problem solving examples in philosophy

The finalists will discuss ethical dilemmas

Rachel Carter (Flickr)

How to solve real-world problems by thinking philosophically

This week, Oxford students will investigate ethical puzzles - from the everyday to the extraordinary - through a practical lens.

The Oxford Uehiro Prize in Practical Ethics has been organised by the Oxford Uehiro Centre for Practical Ethics in Oxford University’s Faculty of Philosophy. The four finalists in the competition will present their cases in an event on Thursday 12 March which is open to the public.

Professor Julian Savulescu, director of the Centre, said: 'This competition aims to bring students from across Oxford together to think about an issue in practical ethics, drawing on their own expertise whether that is philosophy, politics, theology, or even science or medicine.

'Whatever career our students choose, a workforce which is trained to identify ethical problems, think logically about how and why they occur, and find an ethical solution will be a positive step forward for the future.'

The four philosophy students have given Arts Blog a preview of their arguments.

Should I stop playing music in my room because my neighbour can hear it through the wall?

According to Miles Unterreiner, a graduate student at St John's College, we all engage with practical ethics, whether we're aware of it or not.

'Supposing my displeased neighbour wants me to stop listening to music because she can hear it through the wall,' he said.

'I think I have a right to play music in my own room. Should she buy earplugs, or am I obligated to buy headphones?

This is a small and relatively insignificant example, one of the many questions about right and wrong that we ask ourselves every day.

How many lives can you save?

'If we care about the well-being of others, we should try to improve the lives of as many people as possible by as much as possible,’ said Dillon Bowen of Pembroke College, who has researched the most effective ways to give to charity.

'Now, when you first hear this, it seems like a strikingly obvious idea. If I donate £100 to charity, and I have the choice between donating to a charity which can save two children from starvation, or one which can save 20 children, I ought to choose the latter.

'But these sorts of economic questions don't often enter into people's minds when they donate money. People see someone in need, feel a strong visceral desire to help, and donate to the cause. End of moral calculus.

'But when it comes to morality, we need to think more reasonably. It's good that we want to help people, but bad that the way we go about doing it is so ineffective. We need to retain the altruistic intuition to help others, but use our reason to make sure we're helping others effectively. '

How should you live if you care about animals?

Xav Cohen, of Balliol College, is vegan because he cares about the harm that comes to animals from humans eating meat and using animal products. But it's hard to say how vegans should behave if they really want to minimise harm to animals: should they try to convince as many people as possible to adopt a fully vegan lifestyle?

'I found that vegans should really be looking to build a broad and accessible social movement which allows people to reduce their consumption of animal products, rather than condemning anything that isn't full veganism,' he said.

'This will lead to less harm to animals overall. What's needed is a popular label or movement which is plural and accepting, with the only requirement that we do more to reduce harm to animals. '

Should people be allowed to have breast implant surgery if it will harm them?

'Some would argue that a woman’s decision to have breast implants is morally unproblematic, as long as the woman is not coerced into having the surgery,' said Jessica Laimann, also of Balliol College. 'But if women believe that their success, self-worth, and even their careers depend on their appearance, is this still the case? Arguably, breast implant surgery is significantly harmful.'

So should we prohibit this kind of surgery in order to protect people from harming themselves?

'I immediately feel uneasy about the idea of prohibition. There is something deeply problematic about letting a society create people with the desire to inflict harm on themselves, and then trying to solve the problem by prohibiting these people from acting on that desire.

'Prohibiting breast implant surgery would put the lion’s share of the costs of changing harmful social norms on the people who already suffer most from them. Instead, we need to forcefully address the circumstances that make them willing to harm themselves in the first place.'

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Philosophical Problems

Let’s start off easy. A “Philosophical Problem” is like a super tough riddle about life and the universe that even the smartest people can’t quite solve. Imagine you’ve found a strange puzzle box at a garage sale with no instructions. Opening it is tough because you don’t know how it works, yet you have a feeling that you can figure it out. That’s what a philosophical problem is like.

Now, to be more detailed, a philosophical problem is a hard question about life, reality, and what it means to be a good person. It’s not something that can be answered with a calculator or a crazy invention. It’s the kind of question that might keep you awake at night because the answer doesn’t come easily. Philosophers are people who can’t help but wonder about these questions, like why we dream or if there’s a perfect way to live.

Approaching the Problems

So, how do you start figuring out these brain-twisters? Think big! Ask yourself those weird questions. Why is there anything at all? Is there a way to live the best life possible? You don’t need fancy gadgets for this; your brain is your best tool. Talk about it with friends or someone who loves to think deeply about things. Read books by people who have been thinking about these problems for years. and why not try writing your thoughts down? That can really help make things clear.

Types of Philosophical Problems

There are different types of philosophical problems, kind of like different genres of video games. Some you might battle through like an epic adventure, and some are more like puzzles that need solving. Now, imagine all the diverse games out there; philosophical problems are just as varied:

Metaphysical problems : These are like the mysteries of the universe. They make us wonder about things we can’t see or touch but somehow just know are there.
Epistemological problems : This is like the maze of knowledge . It’s all about questioning how we learn things and what it means to truly “know” something.
Ethical problems : Picture a crossroads where each path is a different choice between right and wrong. These are the problems that deal with what we should do or shouldn’t do.
Logical problems : These are like brain training puzzles. They make us think twice about how we make sense of things and argue our points.
Aesthetic problems : Imagine standing in an art gallery, wondering why one painting makes you feel happy and another makes you feel sad. These questions are about art and beauty.
Political philosophy problems : This is like the strategy in a multiplayer game where everyone has to decide on the rules and how to play fair. They focus on law, society, and what being fair means.

This one is a classic head-scratcher because it makes us question everything around us. It’s like questioning if a movie is real life or just a bunch of pictures flashing quickly.

This is about figuring out if we can ever be 100% sure about anything. It’s like trying to find your way around a new town without a map—you’re not always sure you’re going the right way, even if you think you are.

It’s like looking at an intricate toy and wondering who made it and why. We’re part of something much bigger, and it’s strange to think why there’s anything instead of just empty space.

This is all about the mystery of how our brains and bodies work together. It’s like having two teammates in a game who need to work perfectly together, but you’re not quite sure how they communicate.

This question is like wondering if we are the players or just characters in a video game following a script. It’s all about choice and whether we’re really in control of our actions or not.

Why Is Philosophy Important?

Knowing about philosophy is a big deal because it’s like training for your mind. It helps you think more clearly and ask better questions. Whenever you’re trying to figure out the tricky stuff, like what to believe, philosophy gives you the moves to do it. It helps make you a keen idea detective, always ready to learn something new or see things from a different side.

Plus, it’s not just about knowing stuff—it’s about how you live your life. Philosophy encourages you to dig into the real meaning behind everyday things, and that can make your life richer and more interesting. Bottom line, it can help you stand up for what you believe in and be the kind of person you want to be.

Origin of Philosophical Problems

These brain teasers aren’t new. Think of them as vintage, like old vinyl records that are still cool today. Ancient guys like Socrates, Plato , and Aristotle were some of the first to ask these big questions. They set the stage, and since then, people from all over the world and all through history have been adding their own thoughts into the mix.

Controversies in Philosophy

Because these puzzles have no clear answers, people often end up in debates. Philosophers have argued for ages, throwing different ideas back and forth. One big question today is whether philosophy still matters, now that we know so much about the world through science. But many say it’s more important than ever because it helps us handle new challenges and changes in our world.

Why Do Philosophical Problems Persist?

Philosophical problems stick around because they aren’t like a math quiz that has clear answers. They are tied to who we are as people, and they grow and change as we meet new thinkers, discover new things, and as the world changes around us.

Philosophy in Everyday Life

Believe it or not, you’re probably doing philosophy without even realizing it. When you debate with your friends about what’s fair or not, or wonder about the truth of something, you’re being a philosopher. It’s not just a thing for old guys in libraries; it’s for everybody trying to work out life’s big puzzles.

48 Problem Solving

Department of Psychological and Brain Sciences, University of California, Santa Barbara

Published: 03 June 2013
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Problem solving refers to cognitive processing directed at achieving a goal when the problem solver does not initially know a solution method. A problem exists when someone has a goal but does not know how to achieve it. Problems can be classified as routine or nonroutine, and as well defined or ill defined. The major cognitive processes in problem solving are representing, planning, executing, and monitoring. The major kinds of knowledge required for problem solving are facts, concepts, procedures, strategies, and beliefs. Classic theoretical approaches to the study of problem solving are associationism, Gestalt, and information processing. Current issues and suggested future issues include decision making, intelligence and creativity, teaching of thinking skills, expert problem solving, analogical reasoning, mathematical and scientific thinking, everyday thinking, and the cognitive neuroscience of problem solving. Common themes concern the domain specificity of problem solving and a focus on problem solving in authentic contexts.

The study of problem solving begins with defining problem solving, problem, and problem types. This introduction to problem solving is rounded out with an examination of cognitive processes in problem solving, the role of knowledge in problem solving, and historical approaches to the study of problem solving.

Definition of Problem Solving

Problem solving refers to cognitive processing directed at achieving a goal for which the problem solver does not initially know a solution method. This definition consists of four major elements (Mayer, 1992 ; Mayer & Wittrock, 2006 ):

Cognitive —Problem solving occurs within the problem solver’s cognitive system and can only be inferred indirectly from the problem solver’s behavior (including biological changes, introspections, and actions during problem solving). Process —Problem solving involves mental computations in which some operation is applied to a mental representation, sometimes resulting in the creation of a new mental representation. Directed —Problem solving is aimed at achieving a goal. Personal —Problem solving depends on the existing knowledge of the problem solver so that what is a problem for one problem solver may not be a problem for someone who already knows a solution method.

The definition is broad enough to include a wide array of cognitive activities such as deciding which apartment to rent, figuring out how to use a cell phone interface, playing a game of chess, making a medical diagnosis, finding the answer to an arithmetic word problem, or writing a chapter for a handbook. Problem solving is pervasive in human life and is crucial for human survival. Although this chapter focuses on problem solving in humans, problem solving also occurs in nonhuman animals and in intelligent machines.

How is problem solving related to other forms of high-level cognition processing, such as thinking and reasoning? Thinking refers to cognitive processing in individuals but includes both directed thinking (which corresponds to the definition of problem solving) and undirected thinking such as daydreaming (which does not correspond to the definition of problem solving). Thus, problem solving is a type of thinking (i.e., directed thinking).

Reasoning refers to problem solving within specific classes of problems, such as deductive reasoning or inductive reasoning. In deductive reasoning, the reasoner is given premises and must derive a conclusion by applying the rules of logic. For example, given that “A is greater than B” and “B is greater than C,” a reasoner can conclude that “A is greater than C.” In inductive reasoning, the reasoner is given (or has experienced) a collection of examples or instances and must infer a rule. For example, given that X, C, and V are in the “yes” group and x, c, and v are in the “no” group, the reasoning may conclude that B is in “yes” group because it is in uppercase format. Thus, reasoning is a type of problem solving.

Definition of Problem

A problem occurs when someone has a goal but does not know to achieve it. This definition is consistent with how the Gestalt psychologist Karl Duncker ( 1945 , p. 1) defined a problem in his classic monograph, On Problem Solving : “A problem arises when a living creature has a goal but does not know how this goal is to be reached.” However, today researchers recognize that the definition should be extended to include problem solving by intelligent machines. This definition can be clarified using an information processing approach by noting that a problem occurs when a situation is in the given state, the problem solver wants the situation to be in the goal state, and there is no obvious way to move from the given state to the goal state (Newell & Simon, 1972 ). Accordingly, the three main elements in describing a problem are the given state (i.e., the current state of the situation), the goal state (i.e., the desired state of the situation), and the set of allowable operators (i.e., the actions the problem solver is allowed to take). The definition of “problem” is broad enough to include the situation confronting a physician who wishes to make a diagnosis on the basis of preliminary tests and a patient examination, as well as a beginning physics student trying to solve a complex physics problem.

Types of Problems

It is customary in the problem-solving literature to make a distinction between routine and nonroutine problems. Routine problems are problems that are so familiar to the problem solver that the problem solver knows a solution method. For example, for most adults, “What is 365 divided by 12?” is a routine problem because they already know the procedure for long division. Nonroutine problems are so unfamiliar to the problem solver that the problem solver does not know a solution method. For example, figuring out the best way to set up a funding campaign for a nonprofit charity is a nonroutine problem for most volunteers. Technically, routine problems do not meet the definition of problem because the problem solver has a goal but knows how to achieve it. Much research on problem solving has focused on routine problems, although most interesting problems in life are nonroutine.

Another customary distinction is between well-defined and ill-defined problems. Well-defined problems have a clearly specified given state, goal state, and legal operators. Examples include arithmetic computation problems or games such as checkers or tic-tac-toe. Ill-defined problems have a poorly specified given state, goal state, or legal operators, or a combination of poorly defined features. Examples include solving the problem of global warming or finding a life partner. Although, ill-defined problems are more challenging, much research in problem solving has focused on well-defined problems.

Cognitive Processes in Problem Solving

The process of problem solving can be broken down into two main phases: problem representation , in which the problem solver builds a mental representation of the problem situation, and problem solution , in which the problem solver works to produce a solution. The major subprocess in problem representation is representing , which involves building a situation model —that is, a mental representation of the situation described in the problem. The major subprocesses in problem solution are planning , which involves devising a plan for how to solve the problem; executing , which involves carrying out the plan; and monitoring , which involves evaluating and adjusting one’s problem solving.

For example, given an arithmetic word problem such as “Alice has three marbles. Sarah has two more marbles than Alice. How many marbles does Sarah have?” the process of representing involves building a situation model in which Alice has a set of marbles, there is set of marbles for the difference between the two girls, and Sarah has a set of marbles that consists of Alice’s marbles and the difference set. In the planning process, the problem solver sets a goal of adding 3 and 2. In the executing process, the problem solver carries out the computation, yielding an answer of 5. In the monitoring process, the problem solver looks over what was done and concludes that 5 is a reasonable answer. In most complex problem-solving episodes, the four cognitive processes may not occur in linear order, but rather may interact with one another. Although some research focuses mainly on the execution process, problem solvers may tend to have more difficulty with the processes of representing, planning, and monitoring.

Knowledge for Problem Solving

An important theme in problem-solving research is that problem-solving proficiency on any task depends on the learner’s knowledge (Anderson et al., 2001 ; Mayer, 1992 ). Five kinds of knowledge are as follows:

Facts —factual knowledge about the characteristics of elements in the world, such as “Sacramento is the capital of California” Concepts —conceptual knowledge, including categories, schemas, or models, such as knowing the difference between plants and animals or knowing how a battery works Procedures —procedural knowledge of step-by-step processes, such as how to carry out long-division computations Strategies —strategic knowledge of general methods such as breaking a problem into parts or thinking of a related problem Beliefs —attitudinal knowledge about how one’s cognitive processing works such as thinking, “I’m good at this”

Although some research focuses mainly on the role of facts and procedures in problem solving, complex problem solving also depends on the problem solver’s concepts, strategies, and beliefs (Mayer, 1992 ).

Historical Approaches to Problem Solving

Psychological research on problem solving began in the early 1900s, as an outgrowth of mental philosophy (Humphrey, 1963 ; Mandler & Mandler, 1964 ). Throughout the 20th century four theoretical approaches developed: early conceptions, associationism, Gestalt psychology, and information processing.

Early Conceptions

The start of psychology as a science can be set at 1879—the year Wilhelm Wundt opened the first world’s psychology laboratory in Leipzig, Germany, and sought to train the world’s first cohort of experimental psychologists. Instead of relying solely on philosophical speculations about how the human mind works, Wundt sought to apply the methods of experimental science to issues addressed in mental philosophy. His theoretical approach became structuralism —the analysis of consciousness into its basic elements.

Wundt’s main contribution to the study of problem solving, however, was to call for its banishment. According to Wundt, complex cognitive processing was too complicated to be studied by experimental methods, so “nothing can be discovered in such experiments” (Wundt, 1911/1973 ). Despite his admonishments, however, a group of his former students began studying thinking mainly in Wurzburg, Germany. Using the method of introspection, subjects were asked to describe their thought process as they solved word association problems, such as finding the superordinate of “newspaper” (e.g., an answer is “publication”). Although the Wurzburg group—as they came to be called—did not produce a new theoretical approach, they found empirical evidence that challenged some of the key assumptions of mental philosophy. For example, Aristotle had proclaimed that all thinking involves mental imagery, but the Wurzburg group was able to find empirical evidence for imageless thought .

Associationism

The first major theoretical approach to take hold in the scientific study of problem solving was associationism —the idea that the cognitive representations in the mind consist of ideas and links between them and that cognitive processing in the mind involves following a chain of associations from one idea to the next (Mandler & Mandler, 1964 ; Mayer, 1992 ). For example, in a classic study, E. L. Thorndike ( 1911 ) placed a hungry cat in what he called a puzzle box—a wooden crate in which pulling a loop of string that hung from overhead would open a trap door to allow the cat to escape to a bowl of food outside the crate. Thorndike placed the cat in the puzzle box once a day for several weeks. On the first day, the cat engaged in many extraneous behaviors such as pouncing against the wall, pushing its paws through the slats, and meowing, but on successive days the number of extraneous behaviors tended to decrease. Overall, the time required to get out of the puzzle box decreased over the course of the experiment, indicating the cat was learning how to escape.

Thorndike’s explanation for how the cat learned to solve the puzzle box problem is based on an associationist view: The cat begins with a habit family hierarchy —a set of potential responses (e.g., pouncing, thrusting, meowing, etc.) all associated with the same stimulus (i.e., being hungry and confined) and ordered in terms of strength of association. When placed in the puzzle box, the cat executes its strongest response (e.g., perhaps pouncing against the wall), but when it fails, the strength of the association is weakened, and so on for each unsuccessful action. Eventually, the cat gets down to what was initially a weak response—waving its paw in the air—but when that response leads to accidentally pulling the string and getting out, it is strengthened. Over the course of many trials, the ineffective responses become weak and the successful response becomes strong. Thorndike refers to this process as the law of effect : Responses that lead to dissatisfaction become less associated with the situation and responses that lead to satisfaction become more associated with the situation. According to Thorndike’s associationist view, solving a problem is simply a matter of trial and error and accidental success. A major challenge to assocationist theory concerns the nature of transfer—that is, where does a problem solver find a creative solution that has never been performed before? Associationist conceptions of cognition can be seen in current research, including neural networks, connectionist models, and parallel distributed processing models (Rogers & McClelland, 2004 ).

