreiber, 2013 ), which seems to be banned from psychological methods but nevertheless offers insights into thought processes; the use of life-streaming ( Wendt, 2017 ), a medium in which streamers generate a video stream of think-aloud data in computer-gaming; political decision-making ( Dhami et al., 2015 ) that demonstrates error-proneness in groups; historical case studies ( Dörner and Güss, 2011 ) that give insights into the thinking styles of political leaders; the use of the critical incident technique ( Reuschenbach, 2008 ) to construct complex scenarios; and simulations with different degrees of fidelity ( Gray, 2002 ).

The methods tool box is full of instruments that have to be explored more carefully before any individual instrument receives a ban or research narrows its focus to only one paradigm for data collection. Brehmer and Dörner (1993) discussed the tensions between “research in the laboratory and research in the field”, optimistically concluding “that the new methodology of computer-simulated microworlds will provide us with the means to bridge the gap between the laboratory and the field” (p. 183). The idea behind this optimism was that computer-simulated scenarios would bring more complexity from the outside world into the controlled lab environment. But this is not true for all simulated scenarios. In his paper on simulated environments, Gray (2002) differentiated computer-simulated environments with respect to three dimensions: (1) tractability (“the more training subjects require before they can use a simulated task environment, the less tractable it is”, p. 211), correspondence (“High correspondence simulated task environments simulate many aspects of one task environment. Low correspondence simulated task environments simulate one aspect of many task environments”, p. 214), and engagement (“A simulated task environment is engaging to the degree to which it involves and occupies the participants; that is, the degree to which they agree to take it seriously”, p. 217). But the mere fact that a task is called a “computer-simulated task environment” does not mean anything specific in terms of these three dimensions. This is one of several reasons why we should differentiate between those studies that do not address the core features of CPS and those that do.

What is not CPS?

Even though a growing number of references claiming to deal with complex problems exist (e.g., Greiff and Wüstenberg, 2015 ; Greiff et al., 2016 ), it would be better to label the requirements within these tasks “dynamic problem solving,” as it has been done adequately in earlier work ( Greiff et al., 2012 ). The dynamics behind on-off-switches ( Thimbleby, 2007 ) are remarkable but not really complex. Small nonlinear systems that exhibit stunningly complex and unstable behavior do exist – but they are not used in psychometric assessments of so-called CPS. There are other small systems (like MicroDYN scenarios: Greiff and Wüstenberg, 2014 ) that exhibit simple forms of system behavior that are completely predictable and stable. This type of simple systems is used frequently. It is even offered commercially as a complex problem-solving test called COMPRO ( Greiff and Wüstenberg, 2015 ) for business applications. But a closer look reveals that the label is not used correctly; within COMPRO, the used linear equations are far from being complex and the system can be handled properly by using only one strategy (see for more details Funke et al., 2017 ).

Why do simple linear systems not fall within CPS? At the surface, nonlinear and linear systems might appear similar because both only include 3–5 variables. But the difference is in terms of systems behavior as well as strategies and learning. If the behavior is simple (as in linear systems where more input is related to more output and vice versa), the system can be easily understood (participants in the MicroDYN world have 3 minutes to explore a complex system). If the behavior is complex (as in systems that contain strange attractors or negative feedback loops), things become more complicated and much more observation is needed to identify the hidden structure of the unknown system ( Berry and Broadbent, 1984 ; Hundertmark et al., 2015 ).

Another issue is learning. If tasks can be solved using a single (and not so complicated) strategy, steep learning curves are to be expected. The shift from problem solving to learned routine behavior occurs rapidly, as was demonstrated by Luchins (1942) . In his water jar experiments, participants quickly acquired a specific strategy (a mental set) for solving certain measurement problems that they later continued applying to problems that would have allowed for easier approaches. In the case of complex systems, learning can occur only on very general, abstract levels because it is difficult for human observers to make specific predictions. Routines dealing with complex systems are quite different from routines relating to linear systems.

What should not be studied under the label of CPS are pure learning effects, multiple-cue probability learning, or tasks that can be solved using a single strategy. This last issue is a problem for MicroDYN tasks that rely strongly on the VOTAT strategy (“vary one thing at a time”; see Tschirgi, 1980 ). In real-life, it is hard to imagine a business manager trying to solve her or his problems by means of VOTAT.

What is CPS?

