the human problem solving

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Human problem solving

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Overview of the Problem-Solving Mental Process

• Identify the Problem
• Define the Problem
• Form a Strategy
• Organize Information
• Allocate Resources
• Monitor Progress
• Evaluate the Results

Frequently Asked Questions

Problem-solving is a mental process that involves discovering, analyzing, and solving problems. The ultimate goal of problem-solving is to overcome obstacles and find a solution that best resolves the issue.

The best strategy for solving a problem depends largely on the unique situation. In some cases, people are better off learning everything they can about the issue and then using factual knowledge to come up with a solution. In other instances, creativity and insight are the best options.

It is not necessary to follow problem-solving steps sequentially, It is common to skip steps or even go back through steps multiple times until the desired solution is reached.

In order to correctly solve a problem, it is often important to follow a series of steps. Researchers sometimes refer to this as the problem-solving cycle. While this cycle is portrayed sequentially, people rarely follow a rigid series of steps to find a solution.

The following steps include developing strategies and organizing knowledge.

1. Identifying the Problem

While it may seem like an obvious step, identifying the problem is not always as simple as it sounds. In some cases, people might mistakenly identify the wrong source of a problem, which will make attempts to solve it inefficient or even useless.

Some strategies that you might use to figure out the source of a problem include :

• Asking questions about the problem
• Breaking the problem down into smaller pieces
• Looking at the problem from different perspectives
• Conducting research to figure out what relationships exist between different variables

2. Defining the Problem

After the problem has been identified, it is important to fully define the problem so that it can be solved. You can define a problem by operationally defining each aspect of the problem and setting goals for what aspects of the problem you will address

At this point, you should focus on figuring out which aspects of the problems are facts and which are opinions. State the problem clearly and identify the scope of the solution.

3. Forming a Strategy

After the problem has been identified, it is time to start brainstorming potential solutions. This step usually involves generating as many ideas as possible without judging their quality. Once several possibilities have been generated, they can be evaluated and narrowed down.

The next step is to develop a strategy to solve the problem. The approach used will vary depending upon the situation and the individual's unique preferences. Common problem-solving strategies include heuristics and algorithms.

• Heuristics are mental shortcuts that are often based on solutions that have worked in the past. They can work well if the problem is similar to something you have encountered before and are often the best choice if you need a fast solution.
• Algorithms are step-by-step strategies that are guaranteed to produce a correct result. While this approach is great for accuracy, it can also consume time and resources.

Heuristics are often best used when time is of the essence, while algorithms are a better choice when a decision needs to be as accurate as possible.

4. Organizing Information

Before coming up with a solution, you need to first organize the available information. What do you know about the problem? What do you not know? The more information that is available the better prepared you will be to come up with an accurate solution.

When approaching a problem, it is important to make sure that you have all the data you need. Making a decision without adequate information can lead to biased or inaccurate results.

5. Allocating Resources

Of course, we don't always have unlimited money, time, and other resources to solve a problem. Before you begin to solve a problem, you need to determine how high priority it is.

If it is an important problem, it is probably worth allocating more resources to solving it. If, however, it is a fairly unimportant problem, then you do not want to spend too much of your available resources on coming up with a solution.

At this stage, it is important to consider all of the factors that might affect the problem at hand. This includes looking at the available resources, deadlines that need to be met, and any possible risks involved in each solution. After careful evaluation, a decision can be made about which solution to pursue.

6. Monitoring Progress

After selecting a problem-solving strategy, it is time to put the plan into action and see if it works. This step might involve trying out different solutions to see which one is the most effective.

It is also important to monitor the situation after implementing a solution to ensure that the problem has been solved and that no new problems have arisen as a result of the proposed solution.

Effective problem-solvers tend to monitor their progress as they work towards a solution. If they are not making good progress toward reaching their goal, they will reevaluate their approach or look for new strategies .

7. Evaluating the Results

After a solution has been reached, it is important to evaluate the results to determine if it is the best possible solution to the problem. This evaluation might be immediate, such as checking the results of a math problem to ensure the answer is correct, or it can be delayed, such as evaluating the success of a therapy program after several months of treatment.

Once a problem has been solved, it is important to take some time to reflect on the process that was used and evaluate the results. This will help you to improve your problem-solving skills and become more efficient at solving future problems.

A Word From Verywell​

It is important to remember that there are many different problem-solving processes with different steps, and this is just one example. Problem-solving in real-world situations requires a great deal of resourcefulness, flexibility, resilience, and continuous interaction with the environment.

Human problem solving

By allen newell and herbert a. simon.

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Book review: human problem solving.

How do we solve hard problems? What are we thinking about as we work? What influences whether we find an answer or remain stuck forever?

These are the questions Allen Newell and Herbert Simon set out to address in their landmark 1972 book, Human Problem Solving . Their work has had an enormous influence on psychology, artificial intelligence, and economics.

How do you get reliable data for complicated problems? Here’s the basic strategy behind HPS :

• Find a category of problems you want to study. 1
• Write a computer program to solve the problem.
• Get participants to solve the problem while verbalizing their thought processes.
• Compare the computer program to the transcripts of real people solving the problem and look for similarities and differences.

While all models are imperfect, computer programs have some distinct advantages as theories of human performance. For starters, they can solve the problems proposed. Since we know how computers work, but not how minds work, using a known process—the computer program—as a model avoids the issue of trying to explain one mysterious phenomenon with another.

However, Newell and Simon go further than this theoretical convenience. They argue that human thinking is an information processing system, just as a computer is. This remains a controversial thesis, but nonetheless, it makes strong and interesting predictions about how we think. 2

Key Idea: Problem Solving is Searching a Problem Space

Newell and Simon argue problem solving is essentially a search through an abstract problem space. We navigate through this space using operators, and those operators transform our current information state into a new one. We evaluate this state, and if it matches our answer (or is good enough for our purposes), the problem is solved.

We can liken this to finding our way in a physical space. Compare problem solving to finding the exit of a maze:

• The problem space is the physical space in the maze . You have some current location, and you want to be at the exit. Solving the problem means finding your way out.
• Operators are the physical movements you can make . You can go left, right, forward or backward. After each movement, you’re in a different place. You evaluate your new state and decide if you have found the solution or if you should move again.