Gestalt Psychology

The Gestalt approach to problem solving developed in the 1930s and 1940s as a counterbalance to the associationist approach. According to the Gestalt approach, cognitive representations consist of coherent structures (rather than individual associations) and the cognitive process of problem solving involves building a coherent structure (rather than strengthening and weakening of associations). For example, in a classic study, Kohler ( 1925 ) placed a hungry ape in a play yard that contained several empty shipping crates and a banana attached overhead but out of reach. Based on observing the ape in this situation, Kohler noted that the ape did not randomly try responses until one worked—as suggested by Thorndike’s associationist view. Instead, the ape stood under the banana, looked up at it, looked at the crates, and then in a flash of insight stacked the crates under the bananas as a ladder, and walked up the steps in order to reach the banana.

According to Kohler, the ape experienced a sudden visual reorganization in which the elements in the situation fit together in a way to solve the problem; that is, the crates could become a ladder that reduces the distance to the banana. Kohler referred to the underlying mechanism as insight —literally seeing into the structure of the situation. A major challenge of Gestalt theory is its lack of precision; for example, naming a process (i.e., insight) is not the same as explaining how it works. Gestalt conceptions can be seen in modern research on mental models and schemas (Gentner & Stevens, 1983 ).

Information Processing

The information processing approach to problem solving developed in the 1960s and 1970s and was based on the influence of the computer metaphor—the idea that humans are processors of information (Mayer, 2009 ). According to the information processing approach, problem solving involves a series of mental computations—each of which consists of applying a process to a mental representation (such as comparing two elements to determine whether they differ).

In their classic book, Human Problem Solving , Newell and Simon ( 1972 ) proposed that problem solving involved a problem space and search heuristics . A problem space is a mental representation of the initial state of the problem, the goal state of the problem, and all possible intervening states (based on applying allowable operators). Search heuristics are strategies for moving through the problem space from the given to the goal state. Newell and Simon focused on means-ends analysis , in which the problem solver continually sets goals and finds moves to accomplish goals.

Newell and Simon used computer simulation as a research method to test their conception of human problem solving. First, they asked human problem solvers to think aloud as they solved various problems such as logic problems, chess, and cryptarithmetic problems. Then, based on an information processing analysis, Newell and Simon created computer programs that solved these problems. In comparing the solution behavior of humans and computers, they found high similarity, suggesting that the computer programs were solving problems using the same thought processes as humans.

An important advantage of the information processing approach is that problem solving can be described with great clarity—as a computer program. An important limitation of the information processing approach is that it is most useful for describing problem solving for well-defined problems rather than ill-defined problems. The information processing conception of cognition lives on as a keystone of today’s cognitive science (Mayer, 2009 ).

Classic Issues in Problem Solving

Three classic issues in research on problem solving concern the nature of transfer (suggested by the associationist approach), the nature of insight (suggested by the Gestalt approach), and the role of problem-solving heuristics (suggested by the information processing approach).

Transfer refers to the effects of prior learning on new learning (or new problem solving). Positive transfer occurs when learning A helps someone learn B. Negative transfer occurs when learning A hinders someone from learning B. Neutral transfer occurs when learning A has no effect on learning B. Positive transfer is a central goal of education, but research shows that people often do not transfer what they learned to solving problems in new contexts (Mayer, 1992 ; Singley & Anderson, 1989 ).

Three conceptions of the mechanisms underlying transfer are specific transfer , general transfer , and specific transfer of general principles . Specific transfer refers to the idea that learning A will help someone learn B only if A and B have specific elements in common. For example, learning Spanish may help someone learn Latin because some of the vocabulary words are similar and the verb conjugation rules are similar. General transfer refers to the idea that learning A can help someone learn B even they have nothing specifically in common but A helps improve the learner’s mind in general. For example, learning Latin may help people learn “proper habits of mind” so they are better able to learn completely unrelated subjects as well. Specific transfer of general principles is the idea that learning A will help someone learn B if the same general principle or solution method is required for both even if the specific elements are different.

In a classic study, Thorndike and Woodworth ( 1901 ) found that students who learned Latin did not subsequently learn bookkeeping any better than students who had not learned Latin. They interpreted this finding as evidence for specific transfer—learning A did not transfer to learning B because A and B did not have specific elements in common. Modern research on problem-solving transfer continues to show that people often do not demonstrate general transfer (Mayer, 1992 ). However, it is possible to teach people a general strategy for solving a problem, so that when they see a new problem in a different context they are able to apply the strategy to the new problem (Judd, 1908 ; Mayer, 2008 )—so there is also research support for the idea of specific transfer of general principles.

Insight refers to a change in a problem solver’s mind from not knowing how to solve a problem to knowing how to solve it (Mayer, 1995 ; Metcalfe & Wiebe, 1987 ). In short, where does the idea for a creative solution come from? A central goal of problem-solving research is to determine the mechanisms underlying insight.

The search for insight has led to five major (but not mutually exclusive) explanatory mechanisms—insight as completing a schema, insight as suddenly reorganizing visual information, insight as reformulation of a problem, insight as removing mental blocks, and insight as finding a problem analog (Mayer, 1995 ). Completing a schema is exemplified in a study by Selz (Fridja & de Groot, 1982 ), in which people were asked to think aloud as they solved word association problems such as “What is the superordinate for newspaper?” To solve the problem, people sometimes thought of a coordinate, such as “magazine,” and then searched for a superordinate category that subsumed both terms, such as “publication.” According to Selz, finding a solution involved building a schema that consisted of a superordinate and two subordinate categories.

Reorganizing visual information is reflected in Kohler’s ( 1925 ) study described in a previous section in which a hungry ape figured out how to stack boxes as a ladder to reach a banana hanging above. According to Kohler, the ape looked around the yard and found the solution in a flash of insight by mentally seeing how the parts could be rearranged to accomplish the goal.

Reformulating a problem is reflected in a classic study by Duncker ( 1945 ) in which people are asked to think aloud as they solve the tumor problem—how can you destroy a tumor in a patient without destroying surrounding healthy tissue by using rays that at sufficient intensity will destroy any tissue in their path? In analyzing the thinking-aloud protocols—that is, transcripts of what the problem solvers said—Duncker concluded that people reformulated the goal in various ways (e.g., avoid contact with healthy tissue, immunize healthy tissue, have ray be weak in healthy tissue) until they hit upon a productive formulation that led to the solution (i.e., concentrating many weak rays on the tumor).

Removing mental blocks is reflected in classic studies by Duncker ( 1945 ) in which solving a problem involved thinking of a novel use for an object, and by Luchins ( 1942 ) in which solving a problem involved not using a procedure that had worked well on previous problems. Finding a problem analog is reflected in classic research by Wertheimer ( 1959 ) in which learning to find the area of a parallelogram is supported by the insight that one could cut off the triangle on one side and place it on the other side to form a rectangle—so a parallelogram is really a rectangle in disguise. The search for insight along each of these five lines continues in current problem-solving research.

Heuristics are problem-solving strategies, that is, general approaches to how to solve problems. Newell and Simon ( 1972 ) suggested three general problem-solving heuristics for moving from a given state to a goal state: random trial and error , hill climbing , and means-ends analysis . Random trial and error involves randomly selecting a legal move and applying it to create a new problem state, and repeating that process until the goal state is reached. Random trial and error may work for simple problems but is not efficient for complex ones. Hill climbing involves selecting the legal move that moves the problem solver closer to the goal state. Hill climbing will not work for problems in which the problem solver must take a move that temporarily moves away from the goal as is required in many problems.

Means-ends analysis involves creating goals and seeking moves that can accomplish the goal. If a goal cannot be directly accomplished, a subgoal is created to remove one or more obstacles. Newell and Simon ( 1972 ) successfully used means-ends analysis as the search heuristic in a computer program aimed at general problem solving, that is, solving a diverse collection of problems. However, people may also use specific heuristics that are designed to work for specific problem-solving situations (Gigerenzer, Todd, & ABC Research Group, 1999 ; Kahneman & Tversky, 1984 ).

Current and Future Issues in Problem Solving

Eight current issues in problem solving involve decision making, intelligence and creativity, teaching of thinking skills, expert problem solving, analogical reasoning, mathematical and scientific problem solving, everyday thinking, and the cognitive neuroscience of problem solving.

Decision Making

Decision making refers to the cognitive processing involved in choosing between two or more alternatives (Baron, 2000 ; Markman & Medin, 2002 ). For example, a decision-making task may involve choosing between getting $240 for sure or having a 25% change of getting $1000. According to economic theories such as expected value theory, people should chose the second option, which is worth $250 (i.e., .25 x $1000) rather than the first option, which is worth $240 (1.00 x $240), but psychological research shows that most people prefer the first option (Kahneman & Tversky, 1984 ).

Research on decision making has generated three classes of theories (Markman & Medin, 2002 ): descriptive theories, such as prospect theory (Kahneman & Tversky), which are based on the ideas that people prefer to overweight the cost of a loss and tend to overestimate small probabilities; heuristic theories, which are based on the idea that people use a collection of short-cut strategies such as the availability heuristic (Gigerenzer et al., 1999 ; Kahneman & Tversky, 2000 ); and constructive theories, such as mental accounting (Kahneman & Tversky, 2000 ), in which people build a narrative to justify their choices to themselves. Future research is needed to examine decision making in more realistic settings.

Intelligence and Creativity

Although researchers do not have complete consensus on the definition of intelligence (Sternberg, 1990 ), it is reasonable to view intelligence as the ability to learn or adapt to new situations. Fluid intelligence refers to the potential to solve problems without any relevant knowledge, whereas crystallized intelligence refers to the potential to solve problems based on relevant prior knowledge (Sternberg & Gregorenko, 2003 ). As people gain more experience in a field, their problem-solving performance depends more on crystallized intelligence (i.e., domain knowledge) than on fluid intelligence (i.e., general ability) (Sternberg & Gregorenko, 2003 ). The ability to monitor and manage one’s cognitive processing during problem solving—which can be called metacognition —is an important aspect of intelligence (Sternberg, 1990 ). Research is needed to pinpoint the knowledge that is needed to support intelligent performance on problem-solving tasks.

Creativity refers to the ability to generate ideas that are original (i.e., other people do not think of the same idea) and functional (i.e., the idea works; Sternberg, 1999 ). Creativity is often measured using tests of divergent thinking —that is, generating as many solutions as possible for a problem (Guilford, 1967 ). For example, the uses test asks people to list as many uses as they can think of for a brick. Creativity is different from intelligence, and it is at the heart of creative problem solving—generating a novel solution to a problem that the problem solver has never seen before. An important research question concerns whether creative problem solving depends on specific knowledge or creativity ability in general.

Teaching of Thinking Skills

How can people learn to be better problem solvers? Mayer ( 2008 ) proposes four questions concerning teaching of thinking skills:

What to teach —Successful programs attempt to teach small component skills (such as how to generate and evaluate hypotheses) rather than improve the mind as a single monolithic skill (Covington, Crutchfield, Davies, & Olton, 1974 ). How to teach —Successful programs focus on modeling the process of problem solving rather than solely reinforcing the product of problem solving (Bloom & Broder, 1950 ). Where to teach —Successful programs teach problem-solving skills within the specific context they will be used rather than within a general course on how to solve problems (Nickerson, 1999 ). When to teach —Successful programs teaching higher order skills early rather than waiting until lower order skills are completely mastered (Tharp & Gallimore, 1988 ).

Overall, research on teaching of thinking skills points to the domain specificity of problem solving; that is, successful problem solving depends on the problem solver having domain knowledge that is relevant to the problem-solving task.

Expert Problem Solving

Research on expertise is concerned with differences between how experts and novices solve problems (Ericsson, Feltovich, & Hoffman, 2006 ). Expertise can be defined in terms of time (e.g., 10 years of concentrated experience in a field), performance (e.g., earning a perfect score on an assessment), or recognition (e.g., receiving a Nobel Prize or becoming Grand Master in chess). For example, in classic research conducted in the 1940s, de Groot ( 1965 ) found that chess experts did not have better general memory than chess novices, but they did have better domain-specific memory for the arrangement of chess pieces on the board. Chase and Simon ( 1973 ) replicated this result in a better controlled experiment. An explanation is that experts have developed schemas that allow them to chunk collections of pieces into a single configuration.

In another landmark study, Larkin et al. ( 1980 ) compared how experts (e.g., physics professors) and novices (e.g., first-year physics students) solved textbook physics problems about motion. Experts tended to work forward from the given information to the goal, whereas novices tended to work backward from the goal to the givens using a means-ends analysis strategy. Experts tended to store their knowledge in an integrated way, whereas novices tended to store their knowledge in isolated fragments. In another study, Chi, Feltovich, and Glaser ( 1981 ) found that experts tended to focus on the underlying physics concepts (such as conservation of energy), whereas novices tended to focus on the surface features of the problem (such as inclined planes or springs). Overall, research on expertise is useful in pinpointing what experts know that is different from what novices know. An important theme is that experts rely on domain-specific knowledge rather than solely general cognitive ability.

Analogical Reasoning

Analogical reasoning occurs when people solve one problem by using their knowledge about another problem (Holyoak, 2005 ). For example, suppose a problem solver learns how to solve a problem in one context using one solution method and then is given a problem in another context that requires the same solution method. In this case, the problem solver must recognize that the new problem has structural similarity to the old problem (i.e., it may be solved by the same method), even though they do not have surface similarity (i.e., the cover stories are different). Three steps in analogical reasoning are recognizing —seeing that a new problem is similar to a previously solved problem; abstracting —finding the general method used to solve the old problem; and mapping —using that general method to solve the new problem.

Research on analogical reasoning shows that people often do not recognize that a new problem can be solved by the same method as a previously solved problem (Holyoak, 2005 ). However, research also shows that successful analogical transfer to a new problem is more likely when the problem solver has experience with two old problems that have the same underlying structural features (i.e., they are solved by the same principle) but different surface features (i.e., they have different cover stories) (Holyoak, 2005 ). This finding is consistent with the idea of specific transfer of general principles as described in the section on “Transfer.”

Mathematical and Scientific Problem Solving

Research on mathematical problem solving suggests that five kinds of knowledge are needed to solve arithmetic word problems (Mayer, 2008 ):

Factual knowledge —knowledge about the characteristics of problem elements, such as knowing that there are 100 cents in a dollar Schematic knowledge —knowledge of problem types, such as being able to recognize time-rate-distance problems Strategic knowledge —knowledge of general methods, such as how to break a problem into parts Procedural knowledge —knowledge of processes, such as how to carry our arithmetic operations Attitudinal knowledge —beliefs about one’s mathematical problem-solving ability, such as thinking, “I am good at this”

People generally possess adequate procedural knowledge but may have difficulty in solving mathematics problems because they lack factual, schematic, strategic, or attitudinal knowledge (Mayer, 2008 ). Research is needed to pinpoint the role of domain knowledge in mathematical problem solving.

Research on scientific problem solving shows that people harbor misconceptions, such as believing that a force is needed to keep an object in motion (McCloskey, 1983 ). Learning to solve science problems involves conceptual change, in which the problem solver comes to recognize that previous conceptions are wrong (Mayer, 2008 ). Students can be taught to engage in scientific reasoning such as hypothesis testing through direct instruction in how to control for variables (Chen & Klahr, 1999 ). A central theme of research on scientific problem solving concerns the role of domain knowledge.

Everyday Thinking

Everyday thinking refers to problem solving in the context of one’s life outside of school. For example, children who are street vendors tend to use different procedures for solving arithmetic problems when they are working on the streets than when they are in school (Nunes, Schlieman, & Carraher, 1993 ). This line of research highlights the role of situated cognition —the idea that thinking always is shaped by the physical and social context in which it occurs (Robbins & Aydede, 2009 ). Research is needed to determine how people solve problems in authentic contexts.

Cognitive Neuroscience of Problem Solving

The cognitive neuroscience of problem solving is concerned with the brain activity that occurs during problem solving. For example, using fMRI brain imaging methodology, Goel ( 2005 ) found that people used the language areas of the brain to solve logical reasoning problems presented in sentences (e.g., “All dogs are pets…”) and used the spatial areas of the brain to solve logical reasoning problems presented in abstract letters (e.g., “All D are P…”). Cognitive neuroscience holds the potential to make unique contributions to the study of problem solving.

Problem solving has always been a topic at the fringe of cognitive psychology—too complicated to study intensively but too important to completely ignore. Problem solving—especially in realistic environments—is messy in comparison to studying elementary processes in cognition. The field remains fragmented in the sense that topics such as decision making, reasoning, intelligence, expertise, mathematical problem solving, everyday thinking, and the like are considered to be separate topics, each with its own separate literature. Yet some recurring themes are the role of domain-specific knowledge in problem solving and the advantages of studying problem solving in authentic contexts.

Future Directions

Some important issues for future research include the three classic issues examined in this chapter—the nature of problem-solving transfer (i.e., How are people able to use what they know about previous problem solving to help them in new problem solving?), the nature of insight (e.g., What is the mechanism by which a creative solution is constructed?), and heuristics (e.g., What are some teachable strategies for problem solving?). In addition, future research in problem solving should continue to pinpoint the role of domain-specific knowledge in problem solving, the nature of cognitive ability in problem solving, how to help people develop proficiency in solving problems, and how to provide aids for problem solving.

Anderson L. W. , Krathwohl D. R. , Airasian P. W. , Cruikshank K. A. , Mayer R. E. , Pintrich P. R. , Raths, J., & Wittrock M. C. ( 2001 ). A taxonomy for learning, teaching, and assessing: A revision of Bloom’s taxonomy of educational objectives. New York : Longman.

Baron J. ( 2000 ). Thinking and deciding (3rd ed.). New York : Cambridge University Press.

Google Scholar

Google Preview

Bloom B. S. , & Broder B. J. ( 1950 ). Problem-solving processes of college students: An exploratory investigation. Chicago : University of Chicago Press.