In the early days of CPS research, planet Earth’s dynamics and complexities gained attention through such books as “The limits to growth” ( Meadows et al., 1972 ) and “Beyond the limits” ( Meadows et al., 1992 ). In the current decade, for example, the World Economic Forum (2016) attempts to identify the complexities and risks of our modern world. In order to understand the meaning of complexity and uncertainty, taking a look at the worlds’ most pressing issues is helpful. Searching for strategies to cope with these problems is a difficult task: surely there is no place for the simple principle of “vary-one-thing-at-a-time” (VOTAT) when it comes to global problems. The VOTAT strategy is helpful in the context of simple problems ( Wüstenberg et al., 2014 ); therefore, whether or not VOTAT is helpful in a given problem situation helps us distinguish simple from complex problems.

Because there exist no clear-cut strategies for complex problems, typical failures occur when dealing with uncertainty ( Dörner, 1996 ; Güss et al., 2015 ). Ramnarayan et al. (1997) put together a list of generic errors (e.g., not developing adequate action plans; lack of background control; learning from experience blocked by stereotype knowledge; reactive instead of proactive action) that are typical of knowledge-rich complex systems but cannot be found in simple problems.

Complex problem solving is not a one-dimensional, low-level construct. On the contrary, CPS is a multi-dimensional bundle of competencies existing at a high level of abstraction, similar to intelligence (but going beyond IQ). As Funke et al. (2018) state: “Assessment of transversal (in educational contexts: cross-curricular) competencies cannot be done with one or two types of assessment. The plurality of skills and competencies requires a plurality of assessment instruments.”

There are at least three different aspects of complex systems that are part of our understanding of a complex system: (1) a complex system can be described at different levels of abstraction; (2) a complex system develops over time, has a history, a current state, and a (potentially unpredictable) future; (3) a complex system is knowledge-rich and activates a large semantic network, together with a broad list of potential strategies (domain-specific as well as domain-general).

Complex problem solving is not only a cognitive process but is also an emotional one ( Spering et al., 2005 ; Barth and Funke, 2010 ) and strongly dependent on motivation (low-stakes versus high-stakes testing; see Hermes and Stelling, 2016 ).

Furthermore, CPS is a dynamic process unfolding over time, with different phases and with more differentiation than simply knowledge acquisition and knowledge application. Ideally, the process should entail identifying problems (see Dillon, 1982 ; Lee and Cho, 2007 ), even if in experimental settings, problems are provided to participants a priori . The more complex and open a given situation, the more options can be generated (T. S. Schweizer et al., 2016 ). In closed problems, these processes do not occur in the same way.

In analogy to the difference between formative (process-oriented) and summative (result-oriented) assessment ( Wiliam and Black, 1996 ; Bennett, 2011 ), CPS should not be reduced to the mere outcome of a solution process. The process leading up to the solution, including detours and errors made along the way, might provide a more differentiated impression of a person’s problem-solving abilities and competencies than the final result of such a process. This is one of the reasons why CPS environments are not, in fact, complex intelligence tests: research on CPS is not only about the outcome of the decision process, but it is also about the problem-solving process itself.

Complex problem solving is part of our daily life: finding the right person to share one’s life with, choosing a career that not only makes money, but that also makes us happy. Of course, CPS is not restricted to personal problems – life on Earth gives us many hard nuts to crack: climate change, population growth, the threat of war, the use and distribution of natural resources. In sum, many societal challenges can be seen as complex problems. To reduce that complexity to a one-hour lab activity on a random Friday afternoon puts it out of context and does not address CPS issues.

Theories about CPS should specify which populations they apply to. Across populations, one thing to consider is prior knowledge. CPS research with experts (e.g., Dew et al., 2009 ) is quite different from problem solving research using tasks that intentionally do not require any specific prior knowledge (see, e.g., Beckmann and Goode, 2014 ).

More than 20 years ago, Frensch and Funke (1995b) defined CPS as follows:

CPS occurs to overcome barriers between a given state and a desired goal state by means of behavioral and/or cognitive, multi-step activities. The given state, goal state, and barriers between given state and goal state are complex, change dynamically during problem solving, and are intransparent. The exact properties of the given state, goal state, and barriers are unknown to the solver at the outset. CPS implies the efficient interaction between a solver and the situational requirements of the task, and involves a solver’s cognitive, emotional, personal, and social abilities and knowledge. (p. 18)

The above definition is rather formal and does not account for content or relations between the simulation and the real world. In a sense, we need a new definition of CPS that addresses these issues. Based on our previous arguments, we propose the following working definition:

Complex problem solving is a collection of self-regulated psychological processes and activities necessary in dynamic environments to achieve ill-defined goals that cannot be reached by routine actions. Creative combinations of knowledge and a broad set of strategies are needed. Solutions are often more bricolage than perfect or optimal. The problem-solving process combines cognitive, emotional, and motivational aspects, particularly in high-stakes situations. Complex problems usually involve knowledge-rich requirements and collaboration among different persons.