Now consider solving a Rubik’s Cube . How does this perspective on problem solving apply?

• The problem space is all the possible configurations of the cube . Given there are over 43 quintillion possibilities, the space is enormous.
• The operators are your ability to rotate the cube in various directions . Even though the space is vast, the operators available at each moment are quite limited.
• Solving the puzzle involves moving through this abstract problem space , ending in a configuration where the colors are properly segregated to each side of the cube.

In a Rubik’s Cube, the operators on the problem space are physical, but they need not be. Consider Sudoku, where there might be other ways of conceptualizing the problem, resulting in different problem spaces:

• A basic space might just be the set of all possible assignments of numbers to squares . Most of these would fail to fit the constraints of the numbers 1-9 being used uniquely in each subgrid, row, and column. Search in this space might look like trying out a random combination and seeing if it is correct.
• A better space would be augmented . Instead of allowing only fixed numbers at each square, you might have information about sets of “possible” numbers. Operators would consist of fixing a particular square and eliminating possibilities from those that remain via other constraints. This is closer to how experts solve Sudoku puzzles , as the basic problem space is unwieldy.

The difficulty of solving a problem isn’t always in searching the problem space. Sometimes, the hard part is choosing the correct space to work in in the first place. Insight-based puzzles, such as the nine-dot puzzle, fit this pattern. In this puzzle, you must cross all nine dots using four straight lines, drawn without lifting your pencil from the paper.

What makes this puzzle difficult is finding the problem space where the solution exists. ( Answer here for those interested.)

What Determines Problem Solving Difficulty?

I’ve already mentioned two factors that influence the difficulty of problems: the size of the problem space and how strongly the task itself suggests the best space. Newell and Simon found others in their research.

A simple one is the role memory has on problem solving. Human cognition depends far more on memory than most of us realize. Because working memory is limited, we lean heavily on past experience to solve new problems.

For instance, subjects universally prefer evaluating chess positions one sequence of moves at a time rather than pursuing multiple sequences simultaneously. For computers, the difference between breadth-first and depth-first search is a technical choice. For humans, depth-first is necessary because we don’t have the working memory capacity to hold multiple intermediate positions in our mind’s eye.

Conversely, many problems cease to be problems at all once we have the correct procedure in memory. As we learn new things, we develop memorized answers and algorithms that eliminate the need for problem solving altogether. Tic-tac-toe is a fun puzzle when you’re a kid, but it’s boring as an adult because the game always leads to a stalemate.

To see something as a problem, then, means it must occupy a strange middle-ground. It must be unfamiliar enough so that the correct answer is not routine, yet not so vast and inscrutable that searching the problem space feels pointless.

Is There a Programming Language of Thought?

Given Newell and Simon frame their theories of human cognition in terms of computer programs, this raises a question: what sorts of programs fit best?

Newell and Simon argue in favor of production systems . A production system is a collection of IF-THEN patterns, each independent from one another. The collection of productions fires when the “IF” part of the observed pattern matches the contents of short-term memory. The “THEN” part corresponds to an operator—you do something to move yourself through the problem space.

This remains a popular choice. ACT-R theory , which continues to be influential in psychological research, is also based on the production system framework.

Productions have a few characteristics that make them plausible for modeling aspects of human thought:

• Their modularity means that parts of what has been learned can transfer to new skills . While transfer research has often been pessimistic, it’s clear that humans transfer acquired skills much better than most computer programs.
• They extend the basic behaviorist notions of stimulus-response . Productions are like habits except, because they can operate on both internal and external states, they are far more powerful. They can incorporate goals, desires and memories.
• They force serial order on human thinking . The brain’s underlying architecture is massively parallel—billions of independently firing neurons. But human thinking is remarkably serial. Productions, processed in parallel but acted on in sequence, suggest a resolution to the paradox.

Can We Solve Problems Better?

Human Problem Solving articulates a theory of cognition, not practical advice. Yet it has implications for the kinds of problems we face in life:

1. The Power of Prior Knowledge

Prior knowledge exerts an enormous influence on problem solving. While raw intelligence—often construed as processing speed or working memory capacity—does play a role, it is often far less important than having key knowledge.

Consider the ways prior knowledge influences your thinking:

• Prior knowledge determines your choice of problem space . This is clear in the cryptarithmetic puzzles used by Newell and Simon. Subjects who already knew a lot about multi-digit addition were able to form a problem space consisting of letter values, odd-even parities and carries. In contrast, less-knowledgeable subjects struggled. Some worked in a more basic problem space, trying out random combinations before giving up. Others attempted dozens of different problem spaces, none of which were particularly suited to the task.
• Prior knowledge determines which operators are available to you . A sophisticated library of operators can make the problem much easier to solve. In some cases, it can eliminate the problem entirely as search is no longer required—you simply proceed with an algorithm that gets the answer directly. Much of what we do in life is routine action, not problem-solving.
• Prior knowledge creates memories of specific patterns, reducing analysis required . In chess, for instance, dynamic patterns require a player to simulate how play will unfold over time. This difficult-to-process task can be replaced by learning static patterns whose outcomes are understood just by looking at them. Consider a pattern such as a “ forking attack ” where a knight attacks two pieces simultaneously. While this pattern can be discovered through searching the possible future moves of the pieces in play, good players recognize it visually on the board. Simple recognition eliminates the need to formally analyze the implications of each piece, sparing precious working memory capacity.

All of this suggests that knowledge is more important than intelligence for particular classes of problems. Of course, the two factors are often correlated. Intelligence speeds learning, which allows you to have more knowledge. However the intelligence-as-accelerated-knowledge-acquisition picture suggests different implications than the intelligence-as-raw-insight picture we often associate with genius. Geniuses are smart, in large part, because they know more things.

2. The Only Solvable Problems are Tractable Ones

This is an area where I’ve changed my thinking. Previously, I had written about what I called “ tractability bias .” We tend to work on solvable, less important problems rather than harder problems that don’t suggest any solutions.