Chase W. G. , & Simon H. A. ( 1973 ). Perception in chess. Cognitive Psychology, 4, 55–81.

Chen Z. , & Klahr D. ( 1999 ). All other things being equal: Acquisition and transfer of the control of variable strategy . Child Development, 70, 1098–1120.

Chi M. T. H. , Feltovich P. J. , & Glaser R. ( 1981 ). Categorization and representation of physics problems by experts and novices. Cognitive Science, 5, 121–152.

Covington M. V. , Crutchfield R. S. , Davies L. B. , & Olton R. M. ( 1974 ). The productive thinking program. Columbus, OH : Merrill.

de Groot A. D. ( 1965 ). Thought and choice in chess. The Hague, The Netherlands : Mouton.

Duncker K. ( 1945 ). On problem solving. Psychological Monographs, 58 (3) (Whole No. 270).

Ericsson K. A. , Feltovich P. J. , & Hoffman R. R. (Eds.). ( 2006 ). The Cambridge handbook of expertise and expert performance. New York : Cambridge University Press.

Fridja N. H. , & de Groot A. D. ( 1982 ). Otto Selz: His contribution to psychology. The Hague, The Netherlands : Mouton.

Gentner D. , & Stevens A. L. (Eds.). ( 1983 ). Mental models. Hillsdale, NJ : Erlbaum.

Gigerenzer G. , Todd P. M. , & ABC Research Group (Eds.). ( 1999 ). Simple heuristics that make us smart. Oxford, England : Oxford University Press.

Goel V. ( 2005 ). Cognitive neuroscience of deductive reasoning. In K. J. Holyoak & R. G. Morrison (Eds.), The Cambridge handbook of thinking and reasoning (pp. 475–492). New York : Cambridge University Press.

Guilford J. P. ( 1967 ). The nature of human intelligence. New York : McGraw-Hill.

Holyoak K. J. ( 2005 ). Analogy. In K. J. Holyoak & R. G. Morrison (Eds.), The Cambridge handbook of thinking and reasoning (pp. 117–142). New York : Cambridge University Press.

Humphrey G. ( 1963 ). Thinking: An introduction to experimental psychology. New York : Wiley.

Judd C. H. ( 1908 ). The relation of special training and general intelligence. Educational Review, 36, 28–42.

Kahneman D. , & Tversky A. ( 1984 ). Choices, values, and frames. American Psychologist, 39, 341–350.

Kahneman D. , & Tversky A. (Eds.). ( 2000 ). Choices, values, and frames. New York : Cambridge University Press.

Kohler W. ( 1925 ). The mentality of apes. New York : Liveright.

Larkin J. H. , McDermott J. , Simon D. P. , & Simon H. A. ( 1980 ). Expert and novice performance in solving physics problems. Science, 208, 1335–1342.

Luchins A. ( 1942 ). Mechanization in problem solving. Psychological Monographs, 54 (6) (Whole No. 248).

Mandler J. M. , & Mandler G. ( 1964 ). Thinking from associationism to Gestalt. New York : Wiley.

Markman A. B. , & Medin D. L. ( 2002 ). Decision making. In D. Medin (Ed.), Stevens’ handbook of experimental psychology, Vol. 2. Memory and cognitive processes (2nd ed., pp. 413–466). New York : Wiley.

Mayer R. E. ( 1992 ). Thinking, problem solving, cognition (2nd ed). New York : Freeman.

Mayer R. E. ( 1995 ). The search for insight: Grappling with Gestalt psychology’s unanswered questions. In R. J. Sternberg & J. E. Davidson (Eds.), The nature of insight (pp. 3–32). Cambridge, MA : MIT Press.

Mayer R. E. ( 2008 ). Learning and instruction. Upper Saddle River, NJ : Merrill Prentice Hall.

Mayer R. E. ( 2009 ). Information processing. In T. L. Good (Ed.), 21st century education: A reference handbook (pp. 168–174). Thousand Oaks, CA : Sage.

Mayer R. E. , & Wittrock M. C. ( 2006 ). Problem solving. In P. A. Alexander & P. H. Winne (Eds.), Handbook of educational psychology (2nd ed., pp. 287–304). Mahwah, NJ : Erlbaum.

McCloskey M. ( 1983 ). Intuitive physics. Scientific American, 248 (4), 122–130.

Metcalfe J. , & Wiebe D. ( 1987 ). Intuition in insight and non-insight problem solving. Memory and Cognition, 15, 238–246.

Newell A. , & Simon H. A. ( 1972 ). Human problem solving. Englewood Cliffs, NJ : Prentice-Hall.

Nickerson R. S. ( 1999 ). Enhancing creativity. In R. J. Sternberg (Ed.), Handbook of creativity (pp. 392–430). New York : Cambridge University Press.

Nunes T. , Schliemann A. D. , & Carraher D. W , ( 1993 ). Street mathematics and school mathematics. Cambridge, England : Cambridge University Press.

Robbins P. , & Aydede M. (Eds.). ( 2009 ). The Cambridge handbook of situated cognition. New York : Cambridge University Press.

Rogers T. T. , & McClelland J. L. ( 2004 ). Semantic cognition: A parallel distributed processing approach. Cambridge, MA : MIT Press.

Singley M. K. , & Anderson J. R. ( 1989 ). The transfer of cognitive skill. Cambridge, MA : Harvard University Press.

Sternberg R. J. ( 1990 ). Metaphors of mind: Conceptions of the nature of intelligence. New York : Cambridge University Press.

Sternberg R. J. ( 1999 ). Handbook of creativity. New York : Cambridge University Press.

Sternberg R. J. , & Gregorenko E. L. (Eds.). ( 2003 ). The psychology of abilities, competencies, and expertise. New York : Cambridge University Press.

Tharp R. G. , & Gallimore R. ( 1988 ). Rousing minds to life: Teaching, learning, and schooling in social context. New York : Cambridge University Press.

Thorndike E. L. ( 1911 ). Animal intelligence. New York: Hafner.

Thorndike E. L. , & Woodworth R. S. ( 1901 ). The influence of improvement in one mental function upon the efficiency of other functions. Psychological Review, 8, 247–261.

Wertheimer M. ( 1959 ). Productive thinking. New York : Harper and Collins.

Wundt W. ( 1973 ). An introduction to experimental psychology. New York : Arno Press. (Original work published in 1911).

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Epistemology for interdisciplinary research – shifting philosophical paradigms of science

Paper in Philosophy of Science in Practice
Open access
Published: 12 December 2018
Volume 9 , article number 16 , ( 2019 )

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Mieke Boon ORCID: orcid.org/0000-0003-2492-2854 1 &
Sophie Van Baalen 1

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In science policy, it is generally acknowledged that science-based problem-solving requires interdisciplinary research. For example, policy makers invest in funding programs such as Horizon 2020 that aim to stimulate interdisciplinary research. Yet the epistemological processes that lead to effective interdisciplinary research are poorly understood. This article aims at an epistemology for interdisciplinary research (IDR), in particular, IDR for solving ‘real-world’ problems. Focus is on the question why researchers experience cognitive and epistemic difficulties in conducting IDR. Based on a study of educational literature it is concluded that higher-education is missing clear ideas on the epistemology of IDR, and as a consequence, on how to teach it. It is conjectured that the lack of philosophical interest in the epistemology of IDR is due to a philosophical paradigm of science (called a physics paradigm of science ), which prevents recognizing severe epistemological challenges of IDR, both in the philosophy of science as well as in science education and research. The proposed alternative philosophical paradigm (called an engineering paradigm of science ) entails alternative philosophical presuppositions regarding aspects such as the aim of science, the character of knowledge, the epistemic and pragmatic criteria for accepting knowledge, and the role of technological instruments. This alternative philosophical paradigm assume the production of knowledge for epistemic functions as the aim of science, and interprets ‘knowledge’ (such as theories, models, laws, and concepts) as epistemic tools that must allow for conducting epistemic tasks by epistemic agents, rather than interpreting knowledge as representations that objectively represent aspects of the world independent of the way in which it was constructed. The engineering paradigm of science involves that knowledge is indelibly shaped by how it is constructed. Additionally, the way in which scientific disciplines (or fields) construct knowledge is guided by the specificities of the discipline, which can be analyzed in terms of disciplinary perspectives . This implies that knowledge and the epistemic uses of knowledge cannot be understood without at least some understanding of how the knowledge is constructed. Accordingly, scientific researchers need so-called metacognitive scaffolds to assist in analyzing and reconstructing how ‘knowledge’ is constructed and how different disciplines do this differently. In an engineering paradigm of science, these metacognitive scaffolds can also be interpreted as epistemic tools, but in this case as tools that guide, enable and constrain analyzing and articulating how knowledge is produced (i.e., explaining epistemological aspects of doing research). In interdisciplinary research, metacognitive scaffolds assist interdisciplinary communication aiming to analyze and articulate how the discipline constructs knowledge.

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1 Introduction

1.1 background.

Most current scientific research in the natural, medical and engineering sciences is expected to be useful for specific ‘real-world’ problems, such as in agriculture and food-industry, (bio)medical technology and pharmacy, ICT and transport, civil engineering and sustainability, weather-forecasting and climate issues, safety and forensics, energy-technology and high-tech (nano-)materials. Real-world problems usually are complex, and philosophers of science emphasize that this requires interdisciplinary or transdisciplinary research (e.g., Schmidt 2008 , 2011 ; Frodeman 2010 ; Krohn 2010 ; Alvargonzález 2011 ; Thorén and Persson 2013 ; Thorén 2015 ; Maki 2016 ; O'Rourke et al. 2016 ). Outside the philosophy of science, the need for interdisciplinary research for real-world problem solving —and associated changes in the structure of universities and higher education systems— has been stressed for decades already (e.g., Jantsch 1972 ; Apostel et al. 1972 ; Klein 1990 , 1996 ; Kline 1995 ; Aram 2004 ; National Academy of Sciences 2005 ; Hirsch-Hadorn et al. 2010 ; Bammer 2013 ; and see overviews by Jacobs and Frickel 2009 , and Newell 2001 , 2013 ). These authors, specialized in science policy and education studies, also report that attempts to promote interdisciplinary research at universities and to train university students for conducting interdisciplinary research often do not lead to the desired results. Scholars with a policy or sociology background usually explain difficulties in establishing interdisciplinary research and education in terms of organization and tend to deny epistemological and cognitive factors.

In the philosophy of science, interdisciplinary research is studied, but with a few exceptions, hardly any philosophical research has been conducted into ‘why interdisciplinary research is so difficult’ (MacLeod 2016 ; Thorén 2015 ). The same applies to educational research, in which both conceptual studies and empirical research are conducted into higher education programs that train students in interdisciplinary (or transdisciplinary) approaches – in these studies too little attention is paid to the cognitive and epistemological difficulties encountered by researchers, in particular, regarding the question whether the ability to do interdisciplinary research requires a specific understanding of science and related cognitive skills.

The thesis put forward in this article consists of two parts. The first part is that the neglect of cognitive and epistemological difficulties in doing interdisciplinary research is, at least partly, due to philosophical beliefs about science that still guide the higher education of researchers and professionals. Addressing this claim requires to explain why interdisciplinary research is difficult, and to show that epistemological and cognitive difficulties have to do with philosophical presuppositions about science, regarding traditional themes such as reductionism, the unity of science, epistemology as ‘evidence for justified true belief,’ and the distinction between the context of discovery and the context of justification. We will argue that, in order to deal with the epistemological and cognitive difficulties, alternatives to these presuppositions must be sought that are better suit to understanding interdisciplinary research.

The second part of our thesis is that an alternative philosophical view of science can be based on Kuhn’s idea of disciplinary matrices (Kuhn 1970 ), in particular, on an expanded version of elements constituting the matrix. This expanded matrix has been used to articulate two different philosophical visions of science, called a physics paradigm of science versus an engineering paradigm of science (Boon 2017a ). Articulating these paradigms was intended to interpret the changing character of the biomedical sciences such as systems biology as compared to, for instance, classical biochemistry – and it was found that the engineering paradigm of science suits better to systems biology than the traditional physics paradigm. Extending this approach, we will argue that the engineering paradigm of science also fits better in understanding interdisciplinary scientific research in ‘real-world’ problem-solving contexts. The engineering paradigm of science makes it also possible to reconsider presuppositions associated with education in interdisciplinary research, which will be substantiated and illustrated by proposing enriched interpretations of some relevant concepts, such as ‘disciplinary perspectives,’ ‘higher-order cognitive skills’ (also called ‘metacognitive skills’), and ‘conceptual frameworks’ (in particular so-called ‘metacognitive scaffolds’).

Altogether, the aim of this article is to develop a vocabulary (a philosophical and conceptual framework) that enables detailed philosophical study of interdisciplinary research practices —particularly in ‘real-world’ problem-solving context— that takes into account the epistemic tasks and cognitive challenges as an inherent aspect of these practices, and also to indicate directions of how philosophers of science may contribute to the academic education therein. In developing this vocabulary, use is made of literature in scholarly domains ranging from philosophy of science, philosophy of education, science policy studies, and studies in (higher) education of science and engineering.

1.2 Structure

The structure of this article is as follows. Section Two focuses on scholarly disciplines that study interdisciplinary research (IDR). These studies appear to focus on organizational aspects of IDR and have little interest in epistemological and cognitive difficulties of interdisciplinary research as experienced by individual researchers (MacLeod 2016 ). This lack of interest is obviously due to the specialization of these scholars (e.g. in science policy) but also stems from the conviction that suitable organization of IDR can solve problems of IDR – a belief that we dispute. In particular, most scholars conceive of IDR as aiming at the integration of knowledge, which we consider to be the epistemologically most challenging part of IDR. We will clarify this in terms of three metaphors of IDR.

Section Three deals with the kind of expertise needed in IDR, and presents educational insights and empirical findings about training scientific researchers and professionals in IDR. It focuses on views about the role of metacognitive skills. It appears that little is being done to train metacognitive skills for IDR, which is explained in terms of dominant philosophical views of science that guide science education, and which critical educational researchers call a positivist view of science.

Section Four first discusses the claim that traditional philosophical views of science insufficiently account for the uses of science, i.e., how scientific research generates and applies scientific knowledge (or, more generally phrased, epistemic resources ) for solving real-world problems. This is illustrated by the fact that traditional philosophy of science has been much more interested in the unity of science while considering interdisciplinary research as a means for achieving unity but not as an issue relevant to philosophical study.

Subsequently, we will turn to the second part of our thesis, starting from the idea that the philosophy of science maintains philosophical views of science —which we call philosophical paradigms of science— that determine what subjects are worthy philosophical study and how these are studied. We argue that a physics paradigm of science has been dominant in traditional philosophy of science, which may have strengthened a physics-dominated view of science in science education and scientific research, and which currently impedes interdisciplinary research because it entails an ineffective epistemology that also conceals cognitive and epistemological difficulties. A more appropriate alternative epistemology comes from an engineering paradigm of science . An outline will be given of the extended Kuhnian matrix that is used as a conceptual framework to analyze philosophical views of science, and of the two philosophical paradigms that result from using this matrix as a framework to articulate philosophical views of science (Boon 2017a ). Central is the difference between science considered as a unified hierarchy, or network of theories (in a physics paradigm), versus ‘knowledge’ considered as epistemic tools constructed and shaped within scientific disciplines, where epistemic tools must be constructed such as to be suitable for being used as epistemic resources in performing epistemic tasks in which new epistemic results are generated in an ever ongoing scientific research processes (in an engineering paradigm). Next, it is argued that the way in which ‘knowledge’ is constructed is partly determined by the disciplinary perspective within which experts perform their research. Kuhn’s traditional notion of disciplinary matrices is proposed as a framework to analyze disciplinary perspectives of disciplines.

Accepting the idea put forward in the engineering paradigm that ‘epistemic results’ such as scientific models are shaped by the specific disciplinary perspective of discipline D A and therefore indelibly entail specificities of the discipline —instead of scientific models being more or less objective or ‘literal’ representations of aspects of the world, as is the epistemological ideal of a physics paradigm— explains why epistemic results produced in D A usually do not speak for themselves and cannot be understood or used in a straightforward manner by experts in D B . It is concluded that: (a) the epistemology that results from the engineering paradigm points to fundamental reasons for the epistemological and cognitive difficulties that we aim to explain, and (b) that this explanation is not easily recognizable nor appreciated in a physics paradigm of science.

Finally, in Section 5, expanding on the engineering paradigm of science in which epistemic recourses (such as theories, models, laws, and concepts) are interpreted as epistemic tools for performing epistemic tasks, we will propose to also interpret the metacognitive scaffolds (discussed in this article) as epistemic tools. Similar to how ‘knowledge’ interpreted as epistemic tool allows, guides and constrains, for instance, thinking about aspects of the physical world, metacognitive scaffolds interpreted as epistemic tools allow, guide and constrain thinking about (epistemological) aspects of scientific practice, such as how the disciplinary perspective of D A shapes the production of ‘knowledge’ in D A . This idea is rather programmatic but aims to make plausible that the epistemology emerging from the engineering paradigm of science not only explains epistemological and cognitive difficulties of interdisciplinary research but also indicates directions to mitigate these difficulties. This epistemology, therefore, is also very promising for developing metacognitive scaffolds that assist in learning and doing scientific research.

1.3 Terminology

Throughout this article, we will use the terms ‘knowledge,’ ‘epistemic resources’ and ‘epistemic results’ interchangeably. In the text, ‘knowledge’ is often put between quotes to indicate that it encompasses all possible types of scientific knowledge, such as fundamental principles; theoretical and empirical knowledge about phenomena; scientific models, laws, concepts, and ‘descriptions’ of (unobservable) phenomena; practical knowledge about (observable) phenomena (e.g., on how to generate or causally affect them by physical or technological circumstances, see Boon 2017c ); theoretical knowledge of the inner working of technological instruments and measurement apparatus (often represented in models of the instrument); and, knowledge of methodologies and epistemic strategies in scientific research.

In this article, we aim to add ‘metacognitive knowledge’ (i.e., the last item in the list above), which is knowledge not about the ‘real-world’ but about how to learn and how to do scientific research. This entails knowledge of epistemological views (e.g., as taught in philosophy of science courses) as well as knowledge of methodologies and epistemic strategies. This kind of knowledge is represented by means of so-called metacognitive scaffolds (also called matrices, or, frameworks) that can be utilized in learning and executing (interdisciplinary) research.