The main differences to the older definition lie in the emphasis on (a) the self-regulation of processes, (b) creativity (as opposed to routine behavior), (c) the bricolage type of solution, and (d) the role of high-stakes challenges. Our new definition incorporates some aspects that have been discussed in this review but were not reflected in the 1995 definition, which focused on attributes of complex problems like dynamics or intransparency.

This leads us to the final reflection about the role of CPS for dealing with uncertainty and complexity in real life. We will distinguish thinking from reasoning and introduce the sense of possibility as an important aspect of validity.

CPS as Combining Reasoning and Thinking in an Uncertain Reality

Leading up to the Battle of Borodino in Leo Tolstoy’s novel “War and Peace”, Prince Andrei Bolkonsky explains the concept of war to his friend Pierre. Pierre expects war to resemble a game of chess: You position the troops and attempt to defeat your opponent by moving them in different directions.

“Far from it!”, Andrei responds. “In chess, you know the knight and his moves, you know the pawn and his combat strength. While in war, a battalion is sometimes stronger than a division and sometimes weaker than a company; it all depends on circumstances that can never be known. In war, you do not know the position of your enemy; some things you might be able to observe, some things you have to divine (but that depends on your ability to do so!) and many things cannot even be guessed at. In chess, you can see all of your opponent’s possible moves. In war, that is impossible. If you decide to attack, you cannot know whether the necessary conditions are met for you to succeed. Many a time, you cannot even know whether your troops will follow your orders…”

In essence, war is characterized by a high degree of uncertainty. A good commander (or politician) can add to that what he or she sees, tentatively fill in the blanks – and not just by means of logical deduction but also by intelligently bridging missing links. A bad commander extrapolates from what he sees and thus arrives at improper conclusions.

Many languages differentiate between two modes of mentalizing; for instance, the English language distinguishes between ‘thinking’ and ‘reasoning’. Reasoning denotes acute and exact mentalizing involving logical deductions. Such deductions are usually based on evidence and counterevidence. Thinking, however, is what is required to write novels. It is the construction of an initially unknown reality. But it is not a pipe dream, an unfounded process of fabrication. Rather, thinking asks us to imagine reality (“Wirklichkeitsfantasie”). In other words, a novelist has to possess a “sense of possibility” (“Möglichkeitssinn”, Robert Musil; in German, sense of possibility is often used synonymously with imagination even though imagination is not the same as sense of possibility, for imagination also encapsulates the impossible). This sense of possibility entails knowing the whole (or several wholes) or being able to construe an unknown whole that could accommodate a known part. The whole has to align with sociological and geographical givens, with the mentality of certain peoples or groups, and with the laws of physics and chemistry. Otherwise, the entire venture is ill-founded. A sense of possibility does not aim for the moon but imagines something that might be possible but has not been considered possible or even potentially possible so far.

Thinking is a means to eliminate uncertainty. This process requires both of the modes of thinking we have discussed thus far. Economic, political, or ecological decisions require us to first consider the situation at hand. Though certain situational aspects can be known, but many cannot. In fact, von Clausewitz (1832) posits that only about 25% of the necessary information is available when a military decision needs to be made. Even then, there is no way to guarantee that whatever information is available is also correct: Even if a piece of information was completely accurate yesterday, it might no longer apply today.

Once our sense of possibility has helped grasping a situation, problem solvers need to call on their reasoning skills. Not every situation requires the same action, and we may want to act this way or another to reach this or that goal. This appears logical, but it is a logic based on constantly shifting grounds: We cannot know whether necessary conditions are met, sometimes the assumptions we have made later turn out to be incorrect, and sometimes we have to revise our assumptions or make completely new ones. It is necessary to constantly switch between our sense of possibility and our sense of reality, that is, to switch between thinking and reasoning. It is an arduous process, and some people handle it well, while others do not.

If we are to believe Tuchman’s (1984) book, “The March of Folly”, most politicians and commanders are fools. According to Tuchman, not much has changed in the 3300 years that have elapsed since the misguided Trojans decided to welcome the left-behind wooden horse into their city that would end up dismantling Troy’s defensive walls. The Trojans, too, had been warned, but decided not to heed the warning. Although Laocoön had revealed the horse’s true nature to them by attacking it with a spear, making the weapons inside the horse ring, the Trojans refused to see the forest for the trees. They did not want to listen, they wanted the war to be over, and this desire ended up shaping their perception.