There is some truth to this account: we do tend to avoid impossible-seeming problems, even if they’re more worthy of our efforts. Yet HPS points to an obvious difficulty: the importance of a problem has nothing to do with our ability to solve it. Even for well-defined challenges, problem spaces can be impossibly vast. Finding a solution, even something that is “good enough,” can be impractical for many classes of problems.

I suspect our emotional aversion to hard problems comes from this place. Unless we have reasonable confidence our problem solving search will arrive at an answer we don’t invest any effort. Because the size of problem spaces can often be enormous, this is shrewd, not lazy.

To be successful we need to work on important problems. But, we also need to find ways to make those problems tractable. The intersection of these two requirements is what makes much of life so intriguing—and challenging.

• HPS focuses on three: cryptarithmetic puzzles, logic theorem proving and chess.
• For some rebuttals to this position, consider Hubert Dreyfus , Philip Agre and Jean Lave . Note also counter-rebuttals from Herbert Simon and John Anderson .

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The theory of problem solving in 1970--and especially the part of it that is empirically validated--is primarily a theory that describes the problem spaces and problem-solving programs, and shows how these adapt the information-processing system to its task environment. At the same time that it has answered some basic questions about problem-solving processes, the research has raised new ones: how do problem solvers generate problem spaces; what is the neurological substrate for the serial, limited-memory information processor; how can our knowledge of problem-solving processes be used to improve human problem solving and learning? Each node in a problem space may be thought of as a possible state of knowledge to which the problem solver may attain. But the subject of internal representation links problem-solving research with two other important areas of psychology: perception and psycholinguistics. The theory of problem solving gives a new basis for attacking the psychology of education and the learning process.

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Human Problem Solving Paperback – February 14, 2023

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First published in 1972, this monumental work develops and defends the authors' information processing theory of human reasoning. Human reasoners, they argue, can be modeled as symbolic "information processing systems" (IPSs), abstracted entirely from physiological bases. Modeling subjects with IPSs yields predictive theories of their problem-solving behavior and performance, and psychological insight into their heuristics and methods.Newell and Simon's previous epoch-making collaborations included the General Problem Solver, the Logic Theorist, and the Information Processing Language. This book is a careful application of those ideas from artificial intelligence - the ideas of AI's first golden age - to cognitive psychology. The authors first develop the formal theory of information processing systems. They then report studies of three symbolic reasoning tasks, and analyze that data using the information processing paradigm. In the final section, they state their comprehensive theory of human problem-solving. The success of the models of cognition given in Human Problem Solving was a major piece of evidence for the physical symbol system hypothesis, which Newell and Simon would first state a few years later. Newell went on to co-develop the Soar cognitive architecture, and Simon to receive the Nobel Prize in Economics. The two jointly received the Turing Award in 1975 for the research program of which Human Problem Solving was the culmination.

This book is also available from Echo Point Books as a hardcover (ISBN 1635617928).

• Print length 938 pages
• Language English
• Publisher Echo Point Books & Media, LLC
• Publication date February 14, 2023
• Dimensions 6.69 x 1.85 x 9.61 inches
• ISBN-10 1648371949
• ISBN-13 978-1648371943
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"...perhaps the most important book on the scientific study of human thinking in the 20th century." - E.A. Feigenbaum, A. M. Turing Award Laureate

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• Publisher ‏ : ‎ Echo Point Books & Media, LLC; Reprint ed. edition (February 14, 2023)
• Language ‏ : ‎ English
• Paperback ‏ : ‎ 938 pages
• ISBN-10 ‏ : ‎ 1648371949
• ISBN-13 ‏ : ‎ 978-1648371943
• Item Weight ‏ : ‎ 3.22 pounds
• Dimensions ‏ : ‎ 6.69 x 1.85 x 9.61 inches
• #3,011 in Medical Cognitive Psychology
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Herbert a. simon.

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Allen Newell

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• DOI: 10.2307/2063712
• Corpus ID: 62537074

Human Problem Solving.

• Nick Axten , A. Newell , Herbert A. Simon
• Published 1 March 1973
• Contemporary Sociology

11,975 Citations

Panel summary: planning and problem solving, the problems with problem solving: reflections on the rise, current status, and possible future of a cognitive research paradigm, problem solving and reasoning, psychology of, expert approaches to analysis, embodied cognition: a field guide, lessons from human problem solving for cognitive systems research.

• Highly Influenced

Embodied Cognition: A field guide

An investigation of human problem solving system: computation as an example, how can the human mind occur in the physical universe, anderson’s theory of cognitive architecture (act*), related papers.

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Complex cognition: the science of human reasoning, problem-solving, and decision-making

• Published: 23 March 2010
• Volume 11 , pages 99–102, ( 2010 )

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• Ann G. Wolf 1  

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Climate change, globalization, policy of peace, and financial market crises—often we are faced with very complex problems. In order to tackle these complex problems, the responsible people should first come to mutual terms. An additional challenge is that typically the involved parties have different (often conflicting) interests and relate the problems to different emotions and wishes. These factors certainly do not ease the quest for a solution to these complex problems.

It is needless to say that the big problems of our time are not easy to solve. Less clear, however, is identifying the causes that led to these problems. Interest conflicts between social groups, the economic and social system or greed—one can think of many responsible factors for the large-scale problems we are currently confronted with.

The present “Special Corner: complex cognition” deals with questions in this regard that have often received little consideration. Under the headline “complex cognition”, we summarize mental activities such as thinking, reasoning, problem - solving, and decision - making that typically rely on the combination and interaction of more elementary processes such as perception, learning, memory, emotion, etc. (cf. Sternberg and Ben-Zeev 2001 ). However, even though complex cognition relies on these elementary functions, the scope of complex cognition research goes beyond the isolated analysis of such elementary mental processes. Two aspects are essential for “complex cognition”: The first aspect refers to the interaction of different mental activities such as perception, memory, learning, reasoning, emotion, etc. The second aspect takes the complexity of the situation into account an agent is confronted with. Based on these two aspects, the term “complex cognition” can be defined in the following way:

Complex psychological processes: We talk about “complex cognition”, when thinking, problem-solving, or decision-making falls back on other cognitive processes such as “perception”, “working memory”, “long-term memory”, “executive processes”, or when the cognitive processes are in close connection with other processes such as “emotion” and “motivation”. The complexity also results from an interaction from a multitude of processes that occur simultaneously or at different points in time and can be realized in different cognitive and/or neuronal structures.