Finally, ‘epistemic resources’ is the ‘knowledge’ used in generating new ‘knowledge,’ while ‘epistemic results’ is the generated ‘knowledge.’ Epistemic results are also referred to as ‘epistemic entities.’ The first two terms emphasize that in this article, scientific research is considered as a never ending process of producing and using ‘knowledge,’ while ‘epistemic entity’ stresses that knowledge is used in a tool-like fashion . The engineering paradigm of science proposed in this article expresses a view of scientific research in which scientific researchers produce ‘knowledge’ that can be used in performing epistemic tasks for specific epistemic purposes (within or outside science), instead of science being the quest for complete and true knowledge.

2 Studies of interdisciplinary research

2.1 definitions of inter- and transdisciplinary research.

Interdisciplinary research is promoted, performed, administered, organized and taught, to the effect that these aspects are studied in scholarly domains ranging from science policy studies, governance studies, STS (science, technology and society) studies, science education, cognitive sciences, philosophy of science and social epistemology.

Already in the 1970s, Jantsch stressed the role of universities in achieving social goals and therefore called for a reform of universities and university education. Changes in society require that the university develops increasingly interdisciplinary approaches, which must also be reflected in university education (Jantsch 1972 ). Jantsch’s scholarly focus was the organizational structure of universities, and how by reforming this structure, interdisciplinarity for meeting societal goals can be achieved.

One of the aims of studies in interdisciplinarity is a correct definition of interdisciplinary research (Newell 2013 ). A consensus definition drawn up in the 1990s is: “A process of answering a question, solving a problem, or addressing a topic that is too broad or complex to be dealt with adequately by a single discipline or profession . . . [interdisciplinary research] draws on disciplinary perspectives and integrates their insights into a more comprehensive perspective ” (Newell 2013 , 24, our emphasis). Definitions can differ with respect to aspects such as problem-definition, level of integration, and the elements integrated, for example, not only laws and theories, but also concepts, theoretical frameworks, methodologies, procedures, instruments, and data (e.g., Apostel et al. 1972 ; Klein 1996 ; Lattuca 2001 ; Aboelela et al. 2007 ; Huutoniemi et al. 2010 ). Most authors agree that the complexity of a problem (either a problem within science or a ‘real world’ problem) is why interdisciplinary research is necessary (Newell 2013 ). Footnote 1

Several authors take transdisciplinarity as a higher form of interdisciplinarity in the sense of ‘being about more complex problems’ —such as problems for which technological solutions are developed that also need to take into account societal, economical, ethical and other humanities aspects— which usually requires to go beyond the confines of academic disciplines (e.g. Klein 2010 ; Hirsch-Hadorn et al. 2010 ; Schmidt 2008 , 2011 ; Alvargonzález 2011 ; Bergmann 2012 ; Aneas 2015 ). Aneas ( 2015 , 1719), for instance, states that: “Transdisciplinarity is a higher stage of disciplinary interaction. It involves a comprehensive framework that organizes knowledge in a new way and is based on cooperation among various sectors of society and multiple stakeholders to address complex issues around a new discourse.” Yet, in the scholarly literature, many examples that would comply with this definition are still called interdisciplinary, Footnote 2 especially in the engineering education literature (e.g., National Academy of Science 2005 ; Nikitina 2006 ; Gnaur et al. 2015 ; Van den Beemt et al. under review ) and in philosophical literature (e.g., Lattuca 2001 ; Frodeman and Mitcham 2007 ; Cullingan and Pena-Mora 2010 ; Tuana 2013 ; Lattuca et al. 2017 ). Footnote 3

In sum, many studies focus on organizational and institutional obstacles to interdisciplinary research, rather than the cognitive and epistemological obstacles (e.g., Turner 2000 ; Jacobs and Frickel 2009 ; Turner et al. 2015 ; Newell 2013 ). Yet, central to most definitions of interdisciplinary and transdisciplinary research —intended to give direction to their organization — is the integration of knowledge (or more broadly, epistemic resources ). Such integration is probably the hardest part for researchers and requires specific abilities or expertise .

2.2 Methods for organizing interdisciplinary research

Several authors have proposed methods for the (internal) organization of interdisciplinary research by specifying steps in the research process. Klein ( 1990 ), Repko ( 2008 ) and Repko and Szostak ( 2017 ), and Menken and Keestra ( 2016 ), for instance, wrote book-length treatments of the interdisciplinary research process.

There appears much overlap between the three methods, especially between Klein ( 1990 ) and Repko ( 2008 ) / Repko and Szostak ( 2017 ), but there is also a remarkable difference as to the aim of interdisciplinary research processes. While Klein, Repko and Szostak focus on developing understanding about complex problems outside science, Menken & Keestra’s approach is oriented at problems within science. Footnote 4 The method by Menken and Keestra ( 2016 ) is faithful to how ‘science-oriented’ scientific research practices are traditionally understood, by including the phases of ‘formulating research (sub-) questions and hypotheses ,’ ‘setting-up the research methods and design’ and ‘performing the data collection and analysis’ within the interdisciplinary setting. In fact, their schema is an expansion of the well-known hypothetical-deductive method. Footnote 5 In explaining interdisciplinary research, their three additions as compared to the HD-method are: the decisions that need to be taken on relevant disciplines; the establishment or choice of a comprehensive theoretical framework within which the participating disciplines need to be embedded; and, the integration of results and insight. Menken and Keestra’s ( 2016 ) schema is traditional in the sense that it focuses on testing hypothesis, with interdisciplinary theoretical insights ‘within science’ as the result; whereas the schemas presented by Klein ( 1990 ), and by Repko ( 2008 ) and Repko and Szostak ( 2017 ) focuses on outcomes that are relevant to solutions for the ‘real-world’ problems at which the research project aims.

2.3 Metaphors of integration: Jigsaw-puzzle, conflict-resolution, and engineering-design

The methods to coordinate research processes discussed above adequately reflect the proper organization of processes of interdisciplinary research as commonly adopted in current research projects. Footnote 6 However, our worry remains how linking and integration of ‘knowledge’ (i.e., epistemic resources ) is understood in these methods.

Regarding the linking or integration of knowledge three kinds of metaphors can be distinguished: (a) the jigsaw-puzzle metaphor according to which integration means that pieces of ‘knowledge’ are fitted together without changing them; (b) the conflict-resolution metaphor , which focuses on apparent disagreements supposedly due to hidden presuppositions and confusion about basic concepts, as in, say, political discourse, and communication to resolve these disagreements; and (c) the engineering-design metaphor, which focuses on the construction of epistemic resources for specific epistemic tasks, usually requiring creative designer-like inventions to combine relevant but heterogeneous bits and pieces into a coherent ‘epistemic entity’ (e.g., a scientific model) within specific epistemic and pragmatic requirements related to the epistemic uses of the epistemic entity. Our idea is that studies of interdisciplinarity may be implicitly guided by epistemological views —loosely expressed by the jigsaw puzzle and the conflict-resolution metaphors—, which hinder an understanding of the epistemic and cognitive difficulties of interdisciplinary research oriented at solving real-world problems, for which the engineering-design metaphor aims to be an alternative.

Menken and Keestra’s ( 2016 ) method complies with a jigsaw-puzzle metaphor of integration, whereas the methods proposed by Klein ( 1990 ), Repko ( 2008 ) and Repko and Szostak ( 2017 ) are closer to a conflict-resolution metaphor, which interprets difficulties of integration as conflicts due to misunderstandings that can be resolved by communication and reflection in order to establish ‘common ground.’

Also philosophers who aim to facilitate difficulties in interdisciplinary research lean towards the conflict-resolution metaphor (e.g., Nikitina 2006 ; Strang 2009 ; Fortuin and van Koppen 2016 ; O'Rourke et al. 2016 ). Undeniably, these philosophers have achieved positive results by means of conceptual frameworks and tools to generate philosophical dialogue by which cross-disciplinary communication is improved, for instance to clarify concepts and background beliefs – and in this manner philosophers can make contributions to students’ and researchers’ ability to reflect on presuppositions.

In short, the methods for interdisciplinary research proposed in interdisciplinary studies do not sufficiently address the inherent epistemic and cognitive difficulties of integration. The philosophical approaches that aim to help researchers solve conceptual confusion are on the right track, but they do not yet touch the deeper epistemological issues. The detailed cases of interdisciplinary research in engineering and bioengineering practices investigated by Mattila ( 2005 ), Nersessian ( 2009 ), Nersessian and Patton ( 2009 ), and MacLeod and Nersessian ( 2013 ) can be taken as examples that suit the engineering-design metaphor – these are illustrative examples of the complexity of interdisciplinary research, but they do not yet offer any tools for learning how to do such research.

3 Studies of teaching and learning interdisciplinary research

3.1 higher-order cognitive skills for expertise in interdisciplinary research.

Definitions of interdisciplinary research discussed in Section 2 focus on the knowledge part, i.e., the integration of knowledge and other epistemic elements, but interdisciplinary research has also been defined in terms of types of expertise of researchers (e.g., Goddiksen 2014 ; Goddiksen and Andersen 2014 ) and types of interdisciplinary collaborations (e.g., Rossini and Porter 1979 ; Andersen and Wagenknecht 2013 ; Andersen 2016 ). This section will focus on the skills needed as a crucial part of the expertise to conduct interdisciplinary research (Goddiksen and Andersen 2014 ; Collins and Evans 2002 , 2007 ; Goddiksen 2014 ).

Scholarly work that aims to articulate the specific skills needed for performing interdisciplinary research and how these skills can be trained and assessed, is mostly found in educational literature. Above, we claimed that, from an epistemological perspective linking and integrating is perhaps one of the severest challenges of utilizing highly-fragmented scientific disciplines in solving highly-complex ‘real-world’ problems. In such interdisciplinary research, having expertise means that scientific researchers are able to use, link and integrate ‘knowledge’ (i.e., epistemic resources including data, concepts, models, and theories) and methods from disciplines D A , D B etc. such as to generate ‘knowledge’ for solving the ‘real-world’ problem.

One may expect that science and engineering education at an academic level have clear ideas on how linking and integration in interdisciplinary research is done, and how to teach it. However, although many studies on engineering education aim at teaching interdisciplinary problem-solving, a thorough analysis of the epistemological difficulties seems to be lacking. In part, in engineering education literature this lack is due to the specific interpretation of interdisciplinary problem-solving, less oriented at integration of knowledge , but rather on taking into account societal values and constraints – which, as suggested above, should rather be called transdisciplinary.

Dealing with broader societal issues usually imply skills that are considered ‘soft’, social, (inter)personal and professional skills, such as integrity, communication, courtesy, responsibility, positive attitude, professionalism, flexibility, teamwork, and work ethic (e.g., Robles 2012 ; National Science Foundation 2008 ; Haynes and Brown-Leonard 2010 ; Gnaur et al. 2015 ; Bosque-Perez et al. 2016 ; Chan et al. 2017 ; Lattuca et al. 2017 ), for which authors typically promote problem-based or case-based pedagogies and co-curricular activities. If cognitive or epistemological challenges of interdisciplinary research projects are mentioned at all, authors usually assume that this is solved by being trained in teamwork and communication skills (see Stentoft 2017 for a comprehensive review).

However, based on a critical meta-study (i.e., a systematic search within scientific literature databases) reviewing how ‘interdisciplinary thinking’ is taught in higher education more generally, Spelt et al. ( 2009 ) conclude that research on this topic is still limited. Interdisciplinary thinking according to them is “the capacity to integrate knowledge of two or more disciplines to produce a cognitive advancement in ways that would have been impossible or unlikely through single disciplinary means,” which they consider a complex cognitive skill . Other authors add that interdisciplinary thinking is a higher-order or metacognitive skill, which involves the ability to search, identify, understand, critically appraise, connect, and integrate theories and methods of different disciplines and to apply the resulting cognitive advancement together with continuous evaluation (e.g., Ivanitskaya et al. 2002 ; Lourdel et al. 2007 ; DeZure 2010 ; Zohar and Barzilai 2013 ; Goddiksen and Andersen 2014 ). Additionally, interdisciplinary thinking requires specific types of knowledge – i.e., next to having knowledge of one’s discipline, also knowledge of disciplinary paradigms and of interdisciplinarity is needed (Spelt et al. 2009 ).

Similarly, based on a meta-study as well, Khosa and Volet ( 2013 ) conclude that the intended higher-order cognitive skills needed in interdisciplinary research are not actually achieved through student-led collaborative and case-based learning activities at university level, and argue that students may need instruction to the development of these skills. In most education reported in the literature, teaching higher-order metacognitive skills (also referred to as ‘deep-learning’) is approached by inviting self-reflection of students. However, usually no clear guidance for how to do this is given. In a recent systematic review of engineering education literature on teaching interdisciplinarity, Van den Beemt et al. ( under review ) found that problem- and project-oriented (PBL) forms of education to promote interdisciplinarity, do indeed promote societal and professional skills and attitudes , including teamwork, project-management and communication, but these authors conclude that there are no indications that problem-based learning (PBL) approaches are successful with regard to the development of higher-order metacognitive skills needed for linking and integrating epistemic resources in real-world problem-solving. Educational programs in engineering often assume that these skills will be learned ‘by doing’ and do not need additional support. Similar to Khosa and Volet ( 2013 ), this assumption is contested by Zohar and Barzilai ( 2013 ), who argue that learning higher-order metacognitive skills needs to be supported by metacognitive scaffolds , which, according to these authors, are generally underdeveloped – i.e., hardly any such scaffolds have been developed. In Section 5, we will make some suggestions about possible scaffolds (or frameworks) to support the development of higher-order skills of students in conducting interdisciplinary research.

Summing up, in order to actually use and generate ‘knowledge’ for solving complex real-world problems, researchers need higher-order cognitive skills, because rules or algorithms for how to use a theory, model, concept or data in this respect usually are not given by the epistemic resources. Footnote 7 Based on studies in the educational literature it is concluded that the teaching of higher-order cognitive skills as a crucial part of expertise in conducting interdisciplinary research remains underdeveloped in higher education, which may be an important reason for difficulties that researcher have in dealing with interdisciplinary research (Thorén and Persson 2013 ; Thorén 2015 ; MacLeod 2016 ). It now needs to be explained: (a) in what sense the lack of teaching these skills has to do with traditional epistemological views, and (b) how alternative epistemological views provide understanding of these specific higher-order cognitive skills.

3.2 Epistemological views conveyed in science teaching

Before turning to the idea that traditional epistemological views may hamper educational ideas on how to teach higher-order cognitive skills as part of developing expertise in interdisciplinary research, the term ‘metacognitive skill’ (and the term ‘higher-order cognitive skill,’ which in this context is used interchangeably) requires some clarification. Flavell ( 1979 ) is a developmental psychologist who is said to have introduced the notion metacognitive knowledge , defined as one’s stored knowledge or beliefs about oneself and others as cognitive agents, about tasks, about actions or strategies, and about how all these interact to affect the outcomes. Metacognitive knowledge consists primarily of knowledge or beliefs about what factors or variables act and interact in what ways to affect the course and outcome of cognitive enterprises. There are three major categories of these factors or variables—person, task, and strategy (Flavell 1979 , 907; also see Pintrich 2002 ). Hence, the original focus in the cognitive sciences was on knowledge concerning one’s own cognitive processes, called metacognitive knowledge . Additionally, it involved the pedagogical view that knowledge and awareness of the working of one’s own cognitive system —acquired by reflection on one’s own learning processes — would improve student’s learning abilities and eventually, provide them with metacognitive skills .

Thus, initially the notions of metacognitive knowledge and skills were focused on students’ learning abilities , disconnected from the ability to understand and use (scientific) knowledge in scientific research and problem-solving tasks. But in the work of later authors, the notion of metacognitive skills becomes entangled with accounts of what it means to (deeply) understand scientific knowledge as part of having acquired expertise in research (esp. in the philosophical work by Goddiksen and Andersen 2014 , and Goddiksen 2014 ). This turn is crucial to our own argument, which aims at an epistemological view that recognizes the contribution of the human cognitive system and specificities of scientific fields (MacLeod 2016 ) to the character and form of (scientific) knowledge (Boon 2017b ). In particular, the metacognitive knowledge must include knowledge of the epistemological nature of (scientific) knowledge, and accordingly, the metacognitive skills of an expert are the higher-order cognitive skills to use this metacognitive knowledge to understand, organize and execute (interdisciplinary) research processes. In Section 4, it will be argued that metacognitive scaffolds represent metacognitive knowledge – these scaffolds (also referred to as matrices and frameworks ) can be utilized to learn and execute (interdisciplinary) scientific research, i.e., to develop metacognitive skills in doing research.

Our interrelated epistemological and educational view is on par with that of those authors in the educational sciences who indeed argue that the dominant positivist view of science hampers the development of metacognitive or higher-order cognitive skills, and who therefore promote the learning of these skills in a direct relationship with alternative epistemological views – often called constructivist views (e.g., Edmondson and Novak 1993 ; Yerrick et al. 1998 ; Procee 2006 ; Tsai 2007 ; Mansilla 2010 ; DeZure 2010 ; Zohar and Barzilai 2013 ; Abd-El-Khalick 2013 ; Sin 2014 ; and see footnote 9).

The core of our philosophical approach aiming to clarify teaching and learning higher-order cognitive skills is to focus on how researchers (and more generally, cognitive and epistemic agents) construct knowledge – which involves an epistemological view that we aim to express by the suggested engineering-design metaphor of knowledge-generation in interdisciplinary research, in contrast with the jig-saw puzzle and the conflict-resolution metaphors. The proposed epistemological view entails that ‘knowledge’ must not be understood as a literal representation of aspects of the world (as is implicit in the jig-saw puzzle metaphor) independent of how researchers trained within a scientific discipline typically construct knowledge, Footnote 8 but rather as shaped by researchers in ways learned within their specific scientific discipline (i.e., as expressed by the alternative engineering-design metaphor). Typical ways of constructing ‘knowledge’ within a discipline is conceptually grasped by the notion ‘disciplinary perspective.’