The objective of psychology is to predict and explain human actions and behavior as accurately as possible. However, thinking cannot be investigated by limiting its study to neatly confined fractions of reality such as the realms of propositional logic, chess, Go tasks, the Tower of Hanoi, and so forth. Within these systems, there is little need for a sense of possibility. But a sense of possibility – the ability to divine and construe an unknown reality – is at least as important as logical reasoning skills. Not researching the sense of possibility limits the validity of psychological research. All economic and political decision making draws upon this sense of possibility. By not exploring it, psychological research dedicated to the study of thinking cannot further the understanding of politicians’ competence and the reasons that underlie political mistakes. Christopher Clark identifies European diplomats’, politicians’, and commanders’ inability to form an accurate representation of reality as a reason for the outbreak of World War I. According to Clark’s (2012) book, “The Sleepwalkers”, the politicians of the time lived in their own make-believe world, wrongfully assuming that it was the same world everyone else inhabited. If CPS research wants to make significant contributions to the world, it has to acknowledge complexity and uncertainty as important aspects of it.

For more than 40 years, CPS has been a new subject of psychological research. During this time period, the initial emphasis on analyzing how humans deal with complex, dynamic, and uncertain situations has been lost. What is subsumed under the heading of CPS in modern research has lost the original complexities of real-life problems. From our point of view, the challenges of the 21st century require a return to the origins of this research tradition. We would encourage researchers in the field of problem solving to come back to the original ideas. There is enough complexity and uncertainty in the world to be studied. Improving our understanding of how humans deal with these global and pressing problems would be a worthwhile enterprise.

Author Contributions

JF drafted a first version of the manuscript, DD added further text and commented on the draft. JF finalized the manuscript.

Authors Note

After more than 40 years of controversial discussions between both authors, this is the first joint paper. We are happy to have done this now! We have found common ground!

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

The authors thank the Deutsche Forschungsgemeinschaft (DFG) for the continuous support of their research over many years. Thanks to Daniel Holt for his comments on validity issues, thanks to Julia Nolte who helped us by translating German text excerpts into readable English and helped us, together with Keri Hartman, to improve our style and grammar – thanks for that! We also thank the two reviewers for their helpful critical comments on earlier versions of this manuscript. Finally, we acknowledge financial support by Deutsche Forschungsgemeinschaft and Ruprecht-Karls-Universität Heidelberg within their funding programme Open Access Publishing .

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Keywords : complex problem solving, validity, assessment, definition, MicroDYN

Citation: Dörner D and Funke J (2017) Complex Problem Solving: What It Is and What It Is Not. Front. Psychol. 8:1153. doi: 10.3389/fpsyg.2017.01153

Received: 14 March 2017; Accepted: 23 June 2017; Published: 11 July 2017.

Reviewed by:

Copyright © 2017 Dörner and Funke. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Joachim Funke, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

A Practical Guide to Problem-Solving Techniques in Systems Engineering

In the world of systems engineering, identifying and addressing issues is a significant part of the job. To ensure the smooth operation of complex systems, engineers employ various practical problem-solving techniques. Problem-solving techniques are not limited to solving issues specific to any one system, but can also be applied when generating new product ideas and solutions.

We'll start by exploring some common analytical and systematic problem-solving techniques, including thought experiments, the 5 Whys, and root cause analysis, before looking at some more creative techniques.

Analytical and Systematic Problem-Solving Techniques

Thought experiments.

A thought experiment is a disciplined imagination process that engineers use to ponder a problem or system without conducting physical experiments. By using hypothetical scenarios, engineers can predict potential challenges and find solutions without the cost and time of real-world testing.

For instance, consider the design of an urban traffic control system. Engineers can create a thought experiment about how the system would handle an emergency, such as a major traffic accident during rush hour. This mental exercise could help identify potential bottlenecks or gaps in the system, allowing engineers to design more effective controls or contingency plans.

The 5 Whys technique, originally developed by Toyota, is a simple yet effective method to drill down to the root of a problem. By repeatedly asking "why?" in response to the previous answer, engineers can uncover the underlying cause behind an issue.

Imagine a server crash in a data centre. The 5 Whys process might look like this:

• Why did the server crash? Because it overheated.
• Why did it overheat? Because the cooling system failed.
• Why did the cooling system fail? Because the coolant was not circulating.
• Why was the coolant not circulating? Because the pump was broken.
• Why was the pump broken? Because it was not maintained as per the recommended schedule.

Through this process, we learn that the root cause of the server crash was inadequate maintenance, not merely a random hardware failure.