Complex conditions: We also talk about “complex cognition” when the conditions are complex in which a person finds himself and in which conclusions need to be drawn, a problem needs to be solved, or decisions need to be made. The complexity of the conditions or constraints can have different causes. The situation structure itself can be difficult to “see”, or the action alternatives are difficult “to put into effect”. The conditions can themselves comprise of many different variables. These variables can exhibit a high level of interdependence and cross-connection, and it can, as time passes by, come to a change of the original conditions (e.g. Dörner and Wearing 1995 ; Osman 2010 ). It can also be the case that the problem is embedded in a larger social context and can be solved only under certain specifications (norms, data, legislations, culture, etc.) or that the problem can only be solved in interaction with other agents, be it other persons or technical systems.

When one summarizes these two aspects, this yields the following view of what should be understood as “complex cognition”.

As “complex cognition” we define all mental processes that are used by individuals for deriving new information out of given information, with the intention to solve problems, make decision, and plan actions. The crucial characteristic of “complex cognition” is that it takes place under complex conditions in which a multitude of cognitive processes interact with one another or with other noncognitive processes.

The “Special Corner: complex cognition” deals with complex cognition from many different perspectives. The typical questions of all contributions are: Does the design of the human mind enable the necessary thinking skills to solve the truly complex problems we are faced with? Where lay the boundaries of our thinking skills? How do people derive at conclusions? What makes a problem a complex problem? How can we improve our skills to effectively solve problems and make sound judgements?

It is for sure too much to expect that the Special Corner answers these questions. If it were that easy, we would not be still searching for an answer. It is, however, our intention with the current collection of articles to bring to focus such questions to a larger extent than has been done so far.

An important starting point is the fact that people’s skills to solve the most complex of all problems and to ponder about the most complex issues is often immense—humankind would not otherwise be there were she is now. Yet, on the other hand, it has become more clear in the past few years that often people drift away from what one would identify as “rational” (Kahneman 2003 ). People hardly ever adhere to that what the norms of logic, the probability calculus, or the mathematical decision theory state. For example, most people (and organizations) typically accept more losses for a potential high gain than would be the case if they were to take into account the rules of the probability theory. Similarly, they draw conclusions from received information in a way that is not according to the rules of logic. When people, for example, accept the rule “If it rains, then the street is wet”, they most often conclude that when the street is wet, it must have rained. That, however, is incorrect from a logical perspective: perhaps a cleaning car just drove by. In psychology, two main views are traditionally put forward to explain how such deviations from the normative guidelines occur. One scientific stream is interested in how deviations from the normative models can be explained (Evans 2005 ; Johnson-Laird 2008 ; Knauff 2007 ; Reason 1990 ). According to this line of research, deviations are caused by the limitations of the human cognitive system. The other psychological stream puts forward as the main criticism that the deviations can actually be regarded as mistakes (Gigerenzer 2008 ). The deviations accordingly have a high value, because they are adjusted to the information structure of the environment (Gigerenzer et al. 1999 ). They have probably developed during evolution, because they could ensure survival as for example the specifications of formal logic (Hertwig and Herzog 2009 ). We, the editors of the special corner, are very pleased that we can offer an impression of this debate with the contributions from Marewski, Gaissmaier, and Gigerenzer and the commentaries to this contribution from Evans and Over. Added to this is a reply from Marewski, Gaissmaier, and Gigerenzer to the commentary from Evans and Over.

Another topic in the area of complex cognition can be best illustrated by means of the climate protection. To be successful in this area, the responsible actors have to consider a multitude of ecological, biological, geological, political, and economical factors, the basic conditions are constantly at change, and the intervention methods are not clear. Because the necessary information is not readily available for the person dealing with the problem, the person is forced to obtain the relevant information from other sources. Furthermore, intervention in the complex variable structure of the climate can trigger processes whose impact was likely not intended. Finally, the system will not “wait” for intervention of the actors but will change itself over time. The special corner is also concerned with thinking and problem-solving in such complex situations. The article by Funke gives an overview of the current state of research on this topic from the viewpoint of the author, in which several research areas are covered that have internationally not received much acknowledgement (but see, for example, Osman 2010 ).

Although most contributions to the special corner come from the area of psychology, the contribution by Ragni and Löffler illustrates that computer science can provide a valuable addition to the understanding of complex cognition. Computer science plays an important role in complex cognition. In general, computer science, which is used to investigate computational processes central to all research approaches, can be placed in a “computational theory of cognition” framework. This is true especially for the development of computational theories of complex cognitive processes. In many of our modern knowledge domains, the application of simulations and modelling has become a major part of the methods inventory. Simulations help forecast the weather and climate change, help govern traffic flow and help comprehend physical processes. Although modelling in these areas is a vastly established method, it has been very little applied in the area of human thinking (but see e.g. Anderson 1990 ; Gray 2007 ). However, exactly in the area of complex cognition, the method of cognitive modelling offers empirical research an additional methodological access to the description and explanation of complex cognitive processes. While the validity of psychological theories can be tested with the use of empirical research, cognitive models, with their internal coherence, make possible to test consistency and completeness (e.g. Schmid 2008 ). They will also lead to new hypotheses that will in turn be possible to test experimentally. The contribution of Ragni and Löffler demonstrates with the help of an interesting example, finding the optimal route, the usefulness of simulation and modelling in psychology.

A further problem in the area of complex cognition is that many problems are solvable only under certain social conditions (norms, values, laws, culture) or only in interaction with other actors (cf. Beller 2008 ). The article on deontic reasoning by Beller is concerned with this topic. Deontic reasoning is thinking about whether actions are forbidden or allowed, obligatory or not obligatory. Beller proposes that social norms, imposing constraints on individual actions, constitute the fundamental concept for deontic thinking and that people reason from such norms flexibly according to deontic core principles. The review paper shows how knowing what in a certain situation is allowed or forbidden can influence how people derive at conclusions.