The proposed epistemological view, therefore, provides an alternative to the widely criticized positivist views of science (esp. see the authors listed above) still conveyed in science education, Footnote 9 and forms the basis for alternative views on teaching and learning higher-order cognitive skills, in particular, regarding the role of metacognitive scaffolds (frameworks) to support developing and executing these skills (also see section Terminology ). The core of our philosophically supported educational view is that to develop expertise in conducting interdisciplinary research, students need to: (1) learn and understand that knowledge is constructed , rather than being a literal representation independent of the disciplinary context and independent of typical (disciplinary) ways in which researchers construct knowledge; (2) learn that, (at least partially) knowing how specific knowledge is generated or constructed, is often crucial to understanding how to use this knowledge in problem-solving tasks; (3) learn to recognize, or actually reconstruct in a systematic fashion, how specific knowledge has been constructed; (4) understand that scientific disciplines have developed different ways to generate knowledge, which is grasped by notions such as disciplinary paradigms , matrices and perspectives (Spelt et al. 2009 ); (5) learn to understand the coherence of epistemic norms and activities in scientific practices (Chang 2012 , 2014 ); and, (6) learn to recognize, or actually reconstruct in a systematic fashion, the specificities of a scientific discipline (Andersen 2013 , 2016 ).

4 Philosophical view of science

4.1 the unity of science versus disciplinary perspectives.

A dominant view of science in the traditional philosophy of science is reflected in the lack of philosophical interest in interdisciplinarity as a subject for philosophical study. Conversely, characteristic of this dominant view is the general interest in the unity of science understood as coherence between the sciences (esp., between scientific theories, laws and concepts in distinct fields or disciplines), where interdisciplinary research is merely seen as a means to achieve this unity (Oppenheim and Putnam 1958 ; Nagel 1961 ; Maull 1977 ; Darden and Maull 1977 ; Grantham 2004 ; Schmidt 2008 , 2011 ; Thorén and Persson 2013 ; Cat 2014 ). The jigsaw-puzzle metaphor of how interdisciplinarity is achieved, therefore, can also be taken as a metaphor of how the unity of science is viewed.

Another characteristic of dominant traditional philosophical views of science is the lack of philosophical interest in epistemological issues of generating epistemic resources for solving problems in the real world, and the role of interdisciplinary research therein. Footnote 10 This neglect is reflected in much of academic science education, which traditionally pays little attention to the use of science in constructing epistemic sources (such as models and concepts) for specific epistemic tasks. Even today it is difficult to encourage bachelor students in the engineering sciences to construct scientific models for real-world target systems. They are trained in constructing mathematical models (especially for the exercises in textbooks), but hardly able to (re)construct scientific models in the sense of searching and putting together epistemic resources into a coherent (preliminary) scientific model (Boon and Knuuttila 2009 ; Knuuttila and Boon 2011 ; Boon 2019 ; Newstetter 2005 ). On this part, we suspect that students often have a confused understanding of (non-mathematical) scientific models because of a naive representational understanding of models in which models must be similar to aspects of the world. Such an understanding makes it very hard to construct, use and adapt scientific models for real-world problem-solving tasks. Philosophical views of science assuming that the ultimate aim of science is justified theories may have been one of the causes of this confusion. Social-constructivist views about science, such as the current consensus view on the ‘nature of science’ (NOS), which is supposed to be generally taught as a correct understanding of science, does not help in this respect (see footnote 9). It is, therefore, an important task for the current philosophy of science to come up with adequate alternative views.

Finally, with regard to the characteristics of dominant views of science that may have affected the view of science still widely ingrained in higher education, it is striking that Kuhn’s profound understanding of the contribution of disciplinary perspectives —which inherently and indelibly shape the scientific results of a discipline or field— has been interpreted as a problem and a threat that must be countered to restore the objectivity, rationality and unity of science. Instead, we claim, this understanding should be firmly embraced to learn more about the consequences for the epistemology of science. More specifically, Kuhn’s insights could have led to the recognition of serious cognitive and epistemological challenges of interdisciplinary research, which deserve philosophical study (Andersen 2013 , 2016 ). However, the jigsaw-puzzle metaphor of interdisciplinarity, and with it a naïve idea of the ‘unity of science,’ remained dominant, Footnote 11 and is obviously incommensurable with notions such as ‘disciplinary perspectives.’ Conversely, we defend that notions such as ‘disciplinary perspectives’ are productive to better understand the character of science and actual scientific research in scientific practices.

It appears, therefore, that philosophical views of science held within the philosophy of science determine which issues are recognized as of philosophical interest. However, as these views also affect views of science in society at large, especially in higher education and scientific research, it is important to critically examine ‘philosophical paradigms of science,’ that is, philosophical paradigms of science ingrained in the philosophy of science and beyond.

4.2 Two philosophical paradigms of science: A physics versus an engineering paradigm of science

At stake are two different philosophical views on science, one focusing on scientific theories for the sake of science, Footnote 12 the other focusing on scientific knowledge (in the sense of epistemic resources and results, see Terminology ) and epistemic strategies for solving real-world problems. Boon ( 2017a ) has argued that these two views can be analyzed in terms of two distinct philosophical paradigms of science .

The core of a Kuhnian notion of paradigms in science —as we interpret it in regard of the epistemological issues raised in this article— is that a scientific practice (or discipline) is embedded in a paradigm that enables and guides it, rather than being guided by strict methodological rules alone. Footnote 13 Conversely, the paradigm is ingrained in the sense that the practice (or discipline) maintains and reinforces it. The paradigm frames what counts as relevant scientific problems and adequate solutions, as well as how these problems are phrased, and also how the discipline deals with it. Although a paradigm cannot be proven or disproven in a straightforward manner, it can be articulated, analysed and disputed, for which the disciplinary matrix introduced by Kuhn ( 1970 ) is suggested as an analytic framework. Footnote 14 In this ‘disciplinary matrix’ explication, a paradigm consists of a loose, non-rigid set of interlocking elements that mutually support and reinforce each other.

The same can be said of philosophical paradigms of science, being views of science that guide and enable philosophical studies of science – i.e., the philosophical paradigm frames what counts as relevant philosophical problems and adequate solutions, how these problems are phrased, and how the philosophy of science deals with it (Boon 2017a ). Accordingly, similar to, and based on Kuhn’s disciplinary matrix by which philosophers can analyse the philosophical fabric of scientific disciplines, a matrix (or framework) has been developed for analysing views of science in the philosophy of science . Expanding on Kuhn’s disciplinary matrix that consists of four elements, thirteen elements that constitute a matrix for analyzing philosophical views of science have been proposed (listed in the left column of Table 1 ). Subsequently, the resulting matrix has been used to articulate two contrasting philosophical views of science, called a physics paradigm and an engineering paradigm of science, of which a sketchy summary is presented in the right column of Table 1 (for a more elaborate version, reference is made to Boon 2017a ).

Crucially, the elements of the matrix constituting and representing a philosophical view of science are intertwined and reinforce each other, to the effect that alternative (philosophical) views on specific aspects of science do not easily get a foothold in the philosophy of science. This is why it is considered a paradigm – it is a comprehensive as well as normative and more or less implicit background view within which philosophical ideas about science are articulated, understood and evaluated.

Although the suggested physics paradigm of science might be dominant as a view of science (implicitly) adopted by philosophers of science but also by scientific researchers (which is especially obvious in roles where they have to articulate their views of science, such as when teaching or when being interviewed by philosophers), reasons can be given that an engineering paradigm of science is more adequate for characterizing actual scientific research practices. In particular, scientific practices aiming at knowledge that is relevant, reliable, and useful for solving real-world problems, are better understood within an engineering paradigm of science.

Contrariwise, most of the traditional philosophy of science considers these kinds of research practices as ‘applied sciences,’ in the sense that these ‘problem-solving’ practices ‘simply’ apply scientific knowledge generated in fundamental or basic sciences (Boon 2006 , 2011 ). This belief is intertwined with, and strongly supported by the suggested physics paradigm of science in regard of elements listed in Table 1 such as the aim of science, ontology, metaphysical presuppositions, methodology, (epistemic) results of scientific research, and the role of technological instruments, which are interpreted differently in an engineering paradigm of science.

4.3 The physics paradigm as a cause of epistemological difficulties of interdisciplinary research?

Does the physics paradigm of science cause philosophical misunderstanding of epistemological difficulties of interdisciplinary research? And if so, is the engineering paradigm doing better?

A reason for this hypothesis is that on the physics paradigm, science aims at theories and (reductive) unity of science, which implies that the aim and results of interdisciplinary research must be phrased in terms of integration of theories, as can be observed indeed in most authors who study interdisciplinarity discussed in this article. Above, this flawed understanding of interdisciplinary integration has been criticized as the jigsaw-puzzle metaphor of interdisciplinarity. Some authors have already criticized the idea that unity of science must be achieved by means of reductive relationships between theories. Footnote 15 We take Maull ( 1977 ) and Darden and Maull ( 1977 ) as in fact pointing at the possibility to draw relationships between disciplines by the exchange of theories and concepts without integration. Footnote 16 Similarly, Thorén and Persson ( 2013 ) have introduced the notion of ‘problem-feeding,’ which is also a way in which collaboration between disciplines can take place without integration proper. Our point is that interdisciplinary research as a means to achieve unity of science through integration of the theoretical content of disciplines fits with a physics paradigm, whereas interdisciplinary research in the sense of collaborations between scientific disciplines aimed at ‘epistemic tools,’ methodologies, and (technological) instruments that can eventually serve as epistemic tools for solving problems outside the disciplines, does not fit well into a physics paradigm, but rather into the engineering paradigm.

A more fundamental issue is to see how disciplinary perspectives cause epistemological and cognitive difficulties in interdisciplinary research. Several authors stress the importance of disciplinary perspectives in interdisciplinary collaborations, either in terms of the rigidity to conceptual or theoretical change (e.g., Thorén and Persson 2013 ), or by indicating the importance of recognizing that different perspectives are possible on the same problem. However, eventually most authors assume that this can be dealt with by communication and finding ‘ common ground ’ among disciplinary perspectives on a problem (e.g., DeZure 2010 ; Ivanitskaya et al. 2002 ; Aram 2004 ; Repko et al. 2007 ; Spelt et al. 2009 ; Haynes and Brown-Leonard 2010 ; Liu et al. 2011 ; Lattuca 2001 , 2002 ; Lattuca et al. 2017 ).

Our concern is that the pursuit of ‘common ground’ by unassisted communication in interdisciplinary research is very difficult or remains at a superficial level – a concern that is also supported by empirical research in the educational sciences discussed above. Conversely, interdisciplinary collaboration may become more effective by better understanding how disciplinary perspectives work in disciplinary scientific research.

First, the contribution of disciplinary perspectives cannot easily be appreciated within a physics paradigm of science, in which it is assumed that science aims at theories that objectively represent aspects of the world, that is, as a two-placed relationship between knowledge and world – an assumption that smoothly agrees with the jigsaw-puzzle metaphor of interdisciplinary research. In our explanation of the role of disciplinary perspectives in generating knowledge within a discipline, we take scientific models as an example. A widely discussed issue in the philosophy of science is how scientific models represent their target-system. A favoured account, also in science education and communication, is that it consists of a similarity relationship (e.g., Giere 1999 , 2004 ), which however appears problematic. In order to avoid the problematic aspects of similarity, both Giere ( 2010 ) and Suárez ( 2003 , 2010 ) develop an account which attributes a key-role to the competent and informed agent . However, their accounts are still not very informative as to the epistemic functioning of models (Knuuttila and Boon 2011 ). Footnote 17 Also ‘context-dependence’ is often mentioned to indicate that an objective (two-place) representational relation is problematic, but with a few exceptions, Footnote 18 hardly any of these studies explains how the ‘context’ or the disciplinary perspective of the epistemic agent contributes to the epistemic character of the representation. We aim to open this ‘black-box,’ as we believe that this is a way in which philosophers can contribute to difficulties of interdisciplinary research.

Boon and Knuuttila ( 2009 ) and Knuuttila and Boon ( 2011 ) have turned focus to how models are constructed , and argue that several heterogeneous elements are built-in the model, which partly but indelibly determines ‘what the model looks like.’ Constructing a scientific model typically occurs within a specific discipline and focuses on a problem within or outside the disciplines. Researchers usually pick a specific aspect of the problem (the phenomenon of interest) for which the model is build. This choice is guided by the disciplinary perspective. Next, the way in which the model is constructed is guided, enabled and constrained by what the discipline has to offer, which concerns aspects such as: the experimental set-up by which the phenomenon can be studied; the instruments which determine what can actually be measured; the available theoretical and empirical knowledge about the phenomenon; and, the kind of simplifications usually made in the discipline (e.g., due to recommended ‘methodological reductions’), both in the experimental investigation and in the construction of the model. Footnote 19 This brief sketch illustrates that scientific models constructed in this manner cannot be understood as a straightforward representations in the sense of a two-placed relationship between model and target-system, nor can they be understood as mathematically derived from abstract theories (although parts of the model may be derived in that way). Boon/Knuuttila ( 2009 , 2011) have argued that scientific models usually are representations in the sense of being epistemic tools that allow for thinking and reasoning about the target system, rather than being representations in the sense of firstly being similar to the target system. Whereas the actual epistemic uses of scientific models are unintelligible when assuming that the model is similar to its target, epistemic uses of scientific models by scientific researchers are better understood when taking into account how the mentioned aspects have shaped the model (see footnote 19). As has been argued in Boon ( 2017a ), the interpretation of scientific models as representation complies with the physics paradigm of science, while the notion of scientific models as epistemic tool is virtually unintelligible within the physic paradigm. Conversely, the notion of knowledge as epistemic tool is a core feature of the engineering paradigm of science.

Regarding the question “Does the physics paradigm of science cause philosophical misunderstanding of epistemological difficulties of interdisciplinary research, and if so, is the engineering paradigm doing better?” it can now be answered that, firstly, the physics paradigm considers scientific knowledge such as models as objective representations independent of how these representations are shaped by the specific scientific discipline. This makes it very hard for experts trained in D B to understand epistemic resources produced by experts trained in D A . Conversely, the engineering paradigm takes into account that aspects of a scientific practice fundamentally shape scientific knowledge. For instance, the suggested method for (re)constructing scientific models (Boon 2019 ; see footnote 19) enables to analyze the aspects that a discipline typically builds-in the model. Within a physics paradigm of science, this contribution of aspects specific to the discipline in shaping epistemic results leads to concerns about the objectivity of knowledge. Yet, this is much lesser of a concern within the engineering paradigm, because an important criterion for accepting epistemic results is rather that the knowledge must be constructed such that it can properly function as an epistemic tool in performing epistemic tasks, for instance with respect to solving real-world problems.

5 Metacognitive scaffolds

5.1 disciplinary matrices and disciplinary perspectives as metacognitive scaffolds.

On the proposed Kuhnian approach, a disciplinary perspective of experts in a specific discipline D A can be made explicit by means of the elements that constitute the disciplinary matrix , that is, in terms of a more or less coherent set of knowledge, beliefs, values and methods that has become ‘second nature’ in the sense that experts are hardly aware of how the specificities of their disciplinary contribute to the ways in which they do their research and generate epistemic results . Being trained in discipline D A has instilled in the researcher a disciplinary perspective specific to D A , which basically enables but also constrains how she does her research. When facing, say, a ‘real-world’ problem, the disciplinary perspective makes her observe phenomena P A typically dealt with in D A . Hence, researchers working in D A observe some aspects P A of the problem, but maybe not aspects P B that would be typically observed by experts trained in discipline D B . Next, the disciplinary perspective makes researchers phrase research questions typical of D A , and construct explanations , models and hypotheses about P A by means of epistemic resources and epistemic strategies typical of D A . Also, researchers investigate P A by means of measurement procedures typically used in D A , and design experimental set-ups and technological instruments in ways typical of D A as well. Finally, epistemic results about P A are tested by procedures also typical of D A .

This brief sketch of what a disciplinary perspective of a discipline D A consists of, results in a set of specific elements that constitute a disciplinary matrix, such as: phenomena; research questions; epistemic resources, e.g., fundamental principles, theoretical and empirical knowledge; epistemic strategies; methods and methodologies, e.g., statistical analysis; experimental and technological setups; and, measurement instruments. To this rather preliminary and intentionally ‘not rigid’ pragmatic list of elements, some elements pointing at deeper philosophical issues listed in Table 1 can be added, for instance when aiming to find out about differences caused by philosophical presuppositions such as may be at stake in collaborations between, for instance, the humanities, social sciences, natural sciences, and the engineering sciences. In short, we suggest that the disciplinary perspective of a specific discipline D A can be analyzed in terms of such a set of cohering elements, called a disciplinary matrix .

The disciplinary matrix can be considered as a metacognitive scaffold , or framework that enables researchers to characterize their own disciplinary perspective in terms of a limited set of concrete aspects typical of their discipline. These aspects can, for example, be used to clarify approaches of the discipline. In interdisciplinary collaborations, this approach can be used to communicate with experts from other disciplines, for instance, in order to find similarities and differences in presuppositions and approaches of D A as compared to D B . In short, the disciplinary matrix to articulate and investigate disciplinary perspectives functions as a metacognitive scaffold that facilitates interdisciplinary communication on the characteristics of each discipline involved in an interdisciplinary research project. It is a scaffold that helps to open up disciplinary silo’s. Footnote 20

Using the term disciplinary perspectives of D A —but even more so, the term metacognitive scaffolds — may suggest a rather ‘immaterial,’ cognitivist take on the contribution of the specificities of a discipline D A in shaping the results and the form of the results. Yet, crucially, also technological instruments used in D A are an inherent part of the disciplinary perspective, not as ‘windows on the world’, but, for example, in already shaping and even generating phenomena that would not exist without these instruments (Giere 2006 ; Van Fraassen 2008 ; Boon 2012 , 2017c ). By referring to disciplinary perspectives, we wish to stress the contribution of the disciplinary perspective to the specificities of the research outcomes. It is to stress that these aspects of the disciplinary perspective (indicated by the elements of the disciplinary matrix) partially determine what the ‘knowledge’ produced by a specific discipline D A ‘looks-like’ – to stress that this knowledge is not a representation of the studied phenomenon, independent of specificities of the scientific discipline that produced this knowledge.