Root Cause Analysis (RCA)

Root cause analysis (RCA) is a systematic process for identifying the underlying causes of faults or problems. RCA aims to prevent the same problems from recurring by eliminating the root cause rather than treating the symptoms.

For example, suppose a manufacturing assembly line is regularly shutting down due to equipment failure. Rather than just fixing or replacing the equipment each time, an RCA might uncover that a specific part is consistently under high stress due to improper alignment, causing it to fail. By correcting this alignment, the systems engineer can prevent the problem from recurring.

Fault Tree Analysis (FTA)

Fault Tree Analysis (FTA) is a top-down, deductive analysis method used to explore the many different causes of a specific failure or undesirable outcome. It graphically represents the logical relationships between subsystem failures, potential human errors, and external events in the form of a tree.

Suppose a software system suffers from frequent downtime. The FTA would start with the undesired event at the top (downtime), and then branch out into various potential causes such as software bugs, hardware failure, network issues, and so on. Each of these branches can then be subdivided further into more specific faults, allowing the engineer to understand all potential causes of the problem and prioritise the most likely or serious ones for remediation.

Simulation Modelling

Simulation modelling is a powerful tool that allows systems engineers to predict the behaviour of a system under different conditions. By creating a digital twin of a real-world system, engineers can understand the system's response to changes in variables, identify potential issues, and test solutions.

For instance, in a complex logistics operation, a simulation model can be used to understand the impact of adding a new product line or increasing order volume. This could reveal potential bottlenecks or inefficiencies, allowing proactive adjustments to be made before they become real-world problems.

Creative Problem-Solving Techniques

Beyond the analytical and systematic problem-solving techniques traditionally used in engineering, there are numerous creative methods that can be applied. These techniques stimulate lateral thinking, enabling you to view problems from a fresh perspective and identify innovative solutions. Here are a few examples:

Brainstorming

Brainstorming is perhaps one of the most commonly used creative problem-solving techniques. It involves gathering a group of people and encouraging them to freely share their thoughts and ideas related to a specific problem. The key is to refrain from any judgment or criticism during the brainstorming process to encourage free thought and out-of-the-box ideas.

SCAMPER is a creative-thinking technique that uses seven types of transformations: Substitute, Combine, Adapt, Modify, Put to another use, Eliminate, and Reverse. By examining a problem through these different lenses, you can generate novel solutions. For example, if you're trying to enhance the efficiency of a manufacturing process, you might "Adapt" a method from a completely different industry or "Combine" two existing processes into one.

Mind Mapping

Mind Mapping is a visual tool that helps structure information, enabling you to better analyse, comprehend, and generate new ideas. Starting with a central concept, you add nodes branching out into related subtopics. This can reveal unexpected connections and encourage creative problem-solving.

Six Thinking Hats

This technique, devised by Edward de Bono, involves viewing a problem from six distinct perspectives, symbolised by hats of different colours. The white hat considers facts and information, the red hat looks at the issue emotionally, the black hat uses caution and considers risks, the yellow hat optimistically thinks about benefits, the green hat encourages creativity, and the blue hat manages the process and oversees the big picture.

Analogy Thinking

Analogy thinking, or analogous thinking, is a method of comparing the problem at hand to other similar situations or phenomena. By drawing parallels, you might find creative solutions that you would not have considered otherwise. For example, an engineer might draw inspiration from the natural world, such as how a bird flies or a tree distributes nutrients, to solve a complex mechanical or systems problem.

In conclusion, problem-solving in systems engineering represents a harmonious blend of art and science. It's not about completely discarding systematic, logical techniques, but instead complementing them with creative strategies. This combination of traditional and creative methods equips systems engineers with the tools to predict, identify, and address issues effectively and efficiently. By fostering a balance between analytical and innovative thinking, fresh insights can be gained and novel solutions developed. This fusion is often where the most impactful solutions are found. As these techniques are regularly practiced and mastered, they can lead to smoother operations, reduced downtime, and ultimately more successful projects. The artistry lies in the creativity, and the science in the application and understanding of these tools, culminating in an exciting, evolving, and rewarding field.

This content was generated using OpenAI's GPT Large Language Model (with some human curation!). Check out the post "Explain it like I'm 5: What is ChatGPT?" to learn more.

The Power of Active Inference in Systems Engineering

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Solving Big Problems Requires Understanding Complex Systems

The 21 st century is full of complex and perplexing problems that have proven tough to solve: pandemics, market crashes, global warming, poverty, and crime. If these problems could be explained by simple cause-and-effect relationships, we’d have a solution by now.