The article of Waldmann, Meder, von Sydow, and Hagmayer is concerned with the important topic of causal reasoning. More specifically, the authors explore the interaction between category and causal induction in causal model learning. The paper is a good example of how experimental work in psychology can combine different research traditions that typically work quite isolated. The paper goes beyond a divide and conquers approach and shows that causal knowledge plays an important role in learning, categorization, perception, decision-making, problem-solving, and text comprehension. In each of these fields, separate theories have been developed to investigate the role of causal knowledge. The first author of the paper is internationally well known for his work on the role of causality in other cognitive functions, in particular in categorization and learning (e.g. Lagnado et al. 2007 ; Waldmann et al. 1995 ). In a number of experimental studies, Waldmann and his colleagues have shown that people when learning about causal relations do not simply form associations between causes and effects but make use of abstract prior assumptions about the underlying causal structure and functional form (Waldmann 2007 ).

We, the guest editors, are very pleased that we have the opportunity with this Special corner to make accessible the topic “complex cognition” to the interdisciplinary readership of Cognitive Processing . We predict a bright future for this topic. The research topic possesses high research relevance in the area of basic research for a multitude of disciplines, for example psychology, computer science, and neuroscience. In addition, this area forms a good foundation for an interdisciplinary cooperation.

A further important reason for the positive development of the area is that the relevance of the area goes beyond fundamental research. In that way, the results of the area can for example also contribute to better understanding of the possibilities and borders of human thinking, problem-solving, and decisions in politics, corporations, and economy. In the long term, it might even lead to practical directions on how to avoid “mistakes” and help us better understand the global challenges of our time—Climate change, globalization, financial market crises, etc.

We thank all the authors for their insightful and inspiring contributions, a multitude of reviewers for their help, the editor-in-chief Marta Olivetti Belardinelli that she gave us the opportunity to address this topic, and the editorial manager, Thomas Hünefeldt, for his support for accomplishing the Special Corner. We wish the readers of the Special Corner lots of fun with reading the contributions!

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Knauff, M., Wolf, A.G. Complex cognition: the science of human reasoning, problem-solving, and decision-making. Cogn Process 11 , 99–102 (2010). https://doi.org/10.1007/s10339-010-0362-z

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It’s one of nature’s great spectacles. In autumn in parts of Europe, just before sunset, hundreds of thousands of common starlings ( Sturnus vulgaris ) congregate unbidden in vast groups and perform what appear to be aerial acrobatics, creating three-dimensional teardrops, globes, ribbons and hourglasses in the sky. In Denmark’s Wadden Sea National Park, a wetland area seasonally replete with bugs attractive to literally millions of migrating starlings, the resulting shape-shifting “clouds” are so dense the locals call them sort sol , or “black sun”.

In Rome, where starlings have gathered since ancient times (creating quite a mess), priests called augurs once made a living out of divining the will of the gods from the shapes the starlings made in the air. Similarly fascinated, British poet Samuel Taylor Coleridge described “starlings in vast flights like smoke … expanding, or contracting, thinning or condensing, now glimmering and shivering.” In 1923, famed ornithologist Edmund Selous wrote how each individual bird seemed to be “linked to every other by some invisible material, as are knots on the meshes of a net by the visible twine connecting them.”

In outback Australia, it’s wild budgerigars that put on a show. The nomadic bush budgies can throng in their tens of thousands over native grasses (and seeds) that flourish after rain in areas such as north-west Queensland.

Budgies and starlings are not unique in this communal activity. Honey bees, ants, termites, fish, and, yes, humans (sometimes) gather to prioritise the goals of the group against those of any particular individual, whether to find food, migrate, seek shelter or safely navigate a Grand Final crowd. The movements within these “murmurations” (the flocks are named for the sound of the birds’ collective wing-flapping) appear to be both random and yet, to our logical minds, somehow controlled by a form of central intelligence, so quickly and deftly do the individual creatures change direction.

Some have said it’s “swarm intelligence” that’s behind this invisible choreography. Swarm intelligence is now a major field of study, not just to discover exactly why and how some animals behave in this way but to apply this knowledge to fields such as computing, logistics and decision-making. It’s helping us predict, for example, the outcomes of sports events, to improve internet searches, to clean homes more efficiently and, somewhat worryingly, to increase the likelihood we will all be wiped out by AI-guided killer drones .

How does “swarm intelligence” work? Why do some creatures co-operate and others don’t? How useful is it to humans?

A murmuration of starlings at dusk over RSPB Minsmere Nature Reserve in Suffolk in 2018. Credit: Getty Images, digitally tinted

What’s swarm intelligence and what animals have it?

Californian electrical engineer Gerardo Beni, who, with one of his students, is credited with coining the phrase swarm intelligence, was researching robots in 1989 when his peers started bandying around words such as “group, flock, herd, horde, drove and multitude” to describe his theories. “With my Japanese colleagues, with whom I was actively involved with robotic research, we agreed to call it ‘Gunchinou’ (群知能), which suggests the idea of a swarm,” Beni tells us. “It made sense to call such groups swarms since the best examples are in the animal kingdom.”

They took inspiration from biologists studying the behaviour of ants who forage on behalf of their colony, as well as fish that cluster to diffuse attention from predators and honey bees with their “ hive mind ” – phenomena also known as “collective intelligence”.

‘Each knows only that it has a compulsion, or an order, to act in a certain way.’

At its most basic, collective intelligence can be seen as part of animals ensuring their survival, individually or as a colony. What’s baffling, though, is that the actions of the individuals follow similar patterns that influence the whole without any direction from a central decision-maker. “In other words,” says Beni, “nobody knows either the end nor the means whereby this goal will, or will not, be achieved. Each knows only that it has a compulsion, or an order, to act in a certain way.”