Acknowledging the contribution of the specificities of a discipline to the epistemic (and technological) results of scientific research stresses why metacognitive scaffolds (and the skills to use these scaffolds) are crucial to the researcher in interdisciplinary settings: For a researchers unfamiliar with D A , the epistemic resources produced by discipline D A do not speak for themselves, as ‘knowledge’ is not a straightforward representation of what the world is like. Instead, to understand ‘knowledge’ of unfamiliar disciplines requires the ability to also recognize it as resulting from specific ways of thinking, experimenting, measuring and modeling within discipline D A . The method of using the disciplinary matrix and disciplinary perspectives as metacognitive scaffolds helps in understanding an unfamiliar discipline D B in terms of the elements that guide and confine the way in which researchers in D B approach their subject and construct ‘knowledge.’ It explains the ‘how’ of research in D B by means of which ‘knowledge’ (the ‘what’) used and produced in D B is more easily understood.

5.2 Frameworks: (disciplinary) matrices and metacognitive scaffolds as epistemic tools

A core idea of the engineering paradigm of science is to interpret epistemic entities such as axiomatic systems, principles, theories, laws, descriptions of (‘unobservable’) phenomena, scientific models and concepts as epistemic tools that can serve in epistemic activities aimed at specific (epistemic) purposes. It is in view of their epistemic functioning that constructed epistemic tools must meet specific epistemic and practical criteria.

As already appears above, additional to interpreting ‘knowledge’ as epistemic tool, we propose to also interpret frameworks such as ( disciplinary) matrices and metacognitive scaffolds as epistemic tools that are constructed and designed (e.g., by philosophers but also researchers) to support students in their learning (to understand science) as well as researchers in performing epistemic tasks (see footnotes 19 and 20 for additional examples of metacognitive scaffolds). This suggestion complies with the engineering paradigm but not very well with the physics paradigm. In the latter, frameworks such as (disciplinary) matrices and metacognitive scaffolds are assessed as to how well (truth-full) they represent their target, whereas the engineering paradigm stresses that they must be assessed as to how well they serve a specific epistemic function (e.g., of the ‘system of practice,’ Chang 2012 ), that is, how well they serve as (epistemic) tools in performing epistemic tasks. As a consequence, in an engineering paradigm of science, disciplinary matrices, disciplinary perspectives and even philosophical paradigms are assessed for how well they function in a specific (scientific, practical, problem-solving) context.

5.3 Interdisciplinary research

In this article, we have aimed to make plausible that epistemological difficulties of interdisciplinary research have to do with dominant philosophical beliefs about science. The core of our argument is that scientific knowledge is usually presented as if it results from a representational relationship between knowledge and world, ignoring the role of disciplinary perspectives. Such an approach may be relatively unproblematic as long as we stay within the confines of a discipline and expect that every newcomer ultimately adapts to the specificities of the discipline. Surely, most researchers have at least some understanding of what it means to have a disciplinary perspective, but working within the confines of their well-established scientific discipline they hardly need to take into account that scientific results are shaped by the specificities of their discipline. Yet, this situation causes wicked problems as soon as interdisciplinary cooperation is requested.

The suggested solutions is to adopt an epistemological view in which scientific knowledge (such as models) is understood as also shaped by the specificities of the discipline. More effectively dealing with the specificities of scientific disciplines in interdisciplinary collaborations may be require meta-cognitive scaffolds (and the ability to use them) that enable analyzing how exactly a discipline generates and applies knowledge. Three examples of these scaffolds have been briefly sketched: the disciplinary perspective of a specific discipline can be analyzed and articulated by means of a (disciplinary) matrix; the way in which models are constructed can be analyzed and articulated by the so-called B&K method (Boon 2019 ); and the way in which scientific concepts are embedded in a wider context can be analyzed by means of concept-mapping (see footnote 20).

Therefore, not integration of theories and disciplinary perspectives is the first task for interdisciplinary collaboration, but clarification of the specificities of the disciplines and of the way in which in a discipline ‘knowledge’ comes about.

Conversely, Kline ( 1995 ) stresses that the complexity of a problem is often only recognized when it is studied from multiple perspectives.

Some authors, such as Schmidt ( 2008 , 2011 ), use interdisciplinarity and transdisciplinarity interchangeably.

Below, we will suggest that the aims of teaching interdisciplinarity expressed in engineering and sustainability education correspond better to the definition of ‘transdisciplinarity.’

Schmidt ( 2008 , 2011 ) has proposed a typology to distinguish between the different types of problems that are addressed in interdisciplinary (and transdisciplinary) research. He calls this object-oriented, theory-oriented and method-oriented interdisciplinarity, versus problem-solving oriented interdisciplinarity. His paradigmatic example of the latter is sustainability science, whereas ‘instrumental’ interdisciplinary approaches in the engineering sciences (which are the focus of our article) fall into the category ‘method-oriented’ interdisciplinarity. Attempts to address the problem of unity and the interconnections within the sciences are covered by object-oriented interdisciplinarity (if someone leans to a realist position on science with respect to ontology) or theory-oriented interdisciplinarity (if one tends towards an anti-realist position focused on epistemology). It is not our intention to discuss this at length, but for our purpose, on the one hand, Schmidt’s methodology-oriented interdisciplinarity is too limited to characterize the engineering sciences, while on the other hand his problem-oriented interdisciplinarity is too much oriented at societal issues, and thus runs the risk of neglecting the specific cognitive and epistemological difficulties of interdisciplinary research aimed at complex ‘real-world’ problems.

In educational settings the hypothetical-deductive method is often referred to as the empirical cycle .

For instance, in national and international research programs, such the European Horizon2020 program on grand societal challenges: https://ec.europa.eu/programmes/horizon2020/en/h2020-section/societal-challenges

The problem that rules for the uses of theories in real-world problems are not given by the theory was already stressed by Cartwright ( 1983 ).

Nor must the generation of knowledge in interdisciplinary research be understood as the mere product of social deliberation, more or less independent of ‘what the world is like,’ as suggested in strong social-constructivism . It should be noted that by using the conflict-resolution metaphor, we do not intend to attribute a strong social-constructivist position to scholar such as Klein, Repko and Szostak, as these authors maintain a much more moderate position. Furthermore, our focus is on the natural and engineering science, whereas many authors studying interdisciplinarity also aim to cover the humanities and social sciences, where the conflict-metaphor is probably more appropriate.

The alternative epistemological view proposed here, also aims to improve on constructivist views explicitly promoted in the so-called nature of science literature. Although the widely adopted view on the nature of science in science education literature stresses the social character of science as well as the role of human values in science, it still reinforces what educational scientists often call a positivist view of science regarding the character of knowledge. McComas et al. ( 1998 ) present a comprehensive list that summarizes the established view on the nature of science (NOS) that must be taught in science education. In recent literature, this so-called consensus view on the NOS has remained mostly unchanged.

Notable exceptions is the early work of Cartwright, Nersessian, and Dupré, and see more recent work by Mattila 2005 ; Mitchell 2009 ; Frodeman 2010 ; Green 2013 , Grüne-Yanoff 2011 , 2014 ; and Holbrook 2013 . Also see the work by Andersen, Goddiksen, Nersessian, MacLeod, Schmidt, and Thorén referred to in this article.

Nonetheless, the unity of science , has been disputed already at an early stage by dissidents such as Dupré ( 1983 ), and also see Mitchell ( 2009 ).

On this view, science is scientific theories.

See Andersen ( 2013 , 2016 ) for a comprehensive explanation of Kuhn’s ideas related to disciplines and interdisciplinarity.

Our approach to the philosophy of science agrees in many respects with Chang ( 2012 , 2014 ) who argues that “serious study of science must be concerned with what it is that we actually do in scientific work. … Scientific work consists in actions, carried out by agents. An agent carries out intentions. A scientist is not a passive receiver of facts or an algorithmic processor of propositions. … [Therefore,] in serious study of science, we need to consider human capabilities (capacities and skills) in performing epistemic activities” (Chang 2014 , 70). Chang ( 2012 ) proposes the notions epistemic activities and systems of practice – i.e., a system of practice is formed by a coherent set of epistemic activities performed in view of aiming to achieve certain purposes. Additionally, it is the overall purposes of a system of practice that define what it means for the system to be functionally coherent .

Yet, Chang ( 2012 ) argues that the notion ‘system of practice’ is better suit for the analysis of practices than Kuhn’s notion ‘disciplinary matrix’ because, according to Chang, it is not clear how the elements of Kuhn’s matrix hang together. In our view, Chang adds important insights, especially by emphasizing the role of epistemic activities in scientific practices and the overall purpose of a system of practice together with the idea of ‘functional coherence’ between epistemic activities in forming a system of practice, but we suggest to consider Kuhn’s notion of ‘disciplinary matrix’ and Chang’s notion of ‘system of practice’ as complementary to a better understanding scientific practices. Additionally, contrary to Chang, we claim that there exists coherence between the elements of the matrix in the sense that these elements mutually support and reinforce each other, which is why it functions as a paradigm.

Admittedly, assuming reductive relationships between theories, as in traditional unity of science views (Oppenheim and Putnam 1958 ; Nagel 1961 ), makes intelligible how integration in science takes place.

Maull ( 1977 ) and Darden and Maull ( 1977 ) introduced the notion of ‘field’ to enable talk about (non-hierarchical and non-reductive) interrelationships that historically develop between fields. They did not introduce ‘field’ as an alternative to ‘discipline.’ In this article, we use ‘fields’ and ‘disciplines’ interchangeably.

Both Giere and Suárez defend to have intentionally developed a deflationary notion of representation that only minimally characterizes the representational relationship between model and real-world target system.

Giere’s ( 2006 ) and Van Fraassen’s ( 2008 ) account of representation are important contributions to developing this understanding, but their accounts are beyond the current scope.

A more systematic explanation of this so-called B&K method for the (re)construction of scientific models is presented in Boon ( 2019 ). The B&K method helps researchers to understand scientific models in an unfamiliar discipline D A in terms of the a list of elements that guide, enable and confine the way in which researchers in D A construct models. Therefore, this list of elements indicates the kinds of aspects that determine the disciplinary matrix, and the suggested method of (re)construction scientific models can be interpreted as a metacognitive scaffold to assist in interdisciplinary communication – in this case, to learn how discipline D A typically constructs scientific models.

Several authors in the educational sciences who explicitly reject a positivist epistemology in science education, have proposed concept-mapping as a way to teach science in a more constructivist fashion – in this case, as a way to better understand scientific concepts as compared to traditional rote learning (e.g., Novak 1990 ; Weideman and Kritzinger 2003 ; Addae et al. 2012 ; Thomas et al. 2016 ). Concept-mapping definitely fits with the engineering paradigm. We also recognize the potential of this approach for learning scientific research. However, concept-mapping is usually introduced as a rather ‘empty’ framework, which students find hard to use. Therefore, we suggest for this approach to become an effective metacognitive scaffold , some more guidance is needed in how to construct a concept-map, for instance similar to the introduction of concrete elements in a matrix (left column of Table 1 ), or the concrete elements in the method to construct scientific models.

Abd-El-Khalick, F. (2013). Teaching with and about nature of science, and science teacher knowledge domains. Science & Education, 22 (9), 2087–2107. https://doi.org/10.1007/s11191-012-9520-2 .

Article Google Scholar

Aboelela, S. W., Larson, E., Bakken, S., Carrasquillo, O., Formicola, A., Glied, S. A., . . . Gebbie, K. M. (2007). Defining interdisciplinary research: Conclusions from a critical review of the literature. Health Services Research , 42, 329–346. https://doi.org/10.1111/j.1475-6773.2006.00621.x

Addae, J. I., Wilson, J. I., & Carrington, C. (2012). Students’ perception of a modified form of PBL using concept mapping. Medical Teacher, 34 (11), e756–e762. https://doi.org/10.3109/0142159X.2012.689440 .

Alvargonzález, D. (2011). Multidisciplinarity, Interdisciplinarity, Transdisciplinarity, and the sciences. International Studies in the Philosophy of Science, 25 (4), 387–403. https://doi.org/10.1080/02698595.2011.623366 .

Andersen, H. (2013). The second essential tension: On tradition and innovation in interdisciplinary research. Topoi, 32 (1), 3–8. https://doi.org/10.1007/s11245-012-9133-z .

Andersen, H. (2016). Collaboration, interdisciplinarity, and the epistemology of contemporary science. Studies in History and Philosophy of Science Part A, 56 , 1–10. https://doi.org/10.1016/j.shpsa.2015.10.006 .

Andersen, H., & Wagenknecht, S. (2013). Epistemic dependence in interdisciplinary groups. Synthese, 190 (11), 1881–1898. https://doi.org/10.1007/s11229-012-0172-1 .

Aneas, A. (2015). Transdisciplinary technology education: A characterisation and some ideas for implementation in the university. Studies in Higher Education, 40 (9), 1715–1728. https://doi.org/10.1080/03075079.2014.899341 .

Apostel, L., Berger, G., Briggs, A., & Michaud, G. (Eds.). (1972). Interdisciplinarity problems of teaching and research in universities . Paris: Organization for Economic Cooperation and Development.

Google Scholar

Aram, J. D. (2004). Concepts of interdisciplinarity: Configurations of knowledge and action. Human Relations, 57 (4), 379–412 http://www.journals.sagepub.com/doi/abs/10.1177/0018726704043893 .

Bammer, G. (2013). Disciplining Interdisciplinarity - integration and implementation sciences for researching complex real-world problems. Canberra: Australian National University E-press.

Bergmann, M. (2012). The integrative approach in transdisciplinary research. In M. Bergmann, T. Jahn, T. Knobloch, W. Krohn, C. Pohl, & E. Schramm (Eds.), Methods for transdisciplinary research - A primer for practice (pp. 22–49). Frankfurt/New York: Campus Verlag.

Boon, M. (2006). How science is applied in technology. International Studies in the Philosophy of Science, 20 (01), 27–47. https://doi.org/10.1080/02698590600640992 .

Boon, M. (2011). In defense of engineering sciences: On the epistemological relations between science and technology. Techné: Research in Philosophy and Technology, 15 (1), 49–71. https://doi.org/10.5840/techne20111515 .

Boon, M. (2012). Scientific concepts in the engineering sciences: Epistemic tools for creating and intervening with phenomena. In U. Feest & F. Steinle (Eds.), Scientific concepts and investigative practice (pp. 219–243). Berlin: De Gruyter.

Boon, M. (2015). Contingency and inevitability in science – Instruments, interfaces and the independent world. In L. Soler, E. Trizio, & A. Pickering (Eds.), Science as it could have been: Discussing the contingent/inevitable aspects of scientific practices (pp. 151–174). Pittsburgh: University of Pittsburgh Press.

Boon, M. (2017a). An engineering paradigm in the biomedical sciences: Knowledge as epistemic tool. Progress in Biophysics and Molecular Biology, 129, 25–39. doi:j.pbiomolbio.2017.04.001.

Boon, M. (2017b). Philosophy of Science In Practice: A Proposal for Epistemological Constructivism. In H. Leitgeb, I. Niiniluoto, P. Seppälä, & E. Sober (Eds.), Logic, Methodology and Philosophy of Science – Proceedings of the 15th International Congress (CLMPS 2015). College Publications, 289–310.

Boon, M. (2017c). Measurements in the engineering sciences: An epistemology of producing knowledge of physical phenomena. In N. Mößner & A. Nordmann (Eds.), Reasoning in measurement (pp. 203–219). London and New York: Routledge.

Boon, M. (2019) scientific methodology in the engineering sciences. Chapter 4 in the Routledge Handbook of Philosophy of Engineering . D. Michelfelder and N. Doorn (eds.). New York: Taylor & Francis / Routledge.

Boon, M., & Knuuttila, T. (2009). Models as epistemic tools in engineering sciences: A pragmatic approach. In a. Meijers (Ed.), Philosophy of technology and engineering sciences. Handbook of the philosophy of science (Vol. 9, pp. 687–720): Elsevier/North-Holland.

Bosque-Perez, N. A., Klos, P. Z., Force, J. E., Waits, L. P., Cleary, K., Rhoades, P., . . . Holbrook, J. D. (2016). A pedagogical model for team-based, problem-focused interdisciplinary doctoral education. BioScience , 66(6), 477–488. https://doi.org/10.1093/biosci/biw042 .

Boumans, M. (1999). Built-in justification. In M. S. Morgan & M. Morrison (Eds.), Models as mediators - perspectives on natural and social science (pp. 66–96). Cambridge: Cambridge University Press.

Chapter Google Scholar

Cartwright, N. (1983). How the Laws of physics lie . Oxford: Clarendon Press, Oxford University Press.

Book Google Scholar

Cartwright, N. (1999). The dappled world. A study of the boundaries of science . Cambridge University Press.

Cat, J. (2014). The Unity of Science. In E. N. Zalta (Ed.), The Stanford Encyclopedia of Philosophy (Winter 2014 Edition).

Chan, C. K., Zhao, Y., & Luk, L. Y. (2017). A validated and reliable instrument investigating engineering students’ perceptions of competency in generic skills. Journal of Engineering Education, 106 (2), 299–325. https://doi.org/10.1002/jee.20165 .

Chang, H. (2012). Is water H2O? Evidence, realism and pluralism: Springer The Netherlands.

Chang, H. (2014). Epistemic Activities and Systems of Practice: Units of Analysis in Philosophy of Science After the Practice Turn. In: L. Soler, M. Lynch, S. D. Zwart, & V. Israel-Jost (Eds.), Science after the Practice Turn in the Philosophy, History, and Social Studies of Science (pp. 75–87): Routledge.

Collins, H. M., & Evans, R. (2002). The third wave of science studies: Studies of expertise and experience. Social Studies of Science, 32 , 235–296.

Collins, H., & Evans, R. (2007). Rethinking Expertise. Chicago . London: The University of Chicago Press.

Cullingan, P. J., & Pena-Mora, F. (2010). Engineering. In R. Frodeman (Ed.), The Oxford handbook of interdisciplinarity (pp. 147–160). Oxford: Oxford University Press.

Darden, L., & Maull, N. (1977). Interfield theories. Philosophy of Science, 44 (1), 43–64.

DeZure, D. (2010). Interdisciplinary pedagogies in higher education. In R. Frodeman (Ed.), The Oxford handbook of interdisciplinarity (pp. 372–387). Oxford: Oxford University Press.