Understanding complex problems demands a different type of thinking, one that embraces the big picture as well as individual causal factors—a combination of “top down” and “bottom up” thinking—one that sees the world as interacting processes in which small-scale changes give rise to emergent properties on a larger scale. What’s needed is “systems thinking.”

What we observe is not nature itself but nature exposed to our method of inquiry. ~Werner Heisenberg, physicist, 1901-1976

While systems thinking is an essential skill needed by a modern workforce, it largely goes unaddressed in the classroom, in part because it is so difficult to teach. Our three-year Linking Complex Systems project , funded by the National Science Foundation, is beginning to explore whether students can learn about complex systems using computer-based models and simulations that enable students to visualize and manipulate systems and parts of systems in a way not often possible in the real world.

Two modeling approaches

Systems thinking is recognized by the Next Generation Science Standards as an important crosscutting concept across multiple science and engineering disciplines. It recently has become a major focus for developing instructional technologies and curricular activities. Working with MIT’s Scheller Teacher Education Program and the Argonne National Laboratory Systems Science Center as advisors, we are developing curricula around epidemics and evolution that utilize two modeling perspectives—systems dynamics (top down) and agent based (bottom up)—in order to evaluate learning when students use more than one approach to complex systems thinking.

Our project uses two well-established modeling applications to test two approaches. SageModeler, developed by the Concord Consortium, takes a systems dynamics approach, and StarLogo, developed at MIT, is an agent-based application. While systems thinking has been notoriously hard to implement in the classroom, we approached the challenge with two applications we knew were free, web based, and student friendly.

SageModeler looks at the big picture first. Students start by designing and building their own overall systems diagram using pictorial variables. They can then connect related component parts, quantify the relationship between variables, and run input and output analyses of the system, all without the burden of writing equations or programming. And since SageModeler is embedded in CODAP, our web-based data analysis application, students also can view the relationships between variables using tables and graphs.

SageModeler takes a “stocks and flows” approach: stocks or “collectors” (e.g., CO2 in the atmosphere) go up or down in the system over time according to rates of change or flows (e.g., parts per million per year).

StarLogo , on the other hand, is an agent-based model that looks at individual components first. It simulates the interactions of particular agents and how they affect the whole system. StarLogo combines a graphical drag-and-drop programming language with a 3D gaming interface in which students can manipulate variables and their values to determine how the variables behave in the system as they interact over time.

Research in the classroom

Depending on the type of problem, one modeling perspective may be more appropriate than another. But experiencing both perspectives demonstrates to students how there is more than one way to solve a problem, and ultimately helps them develop a more nuanced understanding of systems.

Following a focus group meeting and training, we pilot tested this idea with a group of 11th grade students in a Massachusetts school district. Using an epidemic as the subject, half the students used SageModeler (Figure 1) and the other half used StarLogo (Figure 2) to analyze the epidemic. We then surveyed them about the experience.

When asked to describe what models describe or explain, most students responded that models were for describing, representing, or explaining how something works or looks. While the generally accepted view is that agent-based modeling allows system outcomes to emerge from individual interactions, and thus making the system outcome less predictable, and systems dynamics modeling is better suited to work with real-world data to test system models, student experiences were mixed in this regard.

In general, students understood that complex issues have multiple causes, and based on those multiple causes, the system can be influenced in a number of ways. But they had a harder time understanding the relationships between components. Students also had different comfort levels with each approach— some preferring StarLogo over SageModeler and vice versa. For example, one student stated: “StarLogo is very detail oriented and SageModeler shows you exactly how the epidemic would play out.” But given that this was a pilot study, a definitive evaluation of student responses to the two modeling systems will depend on further study.

The purpose of teaching systems thinking is to give students an important problem-solving skill set, and more flexibility when creating and evaluating mental models. If students are exposed to different ways of approaching a problem, they are less likely to fixate on one type of model.

We are planning a new round of research in early 2019 with students in two high school biology classes using a new curriculum developed around genetics and evolution. We hope to unite the two core modeling technologies, SageModeler and StarLogo, and marry the strengths of each to form a new technological genre, called “linked-hybrid modeling,” aimed at supporting learning and reasoning in interconnected complex systems (Figure 3). In addition, we’re developing a set of core learning exemplars for high school students involving complex systems.

For students, as well as educators, to be comfortable with complex systems thinking, these ideas and approaches need to become part of learning across subjects. Our Linking Complex Systems research is one step towards understanding how to give students this critical 21st century skill.