Lisa O’Bryan, whose research at Rice University in Houston, Texas, spans behavioural ecology, psychology and engineering, says not all animal group behaviours should be classified as swarm intelligence. “Simple herding behaviour is not necessarily a type of swarm intelligence,” she tells us. “It is only when these behaviours lead to the solving of explicit problems that they can be considered swarm intelligence.”

Christos Ioannou, professor of behavioural ecology at the University of Bristol, England, has another stab at a definition. “Swarm intelligence is about responding to stimuli, but the swarm intelligence is about taking the limited information of individuals about these stimuli and processing them in a decentralised way so that the group responds in a more intelligent way than if individuals responded alone.”

Whether the behaviour is strictly a form of intelligence is a matter of debate. Melbourne University evolutionary ecologist Mark Elgar, for one, is not a fan of the phrase, suggesting it implies that ants, for example, make rational, democratic choices. In reality, a lot of a colony’s apparently collective behaviour is pretty random: foragers head off and occasionally bump into food, then leave a trail when they return to the nest, which other workers then follow.

“The collective is not a physical entity that you can get your hands on. It is the interaction between the individuals in the collectives. It defines intelligence from a different perspective,” says Hussein Abbass, a swarm intelligence researcher at the University of NSW. Indeed, write Thomas Malone and Michael Bernstein in the Handbook of Collective Intelligence , “There have been almost as many definitions of collective intelligence as there have been writers who have described it.” Their take: “Groups of individuals acting collectively in ways that seem intelligent.”

The phenomenon shows up differently in different types of animals and within species, depending on their individual goals and how closely related they are to the other members of the group. Thousands of types of fish, for example, swim in schools at some point in their lives. Some swim in groups of just three and others – mostly the smaller, more vulnerable ones – in populations kilometres long, the largest of which is thought to be an annual migration of billions of sardines off southern Africa.

A shark swims through a ball of sardines off the east coast of South Africa. Credit: Getty Images, digitally tinted

Sardine schools can be seven kilometres long and nearly two kilometres wide. Even when there are fewer, they can form a close huddle known as a bait ball. As a predator approaches, the fish suddenly dart from the centre of the ball, causing “the confusion effect”, a split second where predators are overwhelmed. “That gives fish a bit of an edge, a bit more time to seek shelter,” says Damien Farine, a behavioural and movement ecologist at ANU. Stragglers, however, become easy pickings. Predators affect starling murmuration too: their size and duration increase when peregrine falcons or sparrowhawks are present, British researchers found in 2017, prompting swift outward bursts or divisions into subgroups.

In Australia, starlings were introduced in the 19th century and flock in rural areas in the south-east, but it’s budgies that capture the eye of birdwatchers, says Sean Dooley from Birdlife Australia. “You’ve got the red dirt, the blue sky, the green of the growth after the rainfall, and then these big wheeling flocks of green birds belting along.” The birds are forced to congregate due to limited food sources in the outback. “They live a life of being in the crowd, and that creates a great temptation for predators,” Dooley says. “They can just adjust their flight in that instant, in that microsecond. And to us, it looks like it’s somehow synchronised, like as if it’s a super-consciousness.”

Budgerigars in the outback. Credit: Getty Images/iStockphoto

In Britain, birdwatcher Edmund Selous was convinced starlings must communicate telepathically. Unfortunately, the likely truth, as suggested by high-speed photography, is more banal: that individuals follow the movements of their near-neighbours (with an eye on each side of its head, a bird has wide-angle “binocular” vision) rather than some central, high-speed command to turn left or right.

Still, exactly why starlings murmurate before they come in to roost remains something of a mystery. Some researchers wonder whether delaying the roosting serves a purpose, such as signalling where the roost site is and possibly recruiting other birds so the site becomes warmer.

‘Half an hour after sunset, when the light has disappeared, they suddenly throw themselves down onto the chosen trees.’

“It is highly probable that their celebrated aerial choreography represents a signal, visible from a great distance, indicating the location of a suitable place to spend the night,” writes Giorgio Parisi, the Nobel-prizewinning physicist, in his book on complex systems, In a Flight of Starlings . “The first small groups to arrive from the countryside begin to dance in a way that becomes ever more frenetic as the light fades. Gradually, the latecomers join until, in the end, flocks of thousands of individuals are formed. Half an hour after sunset, when the light has disappeared, they suddenly throw themselves down onto the chosen trees.”

Starlings at Gretna Green in the south of Scotland in 2017. Credit: Getty Images, digitally tinted

Using high-speed photography to reveal the mysteries of murmurations in Rome, Parisi writes that he was surprised to discover that the density of birds at the edge of the flock was nearly 30 per cent greater than in the middle, “a bit like what happens on crowded buses, where frequently the crush is greatest near the doors …” He theorised this was in response to possible threats. “The birds at the margins tend to bunch up as a defence mechanism, whereas those in the centre do not need to huddle in order to feel safe: they are already protected by their fellows at the edges.”

‘Subgroups of locusts might start marching in different directions but, eventually … everyone aligns in a common direction.’

In schools of fish and flocks of birds, in a fraction of a moment, hundreds of fish or birds can move without having seen a predator themselves. “They don’t have vision beyond those eight or 10 neighbours around them,” says Farine. “These collectives are effective at producing information cascades. For example, an individual at the right-hand edge of a group spots a predator to its right, it’ll turn to the left and will precipitate a left turn by all those individuals to the left of it.” Farine stops short of calling these “rational” decisions, though. “It’s just a behavioural cue or a behaviour rule that they follow.”

The same could be said of locusts, says Camille Buhl, a senior lecturer at the University of Adelaide who researches swarming insects. A singular path emerges from myriad interactions. “This means that every day, there is usually a phase where there is a bit of disorder at first when different subgroups of locusts might start marching in different directions, but eventually, order propagates through the whole group, and everyone aligns in a common direction.”

Starlings at Gretna Green in 2016. Credit: Getty Images, digitally tinted

Are some swarms smarter than others?

“The most evolved swarm intelligence is seen in social insects,” says Bristol University’s Ioannou. Many honey bees in a hive, for example, will be the offspring of one queen. “Being related means there is more likely to be a common goal for the group and less conflict within the group. Stable group membership and being able to recognise individuals within the group helps with cooperation because individuals can reciprocate a favour or avoid others who have ‘cheated’ them in the past.”