Dupré, J. (1983). The disunity of science. Mind, 92 (367), 321–346.

Edmondson, K. M., & Novak, J. D. (1993). The interplay of scientific epistemological views, learning strategies, and attitudes of college students. Journal of Research in Science Teaching, 30 (6), 547–559. https://doi.org/10.1002/tea.3660300604 .

Flavell, J. H. (1979). Metacognition and cognitive monitoring: A new area of cognitive–developmental inquiry. American Psychologist, 34 (10), 906. Retrieved from http://www4.ncsu.edu/~jlnietfe/Metacog_Articles_files/Flavell%20(1979).pdf –911.

Fortuin, K. P. J., & van Koppen, C. S. A. (2016). Teaching and learning reflexive skills in inter- and transdisciplinary research: A framework and its application in environmental science education. Environmental Education Research, 22 (5), 697–716. https://doi.org/10.1080/13504622.2015.1054264 .

Frodeman, R. (2010). Introduction. In R. Frodeman, J. T. Klein, & C. Mitcham (Eds.), The Oxford Handbook of Interdisciplinarity (pp. xxix-xxxix): The Oxford University Press.

Frodeman, R., & Mitcham, C. (2007). New directions in Interdisciplinarity: Broad, deep, and critical. Bulletin of Science, Technology & Society, 27 (6), 506–514. https://doi.org/10.1177/0270467607308284 .

Giere, R. N. (1999). Science without Laws: Science and its conceptual foundations . Chicago: Chicago University Press.

Giere, R. N. (2004). How models are used to represent reality. Philosophy of Science, 71 , 742–752.

Giere, R. N. (2006). Scientific Perspectivism . Chicago and London: The University of Chicago Press.

Giere, R. N. (2010). An agent-based conception of models and scientific representation. Synthese, 172 , 269–281.

Gnaur, D., Svidt, K., & Thygesen, M. (2015). Developing students’ collaborative skills in interdisciplinary learning environments. International Journal of Engineering Education, 31 (1B), 257–266.

Goddiksen, M. P. (2014). Clarifiying interactional and contributory expertise. Studies in History and Philosophy of Science. Part A, 47 , 111–117.

Goddiksen, M., & Andersen, H. (2014). Expertise in interdisciplinary science and education . [Preprint]. Retrieved from: http://philsci-archive.pitt.edu/id/eprint/11151

Grantham, T. A. (2004). Conceptualizing the (dis)unity of science. Philosophy of Science, 71 (2), 133–155. https://doi.org/10.1086/383008 .

Green, S. (2013). When one model is not enough: Combining epistemic tools in systems biology. Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences, 44 (2), 170–180. https://doi.org/10.1016/j.shpsc.2013.03.012 .

Grüne-Yanoff, T. (2011). Models as products of interdisciplinary exchange: Evidence from evolutionary game theory. Studies in History and Philosophy of Science Part A, 42 (2), 386–397. https://doi.org/10.1016/j.shpsa.2010.12.004 .

Grüne-Yanoff, T. (2014). Teaching philosophy of science to scientists: Why, what and how. European Journal for Philosophy of Science, 4 (1), 115–134. https://doi.org/10.1007/s13194-013-0078-x .

Haynes, C., & Brown-Leonard, J. (2010). From surprise parties to mapmaking: Undergraduate journeys toward interdisciplinary understanding. The Journal of Higher Education, 81 (5), 645–666. https://doi.org/10.1080/00221546.2010.11779070 .

Hirsch-Hadorn, G., Pohl, C., & Bammer, G. (2010). Solving problems through transdisciplinary research. In R. Frodeman (Ed.), The Oxford handbook of interdisciplinarity (pp. 431–452). Oxford: Oxford University Press.

Holbrook, J. B. (2013). What is interdisciplinary communication? Reflections on the very idea of disciplinary integration. Synthese, 190 (11), 1865–1879. https://doi.org/10.1007/s11229-012-0179-7 .

Huutoniemi, K., Klein, J. T., Bruun, H., & Hukkinen, J. (2010). Analyzing interdisciplinarity: Typology and indicators. Research Policy, 39 , 79–88.

Ivanitskaya, L., Clark, D., Montgomery, G., & Primeau, R. (2002). Interdisciplinary learning: Process and outcomes. Innovative Higher Education, 27 (2), 95–111. https://doi.org/10.1023/A:1021105309984 .

Jacobs, J. A., & Frickel, S. (2009). Interdisciplinarity: A critical assessment. Annual Review of Sociology, 35 , 43–65.

Jantsch, E. (1972). Inter- and Transdisciplinary University: A systems approach to education and innovation. Higher Education, 1 (1), 7–37.

Khosa, D. K., & Volet, S. E. (2013). Promoting effective collaborative case-based learning at university: A metacognitive intervention. Studies in Higher Education, 38 (6), 870–889. https://doi.org/10.1080/03075079.2011.604409 .

Klein J. T. (1990). Interdisciplinarity: History, Theory and Practice . Detroit: Wayne state University Press.

Klein J. T. (1996). Crossing Boundaries: Knowledge, Disciplinarities, and Interdisciplinarities . Charlottesville: University Press.

Klein, J. T. (2010). A taxonomy of interdisciplinarity. In R. Frodeman, J. T. Klein, & C. Mitcham (Eds.), The Oxford handbook of interdisciplinarity (pp. 15–30). Oxford: Oxford University Press.

Kline, S. J. (1995). Conceptual foundations for multidisciplinary thinking: Stanford University Press.

Knuuttila, T., & Boon, M. (2011). How do models give us knowledge? The case of Carnot’s ideal heat engine. European Journal for Philosophy of Science, 1 (3), 309–334. https://doi.org/10.1007/s13194-011-0029-3 .

Krohn, W. (2010). Interdisciplinary cases and disciplinary knowledge. In R. Frodeman, J. T. Klein, & C. Mitcham (Eds.), The Oxford handbook of interdisciplinarity (pp. 31–49). Oxford: Oxford University Press.

Kuhn, T. S. (1970). The structure of scientific revolutions (second ed.). Chicago: The University of Chicago Press.

Lattuca, L. R. (2001). Creating interdisciplinarity: Interdisciplinary research and teaching among college and university faculty : Vanderbilt University Press.

Lattuca, L. R. (2002). Learning interdisciplinarity: Sociocultural perspectives on academic work. The Journal of Higher Education, 73 (6), 711–739 http://www.jstor.org/stable/1558403 .

Lattuca, L. R., Knight, D. B., Ro, H. K., & Novoselich, B. J. (2017). Supporting the development of Engineers' interdisciplinary competence. Journal of Engineering Education, 106 (1), 71–97. https://doi.org/10.1002/jee.20155 .

Liu, S. Y., Lin, C. S., & Tsai, C. C. (2011). College Students' scientific epistemological views and thinking patterns in Socioscientific decision making. Science Education, 95 (3), 497–517. https://doi.org/10.1002/sce.20422 .

Lourdel, N., Gondran, N., Laforest, V., Debray, B., & Brodhag, C. (2007). Sustainable development cognitive map: A new method of evaluating student understanding. International Journal of Sustainability in Higher Education, 8 (2), 170–182. https://doi.org/10.1108/14676370710726634 .

MacLeod, M. (2016). What makes interdisciplinarity difficult? Some consequences of domain specificity in interdisciplinary practice. Synthese, 195 , 1–24. https://doi.org/10.1007/s11229-016-1236-4 .

MacLeod, M., & Nersessian, N. J. (2013). Coupling simulation and experiment: The bimodal strategy in integrative systems biology. Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences, 44 (4, Part A), 572–584. https://doi.org/10.1016/j.shpsc.2013.07.001 .

Maki, U. (2016). Philosophy of interdisciplinarity. What? Why? How? European Journal for Philosophy of Science, 6 (3), 327–342. https://doi.org/10.1007/s13194-016-0162-0 .

Mansilla, V. B. (2010). Learning to synthesize: the development of interdisciplinary understanding. In R. Frodeman (Ed.), The Oxford handbook of interdisciplinarity (pp. 288–306): Oxford University Press.

Mattila, E. (2005). Interdisciplinarity “in the making”: Modeling infectious diseases. Perspectives on Science, 13 (4), 531–553. https://doi.org/10.1162/106361405775466081 .

Maull, N. L. (1977). Unifying science without reduction. Studies in History and Philosophy of Science Part A, 8 (2), 143–162. https://doi.org/10.1016/0039-3681(77)90012-7 .

McComas, W. F., Almazroa, H., & Clough, M. P. (1998). The nature of science in science education: An introduction. Science & Education, 7 (6), 511–532. https://doi.org/10.1023/A:1008642510402 .

Menken, S., & Keestra, M. (2016). An introduction to interdisciplinary research: Theory and practice . Amsterdam: University Press.

Mitchell, S. D. (2009). Unsimple Truths, Science Complexity and Policy. Chicago and Londen . The University of Chicago Press.

Nagel, E. (1961). The structure of science; problems in the logic of scientific explanation . New York: Harcourt, Brace and World.

National Academy of Sciences; National Academy of Engineering; Institute of Medicine; Policy and Global Affairs; Committee on Science, E., and Public Policy; Committee on Facilitating Interdisciplinary Research. (2005). Facilitating interdisciplinary research . Washington, DC: The National Academies Press.

National Science Foundation. (2008). Impact of transformative interdisciplinary research and graduate education on academic institutions . Washington, Cd.

Nersessian, N. J. (2009). Creating scientific concepts . Cambridge, MA: MIT Press.

Nersessian, N. J., & Patton, C. (2009). Model-based reasoning in interdisciplinary engineering. In a. W. M. Meijers (Ed.), Handbook of the Philosophy of Technology and Engineering Sciences (pp. 687–718).

Newell, W. H. (2001). A theory of interdisciplinary studies. Issues in Integrative Studies, 19 , 1–25.

Newell, W. H. (2013). The state of the field: Interdisciplinary theory. Issues in Interdisciplinary Studies, 31 , 22–43.

Newstetter, W. C. (2005). Designing cognitive apprenticeships for biomedical engineering. Journal of Engineering Education, 94 (2), 207–213.

Nikitina, S. (2006). Three strategies for interdisciplinary teaching: Contextualizing, conceptualizing, and problem-centring. Journal of Curriculum Studies, 38 (3), 251–271. https://doi.org/10.1080/00220270500422632 .

Novak, J. D. (1990). Concept mapping: A useful tool for science education. Journal of Research in Science Teaching, 27 (10), 937–949. https://doi.org/10.1002/tea.3660271003 .

Oppenheim, P., & Putnam, H. (1958). Unity of science as a working hypothesis. In H. Feigl, M. Scriven, & G. Maxwell (Eds.), Minnesota studies in the philosophy of science (Vol. 2, pp. 3–36). Minneapolis: University of Minnesota Press.

O'Rourke, M., Crowley, S., & Gonnerman, C. (2016). On the nature of cross-disciplinary integration: A philosophical framework. Studies in History and Philosophy of Science Part C, 56 , 62–70. https://doi.org/10.1016/j.shpsc.2015.10.003 .

Pintrich, P. R. (2002). The role of metacognitive knowledge in learning, teaching. and assessing. Theory into practice, 41 (4), 219–225 http://www.tandfonline.com/doi/pdf/10.1207/s15430421tip4104_3 .

Procee, H. (2006). Reflection in education: A Kantian epistemology. Educational Theory, 56 (3), 237–253. https://doi.org/10.1111/j.1741-5446.2006.00225.x .

Repko, A. F. (2008). Interdisciplinary research: Process and theory . Thousand Oaks, CA: Sage.

Repko, A. F., & Szostak, R. (2017 3rd ed.). Interdisciplinary research: Process and theory . Los Angeles: Sage.

Repko, A., Navakas, F., & Fiscella, J. (2007). Integrating Interdisciplinarity: How the theories of common ground and Cognitive_Interdisciplinarity are informing the debate on interdisciplinary integration. Issues in Interdisciplinary Studies, 25, 1–31.

Robles, M. M. (2012). Executive perceptions of the top 10 soft skills needed in Today’s workplace. Business Communication Quarterly, 75 (4), 453–465. https://doi.org/10.1177/1080569912460400 .

Rossini, F. A., & Porter, A. L. (1979). Frameworks for integrating interdisciplinary research. Research Policy, 8 , 70–79.

Schmidt, J. C. (2008). Towards a philosophy of interdisciplinarity. Poiesis & Praxis, 5 (1), 53–69. https://doi.org/10.1007/s10202-007-0037-8 .

Schmidt, J. C. (2011). What is a problem? On problem-oriented interdisciplinarity. Poiesis & Praxis, 7 (4), 249–274. https://doi.org/10.1007/s10202-011-0091-0 .

Sin, C. (2014). Epistemology, sociology, and learning and teaching in physics. Science Education, 98 (2), 342–365. https://doi.org/10.1002/sce.21100 .

Spelt, E. J., Biemans, H. J., Tobi, H., Luning, P. A., & Mulder, M. (2009). Teaching and learning in interdisciplinary higher education: A systematic review. Educational Psychology Review, 21 (4), 365–378. https://doi.org/10.1007/s10648-009-9113-z .

Stentoft, D. (2017). From saying to doing interdisciplinary learning: Is problem-based learning the answer? Active Learning in Higher Education . (online) http://www.journals.sagepub.com/doi/abs/10.1177/1469787417693510

Strang, V. (2009). Integrating the social and natural sciences in environmental research: A discussion paper. Environment, Development and Sustainability, 11 (1), 1–18. https://doi.org/10.1007/s10668-007-9095-2 .

Suárez, M. (2003). Scientific representation: Against similarity and isomorphism. International Studies in the Philosophy of Science, 17 (3), 225–244.

Suárez, M. (2010). Scientific representation. Philosophy Compass, 5 (1), 91–101.

Thomas, L., Bennett, S., & Lockyer, L. (2016). Using concept maps and goal-setting to support the development of self-regulated learning in a problem-based learning curriculum. Medical Teacher, 38 (9), 930–935. https://doi.org/10.3109/0142159x.2015.1132408 .

Thorén, H. (2015). The hammer and the nail: interdisciplinarity and problem solving in sustainability science . PhD thesis (pp. 1-356). Lund University.

Thorén, H., & Persson, J. (2013). The philosophy of Interdisciplinarity: Sustainability science and problem-feeding. Journal for General Philosophy of Science, 44 (2), 337–355. https://doi.org/10.1007/s10838-013-9233-5 .

Tsai, C. C. (2007). Teachers' scientific epistemological views: The coherence with instruction and students' views. Science Education, 91 (2), 222–243. https://doi.org/10.1002/sce.20175 .

Tuana, N. (2013). Embedding philosophers in the practices of science: Bringing humanities to the sciences. Synthese, 190 (11), 1955–1973. https://doi.org/10.1007/s11229-012-0171-2 .

Turner, S. (2000). What are disciplines? And how is interdisciplinarity different. In N. Stehr & P. Weingart (Eds.), Practising interdisciplinarity (pp. 46–65). Toronto: University of Toronto Press.

Turner, V. K., Benessaiah, K., Warren, S., & Iwaniec, D. (2015). Essential tensions in interdisciplinary scholarship: Navigating challenges in affect, epistemologies, and structure in environment–society research centers. Higher Education, 70 (4), 649–665. https://doi.org/10.1007/s10734-015-9859-9 .

Van den Beemt, A., MacLeod, M., Van der Veen, J. T., Van de Ven, A. M. A., Van Baalen, S. J., Klaassen, R. G., & Boon, M. (under review). Interdisciplinary engineering education as a holy grail: A systematic review on vision, education, and facilitation.

Van Fraassen, B. C. (2008). Scientific representation . Oxford: Oxford University Press.

Weideman, M., & Kritzinger, W. (2003). Concept mapping: A proposed theoretical model for implementation as a knowledge repository. ICT in Higher Education. Retrieved from https://web.stanford.edu/dept/SUSE/projects/ireport/articles/concept_maps/Concept%20map%20as%20knwoledge%20repository.pdf

Yerrick, R. K., Pedersen, J. E., & Arnason, J. (1998). "We're just spectators": A case study of science teaching, epistemology, and classroom management. Science Education, 82 (6), 619–648.

Zohar, A., & Barzilai, S. (2013). A review of research on metacognition in science education: Current and future directions. Studies in Science Education, 49 (2), 121–169. https://doi.org/10.1080/03057267.2013.847261 .

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Acknowledgements

Earlier versions of this paper have been presented at the SPSP2015 pre-workshop on Teaching Interdisciplinarity in Ahrhus, and in the symposium on Challenges for the implementation of Interdisciplinarity at EPSA2017 in Exeter. This work is financed by an Aspasia grant (409.40216) of the Dutch National Science Foundation (NWO) for the project Philosophy of Science for the Engineering Sciences , and by the work package Interdisciplinary Engineering Education at the 4TU-CEE (Centre Engineering Education) in The Netherlands. We wish to thank Henk Procee and two anonymous reviewers for constructive suggestions.

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Boon, M., Van Baalen, S. Epistemology for interdisciplinary research – shifting philosophical paradigms of science. Euro Jnl Phil Sci 9 , 16 (2019). https://doi.org/10.1007/s13194-018-0242-4

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A journal of philosophy, applied to the real world.

The Fundamental Problem of Philosophy: Its Point

Ingmar Persson

University of Gothenburg, Oxford Uehiro Centre for Practical Ethics

The fundamental problem of philosophy is whether doing it has any point, since if it does not have any point, there is no reason to do it. It is suggested that the intrinsic point of doing philosophy is to establish a rational consensus about what the answers to its main questions are. But it seems that this cannot be accomplished because philosophical arguments are bound to be inconclusive. Still, philosophical research generates an increasing number of finer grained distinctions in terms of which we try to conceptualize reality, and this is a sort of progress. But if, as is likely, our arguments do not suffice to decide between these alternatives, our personalities might slip in to do so. Our philosophy will then express our personality. This could provide philosophy with a point for us. If some of our conclusions have practical import, philosophy could have the further point of giving us something by which we can live.