Carolyn Staudt ( [email protected] ) is a senior scientist. Hee-Sun Lee ( [email protected] ) is a senior research scientist. Steven Roderick ( [email protected] ) is an education consultant.

This material is based upon work supported by the National Science Foundation under grant IIS-1629526. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

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Solving Complex Problems

MIT Sloan, the birthplace of ‘system dynamics’, offers hope for hard-pressed leaders perplexed by complexity

IEDP Editorial

To solve complex business problems we like to think we identify the problem, gather data, evaluate alternatives, select a solution, and implement it. If only! In fact, not to mention the law of unintended consequences, complex problems never fit this open-loop trajectory. Instead, they have many circular, interlocking, sometimes time-delayed relationships among their components, so that solutions require continual feedback, looping back, and a dynamic approach to design.

Unfortunately, open-loop thinking is a difficult mental model to escape from. What's more the mental models we have are always determined by the system in which we are embedded. The system is at the root of all problems, according to MIT Sloan professor John Sterman . We don’t have separate problems related to marketing, finance, or operations they all stem from the system.

"People don't have separate problems,” says Sterman. “They just have problems, and when we insist on dividing the world into silos and acting locally, our problems usually get worse." The solution, he says, in fact a core idea of systems thinking, “is to help people understand that they are embedded in systems that often mould their behaviour in ways they don’t appreciate, and that it's their job to design the systems in which they operate and their direct reports operate."

Conceived at MIT in the mid-1950s, by the late Professor Jay Forrester , ‘System Dynamics’ is a powerful framework for identifying, designing, and implementing solutions for complex challenges. A methodology that is more relevant than ever today in our increasingly complex business world, characterised by the fusion of technology and human endeavour; a world in which senior leaders no longer feel they have the skills to intervene effectively, into the complexity, to make a positive difference.

Any leader faced with the multifaceted challenges typical of modern organizational life can benefit from learning the principles of systems dynamics and using it to understand complex situations and the dynamics those situations produce. And with understanding implementing system dynamics practices to design better operating policies, understand complexity, and guide effective change.

Many of the programs in MIT Sloan Executive Education’s portfolio incorporate systems thinking, and the following courses in particular focus on real-world problem solving using this tool:

Understanding and Solving Complex Business Problems  presents an introduction to System Dynamics. Through exercises and simulation models, participants experience the long-term side effects and impacts of decisions and understand the ways in which performance is tied to structures and policies.

Business Dynamics: MIT's Approach to Diagnosing and Solving Complex Business Problems  is a week-long, hands-on program that offers a deep dive into System Dynamics, including tools and techniques that you can apply to participants own business environment as soon as they complete the program.

Participants in both programs, through intensive, hands-on workshops and interactive experiments, are exposed to the principles of systems thinking and practical methods for putting them into action. They will be introduced to a variety of tools, including mapping techniques, simulation models, and MIT’s management flight simulators—such as  the Beer Game : a table game developed by Jay Forrester, that illustrates the nonlinear complexities of supply chains and the way individuals are circumscribed by the systems in which they act.

Professor John Sterman talks about system dynamics in this short video

MIT Sloan is uniquely positioned at the intersection of technology and business practice, and participants in our programs gain access to MIT’s distinctive blend of intellectual capital and practical, hands-on learning.

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April 5, 2024 feature

This article has been reviewed according to Science X's editorial process and policies . Editors have highlighted the following attributes while ensuring the content's credibility:

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Scientists harness chemical dynamics for complex problem solving

by Tejasri Gururaj , Phys.org

At the intersection of chemistry and computation, researchers from the University of Glasgow have developed a hybrid digital-chemical probabilistic computational system based on the Belousov-Zhabotinsky (BZ) reaction which can be used for solving combinatorial optimization problems.

By harnessing the inherent probabilistic nature of BZ reactions, the system demonstrates emergent behaviors like replication and competition seen in complex systems, reminiscent of living organisms. This could pave the way for novel approaches to computational tasks that are fazed by the limitations set by modern computation.

Combining electronic control and chemical dynamics offers a way to perform efficient computation, combining the best of both towards the development of adaptive, bio-inspired computing platforms with unparalleled efficiency and scalability.

The research led by Prof. Leroy Cronin, the Regius Chair of Chemistry at the University of Glasgow, was published in Nature Communications . Prof. Cronin spoke to Phys.org about their work and stated his motivation behind pursuing the same.

"I wanted to see if we could make a new type of chemical information processing system as I am inspired by how biology can process information in wet brains," he said.