Indeed, says ANU evolutionary biologist Alexander Mikheyev, “A honeybee worker does not reproduce. Her entire life is dedicated towards the health and success of her colony. And the one who actually leaves offspring is the queen, who is typically the mother of the worker. So it’s a way of, basically, helping your relatives leave more offspring.”

When bees swarm because a colony needs to upsize its nest, some will depart in a wave, then pause while just a couple of scouts scope a new home. “They have a few days where they hang around, living on reserves that they’ve carried with them inside their bodies,” says Mikheyev. “They have to make a consensus decision for all of them to go to one place. They actually almost always pick the very best one based on a sort of criteria of what bees like.”

‘They watched ants solve the problem of finding the shortest route to the food source when presented with the choice of two possible paths, one longer than the other.’

Ants are similarly co-operative, says ecologist Mark Elgar, sometimes with extraordinary results. They recruit armies of soldiers to defend against enemies, deliberately deceive neighbouring colonies as to their true size, collect the right kinds of food for growing larvae, and use their own bodies to build bridges so others can reach food sources.

In an experiment conducted in 1989 , says Beni, “They watched ants solve the problem of finding the shortest route to the food source when presented with the choice of two possible paths, one longer than the other. At first, the ants randomly choose one or the other path. But after returning a few times from the location of the food source to their ‘base camp’, they begin to all choose the shortest path.”

Among other creatures, such as fish, says Camille Buhl, it isn’t so much that individuals prioritise the group over themselves but that what’s good for the collective is good for them too. When schooling fish detect danger and begin to flee from it, certainly they are helping their comrades by signalling the threat but also gain the opportunity to get away first. Says Buhl: “Individuals who engage in collective behaviour reap some extra benefit from it without paying much of a cost.”

Individuals cross the road in a collective flow (kind of) in Tokyo. Credit: Getty Images, digitally tinted

Do human beings have swarm intelligence?

“For humans, many more factors enter than for animals,” Helmut Satz, physicist and author of The Rules of the Flock , tells us from Biefeld in Germany. “There are, of course, similar aspects. Let me mention just one. A swarm of fish continues to swim in a given direction, even if one or two members turn off. Only if some 10 or more turn off, the entire swarm follows. Pedestrians waiting to cross a street at a red traffic light also remain there if one or two persons start to cross against the light. But if a significant fraction of those waiting decides to cross, the whole group will follow.”

‘They try to move towards the exit, almost aligning with each other, even if they are strangers.’

Certainly, some human behaviours seem to at least mimic the swarming tendencies of animals and insects. “When you are in a crowd after you leave the stadium, for example, after a big match, the way people move, they try to avoid local collisions,” says UNSW’s Hussein Abbass. “They try to move towards the exit, almost aligning with each other, even if they are strangers to one another.”

We mostly “swarm”, too, on the road in our cars, generally co-operating, with annoying exceptions. And the rail systems of New York and Tokyo would collapse if commuters failed to at least pay lip service to respecting personal space, including by avoiding eye contact.

A throng of commuters pauses in Taipei. Credit: Getty Images, digitally tinted

Democracy, fundamentally, is a form of swarm intelligence in so far as it’s a system where tiny individual acts together influence the path of the whole; as is a group of friends deciding where to eat out, suggests Ioannou. “If everyone has mixed opinions about the possible restaurants, discussing it as a group can lead to a better outcome overall than if one person is left to decide.” Opinion polls and crowdsourced sites such as Reddit, Quora and Wikipedia are in a similar ballpark, possibly Google and TripAdvisor reviews too, though their efficacy can be contentious.

Flying in formation in military aircraft is not unlike the behaviour of geese migrating in a flock, maximising aerodynamic advantage (the trailing aircraft can “draft” a little off the leader to save energy) while keeping beady eyes out for predators. “When flying in cruise formation,” says the US Navy’s 2017 flight training instruction manual , “remember that all aircraft are a team … The most common error in formation operations is the lead pilot’s inability to manage more than one aircraft. All crews must think about all aircraft.”

‘From an evolutionary perspective, we are not in our comfort zone when we take to the air.’

In fact, animals are better at formation, a Royal Australian Air Force officer tells us, using sound, air pressure and other cues to determine their flight paths, unlike humans, who tend to rely on sight alone.

“From an evolutionary perspective, we are not in our comfort zone when we take to the air,” the officer, who requested their name not be shared, tells us. “It gets a little more augmented when you talk about helicopter operations, or transport aircraft – where you have people in the back, mission crew who can look out the window and they can tell the pilots what’s happening out the left-hand side, and the back.”

Royal Australian Air Force F-111 aircraft during exercises with the US Air Force over Nevada in 2006. Credit: Getty Images, digitally tinted

There are also situations where complete co-operation is not only desirable but essential. Elite sports teams, for example, succeed when each individual works for the overall good. The success of New Zealand’s all-conquering All Blacks rugby team has been, at least partly, attributed to a culture that prioritises the group’s welfare over that of individuals, with mantras such as “Pass the ball: leaders create leaders.”

The victory of Iceland’s men’s soccer team over a (technically) much stronger England team in the 2016 European Championships was also ascribed to a culture in which everyone’s views were respected. As Dave Robson writes in his book The Intelligence Trap, “Many commentators highlighted the down-to-earth attitude of Heimir Hallgrímsson, one of Iceland’s two coaches, who still worked part-time as a dentist. He was apparently devoted to listening and understanding others’ points of view, and he tried to cultivate that attitude in all of his players.”

Players for Iceland block a ball in their European Championships match against England in 2016. Credit: Getty Images, digitally tinted

Still, sport is something of a flawed analogy given how strongly teams, whatever their culture and motivations, rely on real-time leadership from captains and coaches, training, and strategy: no elite teams would dare to simply rely on the wisdom of the swarm.