Limitations of modern computing

Modern computing relies on transistors, the building blocks of electronic devices, that are used to create logic gates and memory cells , forming the basis of digital circuits. But, the need and demand for more computational power means that transistors are getting smaller and smaller.

The miniaturization of transistors has several limitations due to constraints set by fabrications and the laws of physics. The smaller the transistor, the harder it is to manufacture and requires more power, dissipating more heat and being less and less energy efficient.

This has led scientists to explore other types of computing, such as quantum computing, which while being extremely powerful at solving problems classical computers can't suffer from scalability issues due to error correction.

On the other hand, computation based on physical processes , such as chemical reactions , uses a mixture of systems such as digital, chemical, and optical. This opens up new avenues for unconventional computing architectures with capabilities beyond traditional digital systems.

The BZ reaction

The BZ reaction is a classic example of a chemical oscillator, with the reactant and product concentrations undergoing periodic changes. It is observed in many chemical systems, such as laboratory settings and biological systems.

The BZ reaction's ability to exhibit complex, nonlinear dynamics makes it an attractive choice for studying emergent phenomena and unconventional computing paradigms.

In this research, the BZ reaction serves as the foundation for a hybrid computational system due to its inherent oscillatory behavior, adaptability, and responsiveness to external stimuli. By harnessing the dynamics of BZ reactions, researchers can emulate complex behaviors seen in natural systems, providing a versatile platform for computation.

The concentrations can serve as binary information (with 0 being low concentrations and 1 for high concentrations) and the oscillating concentrations can serve as time-dependent variables. Additionally, information can propagate between individual cells having BZ reactions through processes like diffusion.

Prof. Cronin further explained, "The reaction has two states on and off and each box [or cell] in the network can be flashing independently, in sync, or after communication. This is the process by which the system can be programmed to compute a problem which is then read out by the camera."

A hybrid programmable information processor

The core of the information processor is a 3D-printed grid of interconnected reactors. Each reactor or cell hosts the BZ reaction, making it an array of BZ reactions.

The input to this array is electronic and is controlled by magnetic stirrers capable of manipulating the reaction within these cells. There are also interfacial stirrers capable of facilitating interactions between coupled cells (via diffusion), this helps to synchronize the oscillations.

The researchers observed that the oscillations of the reactant and product concentrations occur as forced-damped oscillations, with the stirrers playing a crucial role in controlling them.

This behavior is a characteristic feature of BZ reactions, where chemical species undergo periodic changes in concentration over time. These changes are noticed by the changes in the color of the liquids.

The output processing involves two key components: a convolutional neural network (CNN) and a recognition finite state machine (rfsm). These components analyze the reactant and product concentrations within the BZ reaction, which are captured using video cameras.

The CNN classifies the concentrations into discrete chemical states, while the rfsm determines the corresponding chemical state based on this classification.

In simple terms, the discrete chemical states are classified and determined based on the concentrations of reactants and products within the BZ reaction, which are themselves probabilistic due to the nature of the reactions.

The probabilistic nature arises because the BZ reaction is non-linear, resulting in complex interactions between chemical species that exhibit inherent variability and unpredictability in their behavior over time.

The entire system operates smoothly and continuously based on a feedback loop based on the changing colors of the liquid. When the concentrations are oscillating the system is "on" indicated by blue colors and when there is a lack of oscillations, the liquids are red, meaning that the system is "off."

This loop manipulates the stirrers based on the colors, ensuring that the process is continuous with the help of "forced" or external control.

Chemical cellular automata and solving optimization problems

The researchers used the hybrid processor to showcase its computational capability by implementing chemical cellular automata (CCA) in 1D and 2D.

These are mathematical models to simulate complex systems composed of simple components interacting locally with each other according to predefined rules.

This leads to emergent behaviors such as replication and competition exhibited by "Chemits," which are multicellular entities defined by patterns of chemical concentrations within the grid of interconnected reactors hosting the BZ reaction.

These behaviors resemble those observed in living organisms and contribute to the complexity and adaptability of the computational system.

Moreover, the researchers demonstrate that their computational approach, which incorporates both electronic and chemical components, can efficiently tackle combinatorial optimization challenges, like the traveling salesman problem.

On the application side of things, hybrid systems like these could be very useful for deep learning tasks that require non-linear behavior. Chemical systems inherently offer such characteristics, making hybrid-computation architectures resource-efficient for specific problems where non-linearities and probabilistic behavior are vital.

Prof. Cornin added, "I see that a solid-state version could replace artificial intelligence hardware and be trained much easier."

In the future, he wishes to explore the miniaturization of this technology and increase the size of the grid to solve truly large problems.

Journal information: Nature Communications

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