Consider, then, the “dabbawala” food delivery service in Mumbai, India, which began in 1890 and thrives today. Often studied by Western management academics, the dabbawalas are a co-operative network of riders and sorters who each day pick up some 130,000 home-cooked meals from the suburbs in layered steel containers called tiffin boxes, or dabba, and successfully deliver them to workers downtown by lunchtime. Each dabba typically passes through six pairs of hands, on train and bicycle, with no apps, only handwritten labels and the understanding among the dabbawalas that each plays a crucial role in the overall welfare of the network.

“The dabbawalas strictly observe certain rules,” noted the Harvard Business Review in 2012. “Workers have 40 seconds to load the crates of dabbas onto a train at major stations and just 20 seconds at interim stops. The tight schedule helps synchronise everyone and imposes discipline in an environment that might otherwise be chaotic.” On the flipside, customers who are routinely tardy in preparing their dabbas for pickup are discarded as clients, as they pose a threat to the efficiency of the network.

Mumbai’s dabbawalas work together to deliver 130,000 lunches every day. Credit: Getty Images, digitally tinted

How else could swarm intelligence be used?

In his 2004 bestseller The Wisdom of Crowds , James Surowiecki argued there were many examples of how groups solved problems better than even expert individuals. This ranged from contestants on Who Wants to be a Millionaire? asking the studio audience for answers to how investors in the sharemarket react swiftly to bad news. “Collective decisions are most likely to be good ones when they’re made by people with diverse opinions reaching independent conclusions, relying primarily on their private information,” he concludes.

“Groups can solve problems that individuals within the group would not be able to solve on their own,” agrees Lisa O’Bryan. “This is because social interactions between individuals enable the processing and combination of different types of information possessed by different group members.”

In the United States, a company called Unanimous AI is attempting to harness this phenomenon for specific goals: predicting the results of sporting events or political contests and using software to help individuals discuss issues with others and, crucially, contribute their opinions or votes, without being overly influenced by others. They call it “swarming”. “Unlike votes, polls, surveys or prediction markets, which treat each participant as a passive source of data for statistical aggregation, ‘swarming’ treats each person as an active member of a real-time control system, enabling the full population to think together in synchrony and converge on optimised solutions as a unified amplified intelligence,” company chief executive and founder, computer scientist Louis Rosenberg, explained in a 2019 paper.

His company, headquartered in San Francisco, has already shown that swarm intelligence can help groups of doctors to make more accurate diagnoses and groups of financial traders to better forecast markets. “I am confident,” he tells us, “that within the next five to 10 years, we will be able to enable very large human groups to think together as a collective superintelligence that can solve problems no human could ever solve on their own.”

‘The reports that were generated were of distinctly higher quality than if you had a bunch of experts working on it.’

At the University of Melbourne, computer scientist Richard Sinnott has experimented with similar systems designed to allow groups of people to interact and come to decisions, such as on whether or not a photograph is faked, without being influenced by strong personalities or group hierarchies. In a typical military environment, for example, “you might have a commander, and nobody’s going to disagree with him, even though they know he’s wrong” (also known as HiPPO or “Highest-Paid Per­son’s Opin­ion” syn­drome).

Allowing individuals to interact anonymously dispenses with social bias. “This system is really designed around an online team-based collaboration to answer questions,” he says. “We set it up in such a way that you could be quite free to chat informally with everyone else on the platform. You have no idea if you’re speaking to the director of intelligence or if you’re chatting with a cleaner. Teams rate each other’s answers to a given question that has been posed and then collectively work on the answer that was rated most highly to provide the best possible solution, leveraging everyone’s inputs. The reports that were generated were of distinctly higher quality than if you had a bunch of experts working on it.”

The other major application for swarm intelligence allows machines to be less dependent on us, their masters. Think of self-driving cars that interact with other cars to safely choose the quickest routes, groups of robots that clean large homes and commercial spaces, or communications networks that behave like ant colonies, leaving trails of electronic “pheromones” that evaporate over time.

Then there are the military applications. “Swarming drones are a big trend for the future,” says Malcolm Davis, a senior analyst at the Australian Strategic Policy Institute. “What we should expect to see in coming wars is our legacy military systems, our tanks, our fighter aircraft, our ships and so forth, being confronted with large swarms of drones which could potentially be operating independently of human control because they’re operating under artificial intelligence.”

The algorithms underlying the interactions between swarm robots and drone swarms are “frequently inspired by how groups of animals, such as birds and fish, navigate and share information about their environment,” says O’Bryan. China Elec­tron­ics Tech­no­logy Group broke the world record for the world’s largest-ever swarm way back in 2017, with 119 drones costing just a few hun­dred dol­lars each. “This goes all the way back to the tac­tics of Attila the Hun,” Ran­dall Steeb, senior engin­eer at the Rand Cor­por­a­tion in the US, told the Financial Times . “A light attack force that can defeat more power­ful and soph­ist­ic­ated oppon­ents. They come out of nowhere, attack from all sides and then dis­ap­pear, over and over.”

Last year, the Pentagon revealed plans to buy thousands of unmanned drones and other autonomous devices over the next two years in what it called its “Replicator” initiative. According to The New York Times , that might include “flocks” of flying, bomb-equipped drones that can loiter until they acquire a target while sharing information with other drones. The Pentagon described the technology as “small, smart, cheap” and a possible hedge against possible conflict with China. “Replicator is meant to help us overcome the PRC’s biggest advantage, which is mass. More ships. More missiles. More people,” said the US Deputy Secretary of Defence, Kathleen Hicks.

Gerardo Beni, the electrical engineer who coined swarm intelligence, or SI, agrees. “The greatest potential, in my opinion, is in the defence area. SI does not require highly trained, highly competent agents,” he says. “All they have to do is to follow a basic algorithm. The swarm can more easily escape enemy detection, no one knows the overall strategy and objective, it is not vulnerable to the loss of the leader, and its units are not ‘unique’ and can be replaced when some are lost.” For the RAAF member we spoke to, swarm intelligence isn’t something to be afraid of – yet.“I’m very happy that even very expensive robots can be in a war zone instead of people,” the officer says. “I am not fearful for my generation or the next generation. But I don’t know what it looks like when we get to full autonomy.”

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