1. Why Philosophy Fails in the Fundamental Respect of Having An Intrinsic Point

In The Myth of Sisyfos Albert Camus straightaway claims that suicide is the fundamental problem of philosophy, perhaps even the only really serious philosophical problem. He suggests that the importance of a problem is determined by what actions the problem — or, I suppose, rather an answer to it — commits you to. But we should distinguish the philosophical problem which is most important for philosophy , or for us as philosophers , from the philosophical problem which is most important for us all things considered , or as regards all aspects of our lives . It is the former that is most appropriately called the fundamental problem of philosophy, whereas the latter might instead be called the most important problem of philosophy.

Accordingly, I propose to explore what is the fundamental problem of philosophy by looking more narrowly at the consequences for philosophy of answering different philosophical problems. I believe that an application of this strategy lands us in the claim that the fundamental philosophical problem is whether (doing) philosophy has any point . For philosophy, the consequence of doing philosophy being without any point is as drastic as it can be: apparently, it is that there is no good reason to do philosophy. It might be that, for the business of living, other philosophical problems are more important. For instance, it may be more important whether life has a point because if it does not have any point, there would be no reason to prolong life.

It is more important for us whether life has a point than whether philosophy has a point, since if life has some point, but not philosophy, we still have reason to live, whereas if philosophy has some point, but not life, we cannot rationally philosophize, since we have no good reason to live which, of course, is necessary to do philosophy. Notice that even if philosophy has a point, it does not follow that life has a point, only that something in life has a point. Indeed, it seems that part of the point of philosophy could conceivably consist in showing that life does not have any point. If this were so — it will soon emerge that there is reason to doubt that it is so—part of the point of philosophy would be self-defeating, since if life comes to an end, so does philosophy. In any case, the question I am asking is whether philosophy has any point.

The fact that the problem about the point of philosophy is itself a philosophical problem signals a difference between philosophy and other disciplines, since it is not—at least not entirely—a mathematical problem whether mathematics has any point, nor a medical problem whether medicine has any point, and so forth—these are rather (at least partly) philosophical problems, like whether life has a point. This fact poses a special difficulty because in order to find out whether philosophy has any point, we have to philosophize , thereby boldly risking to do something that turns out to be pointless!

The question about the point of philosophy should be made more precise. Doing philosophy could obviously have a point by serving various extrinsic or external ends, such as earning your livelihood, getting recognition for your acumen, or getting the sort of fun you could also get from intellectual games and pastimes. But now I am interested in its intrinsic point, a goal or end that its distinctive method of argumentation and conceptual clarification is designed to attain. (What characterizes this method is, as everything else in philosophy, moot.)

What would provide philosophy with such a point? A reply that readily suggests itself is that it could prove what the true answers to its chief problems are; this would appear to be the outcome that employment of its argumentative and clarificatory method is designed for. But it might seem that any old answer would not do given that it is proven true, for suppose that it is proved that the true answer to the philosophical question whether philosophy has any intrinsic point is that it does not have any. Then it would be paradoxical if it could be inferred that philosophy has an intrinsic point from the fact that it has shown that it does not have any such point! Likewise, we might be disinclined to concede that philosophy could show itself to have an intrinsic point by demonstrating that life does not have any point. For, as remarked, this would be self-defeating, since to do philosophy we have to go on living.

Therefore, we might better deny that philosophy can secure its having an intrinsic point by proving the wrong kind of true answers to philosophical problems. Presumably, these wrongful answers would be skeptical or negative answers. Possible examples—other than the ones just mentioned—would be if philosophy denied us benevolent gods, immortal souls, free will in the most valuable sense, knowledge of a physical world that exists independently of perception, an intelligible connection between mind and body, objective moral truths, and so on. Perhaps if philosophy were to yield such disappointing conclusions all across the board, we would be loath to ascribe any intrinsic point to it, even though it would have delivered true answers to its major problems. Only a minority of us would aim to vindicate for its own sake philosophical truths that we expect to be crushing.

Another possibility is that philosophy fails to have an intrinsic point because it is bound to be inconclusive , incapable of producing a rational consensus about the answers to its leading problems. A glance at its inception makes one possible reason for such inconclusiveness discernible. Philosophy began its life around 2500 years ago as speculation about general aspects of the world. Gradually, experiential and other empirical methods were established to deal with some of these aspects. The study of these aspects then dissociated themselves and became more specific disciplines—physics, biology, etc—in their own right, with their established methodologies. Philosophy remained as the tumultuous leftover that was recalcitrant to any agreed, more precise methodology.

A second reason for its inconclusiveness surfaced when it was remarked above that philosophy is special in that the problem of whether philosophy has any point is itself a philosophical problem. For this indicates that a distinctive feature of philosophy is that it cannot take anything for granted . There could be no other discipline to which the investigation of philosophical presuppositions putatively beyond the scope of its self-examination could be delegated. An inquiry into the most fundamental matters is—by definition, it would appear—philosophical, fundamental matters being bound to remain in the contentious leftover. Since philosophy is in this sense bottomless, or goes all the way down, it seems that it will inevitably be inconclusive: even if philosophical arguments are logically valid—and, thus, guarantee true conclusions if their premises are true—they will inescapably have some premises whose truth can be denied or doubted because in the end they run out of support. It would appear to be especially likely that people will be tempted to deny or doubt some essential premise or other of an argument that has a disappointing conclusion; for this reason alone, inconclusiveness appears to be a more realistic candidate than a consensus about disappointing conclusions for robbing philosophy of its intrinsic point.

Big philosophical controversies frequently assume the form of there being, on the one hand, a pre-reflective intuition which has a strong hold on us in our commonsensical frame of mind and, on the other hand, weighty philosophical arguments against it. For instance, we have a steadfast intuition that there exists a physical world independent of our sense-impressions; yet there are powerful skeptical arguments challenging the justification of this intuition. We are convinced that many of our inductive extrapolations are reliable, but Hume made us realize that it is hard to see how this conviction can be justified. We like to believe that moral norms can be objectively valid in some sense, though it is not easy see what this sense can be. We believe that we can be more or less deserving and that we are more responsible for what we cause by our actions than let happen by our omissions, but these beliefs are opposed by strong reasons for doubt. And so on. Attempts can be made to break such-like dialectical impasses by finding further arguments on one side or the other. This is however likely to lead to similar dialectical impasses because, as noted, in philosophy more or less any useful claim can be—and has been—doubted. Eventually arguments will peter out, and it will have to be extraneous factors such as our personalities and how social circumstances impinge on them that determine whether we come down on one side or the other.

I suppose its bottomlessness and attendant methodological disagreements largely explain why the views of different philosophers often diverge or branch out right from the start into philosophies characteristic of their authors—the philosophy of Spinoza, Kant, etc—while we only rarely have occasion to resort to this manner of speaking in the context of other academic disciplines. When we do—as in the case of Newtonian physics as opposed to the relativistic physics of Einstein—it is because there are disagreements which pertain to the very foundations of the discipline.

Undeniably, there is a fair amount of intersubjective agreement of a negative sort among philosophers. To take just one example, even though he tried his utmost to be critical and skeptical, Descartes still claimed to perceive most clearly and distinctly it to be necessarily true that his idea of something more perfect than himself derives from something that is in fact more perfect than himself. There is in all probability unanimity today that this claim is not necessarily true—indeed, that it is patently false.

However, such a negative consensus does not bring us one whit closer to unanimity concerning the solution of any philosophical problem of note. With respect to such problems, we find ourselves on paths which go on forking indefinitely, making it harder and harder to decide what turns are the right ones. We draw distinctions that empower us to state theses with ever greater precision, but this process of splitting up theses into several more precise versions also makes it harder to determine which of these competing theses are true, or even closer to the truth. Therefore, growing philosophical precision is not like the more precise measurements of something’s weight or length which undoubtedly bring us closer to its real weight or length.

It cannot be denied that greater conceptual precision constitutes progress or development in the discipline of philosophy, but to my mind this is not enough to show that philosophy has any intrinsic point. After all, we can get more and more adept at activities that are pointless, e.g. pastimes that we do just to kill time. If the outcome of our multiplication of distinctions is greater uncertainty and bewilderment about where the truth lies within the space of these distinctions, it seems dubious whether these endeavours could have any intrinsic point for us, whether we could rationally engage in them for their own sake, knowing full well that they will not enable us to close in on the truth.

On the other hand, it might be said that this process of conceptual refinement teaches us to appreciate the complexities of the issues. Even so, what drives us to making ever more distinctions is the idea that they bring us closer to the truth (unless it is that they serve extrinsic ends, such as getting better academic jobs or more recognition for smartness). So, this appreciation of complexity cannot be the primary intrinsic point of doing philosophy that we are looking for, but at most a secondary or subsidiary intrinsic point, which rides on the back of some other intrinsic point. Knowledge of this complexity is rather a by-product of the striving to establish a rational consensus about where the philosophical truth lies.

But even if philosophical disputes are bottomless, their being inconclusive is not anything we could justifiably assume at the outset; it is something that we shall have to work our way towards, by assembling evidence by hard experience of failures to establish agreement. This suggests that up to a point debating a philosophical issue can have a point because there is room for a rational hope of a resolution of it, and as long as this is so, we could legitimately reap benefits in the shape of conceptual clarification and greater understanding of conceptual complexities. But, as the debate continues, a suspicion is liable to grow on us that the arguments are getting too contrived and convoluted for there to be a realistic possibility of them bringing the issue to a close.

Perhaps we can compare the states of the art in contemporary philosophy and in contemporary physics. Contemporary physicists undoubtedly know more about the universe than Newton did, but they are nevertheless also aware that their ignorance about the universe is much greater than he apparently thought his ignorance was. Contemporary physicists are painfully aware of the incompatibility of the two pillars of modern physics, quantum physics and the theory of relativity, of the fact that they know next to nothing about the dark matter and energy which may compose as much as 95% of the universe, of the nature of the Big Bang, and so on. Furthermore, it may be seriously doubted whether they will ever have access to experimental means to make ground-breaking progress, to settle the truth of theories requisite for such progress, for example, to settle what, if any, string theory or multiversum theory is true. Then the point of pursuing research to make more pedestrian progress may be questioned, especially if, as is likely, it will be forbiddingly expensive.

By comparison, philosophical research is exceedingly cheap, and lack of observational data will not be what puts obstacles in its way. The obstacle will rather be that our shared intuitions are not fine-grained enough to decide between ever more precise proposals. This difficulty will increase rather than decrease the number of issues over which there will be disagreement. But with respect to neither physics nor philosophy would the fact that further research might now be pointless because it is clear that it is degenerating into irresolvable disputes about esoteric matters imply that research in the past has been pointless.

2. Philosophy as a Means to Self-Knowledge and as Life-Guiding

The example of Descartes and the most perfect being is worth bringing up also for the reason that it is a good example of wishful thinking in philosophy, of how a claim can appear to be evidently true to philosophers when it is a potent tool in their hands, even though the claim is rather evidently false, and they attempt to adopt a skeptical frame of mind. For Descartes thought he could prove the existence of God—who would then function as a guarantor of the truth of other important beliefs—with the help of the claim that the source of the idea of a being more perfect than himself must lie in something more perfect than himself.

It is worth noting that there is an attitude that can be seen to be a form of wishful thinking about oneself and one’s own assets: the overconfidence bias , the tendency to be unduly confident about one’s own ability to get the facts right, or to perform various practical tasks well. For instance, a majority of drivers believe they drive better than the average, a majority of students believe they are in the top half of their class, a majority of university professors believe they are better researchers and teachers than most of their colleagues. As regards the future, most of us are inclined to think we are capable of achieving a lot more in a certain period of time than it is reasonable to surmise on the basis of what we have achieved in the past—this is the so-called planning fallacy . In academia it notoriously manifests itself in many of us regularly overstepping deadlines that we have optimistically laid down.

The overconfidence bias or more generally wishful thinking may account for why philosophers do not normally end up with disappointing conclusions or concessions about inconclusiveness, and why they are inclined to tweak their philosophizing to yield more satisfactory outcomes. For instance, it may explain why skepticism is not a popular position in epistemology and dualism is not in the philosophy of mind, despite the fact that, after centuries of efforts, no attempt to refute skepticism has won more widespread acceptance, and attempts to reduce the mental to the physical have continued to be failures, though they have successively become watered down.

The world comes out as simpler and scientifically more manageable if there is not anything mental which is distinct in kind from everything physical, since if there is something irreducibly mental, its relation to the physical—in particular, neural states in the brain—appears to be inexplicable. Also, there is the problem of fitting together the mentalistic explanations we provide of our overt behaviour with the mechanistic explanations science delivers of the physical events. In the pre-scientific past, humans often tried to explain natural phenomena in mentalistic terms, as the works of gods or spirits. With the progress of science these animistic explanations have been superseded by mechanistic explanations. Some philosophers seem to want carry on this process to the paradoxical extreme of exorcising or evicting the inquisitive, explanation-seeking mind itself from the world. However, our urge to think in mentalistic terms is unquenchable. It has definitely lost its applicability to inanimate nature, but it will never surrender its applicability to our own behaviour. And it is as vigorous as ever in its enterprise of creating fictive characters in novels, films, computer games, and so on. It is astonishing that, although the earth is now the home to more than seven billion people, we still have not had our fill of people, but eagerly create countless fictive ones in whose company we happily spend a considerable portion of our time.

Wishful thinking, then, is a factor which could step in and clinch cases when the strength of arguments cannot. Another factor is conformism : many studies show that we are reluctant to stand out from the people surrounding us but instead prefer to go along with them. There is a disposition that works in the longer term to support conformism, namely the exposure effect : as a rule, we get attached to what we have been frequently exposed to, like the social and cultural traditions and fellow-beings with which we have grown up. Obviously, if the majority of the citizens of a society tend to comply with its customs, their behaviour will exhibit considerable amount of conformity. The exposure effect guarantees that the ways of life of a society will be stable and change only slowly.

An important species of conformism is people’s inclination to obey those who have managed to ascend to some type of leading positions. This subordination to leaders or authorities is yet another factor that may shape our philosophical views. Students are swayed in the direction of their supervisors’ views due to their persuasive power or charisma, or because they sense that this is prone to improve their career prospects. Even students who started out in earnest, set on discovering the truth of some philosophical matter, may slide into defending views that are suspiciously similar to the views of their teachers. There is a win-win situation here: the students secure a career, and their teachers that their work and reputation live on.

But in the process the former may be transformed from being philosophers living for philosophy to being philosophers living off philosophy in Schopenhauer’s terminology, just as the latter may once have been. This is a process which could occur even if we are not under the influence of any authority. If no conclusive philosophical arguments are to be found, it would not be surprising if in the course of doing philosophy over many years, the belief that such arguments can be found wear thin and are replaced by a slowly growing conviction that philosophy does not have any primary, intrinsic point. But you have invested so much time and effort in philosophy, and made it your profession, so the sunk cost of giving it up would be insufferable. Thusly, living for philosophy could gradually turn into living off philosophy.

The expression ‘living off philosophy’, however, is a bit misleading because it suggests doing philosophy to earn your livelihood. This is important enough if you have not inherited wealth like, say, Schopenhauer and Kierkegaard. But the expression is meant to cover doing philosophy for all kinds of external ends, including getting recognition and intellectual stimulation of the sort that could be got from games like chess. The phrase ‘living for philosophy’ means doing philosophy for its own sake, with its primary, intrinsic point in mind, that it could rationally convince us of some worthwhile philosophical insights.

Now, if we get disillusioned about the possibility of establishing a rational philosophical consensus and, thus, cannot pursue it with this point in mind, is there any alternative to doing it only for fun, fame or fortune? If we possess enough independence of mind to be able to resist the lures of conformism and the rewards that might accrue from it then, when truth-supporting arguments fall short, our own personality traits—rather than the personality traits of others via our conformism—may seep in and influence our conclusions. After all, it was probably such traits that determined our choice to do philosophy in the first place, and the original choice of a philosophical orientation or specialization, so they may play a part in settling our philosophical conclusions as well. William James goes a bit further (James, 2004 Ebook):

‘Of whatever temperament a professional philosopher is, he tries when philosophizing to sink the fact of his temperament…Yet his temperament really gives him a stronger bias than any of his more strictly objective premises. It loads the evidence for him one way or the other.’

Nietzsche put it characteristically hyperbolically (Nietzsche, 1973, §6):

‘every great philosophy has hitherto been: a confession on the part of its author’

The author’s personality shines through not only in the philosophies of such ‘literary’ philosophers as Nietzsche, Schopenhauer and Kierkegaard, but also in the philosophy of a highly academic philosopher like Kant, in which you can clearly discern traces of his Pietistic upbringing and obsessive-compulsive turn of mind.

In order to obtain a more detailed imprint of your personality, however, you have to be a rather well-rounded philosopher, who works across a fairly wide field of philosophical problems—like the great philosophers of the past—rather than a narrow specialist. It is natural, probably even necessary, for philosophical novices to start out as specialists, and only later to become more well-rounded by broadening their horizons gradually, though some issues will have to remain peripheral, receiving only fleeting attention. Careerwise, it is however likely that you will do better if you remain more of a narrow specialist operating in a network with other specialists in the same field who mutually support each other than if you attempt to become a more independently viable, well-rounded philosopher.

Moreover, as philosophy becomes more and more conceptually intricate by the labour of a growing number of specialized philosophers, it becomes increasingly hard for its practitioners to become well-rounded and counteract a fragmentization of philosophy. At least analytic philosophy is prone to contract or spiral inwards in the sense of developing ever more precise treatments of a set of problems prioritized in networks of philosophers who discuss each others work. This is harmful to analytic philosophy overall both because it is likely to make it irrelevant to people outside these networks and because it hampers philosophical progress by turning a blind eye to problems with the presuppositions shared by members of the networks. But to the insiders who take the presuppositions for granted, precision by a proliferation of technicalities gives an air of scientific objectivity.

If your personality traits influence the upshot of your philosophy and, thus, make it expressive of your personality—could it still have something of an intrinsic point? It could, at least in the sense that activities like art and literature, which are obviously also expressive of their creators’ personalities, could have something of an intrinsic point. But philosophy would not then have an exclusively intrinsic point as it would have if its method of rational argument had sufficed to force agreement about the solutions to its major problems. For if philosophical argument turns out to be inconclusive, and your personality has to creep in to make you come down on one side or the other, the point of doing philosophy cannot be wholly intrinsic to it. Its point will be dependent on your taking an interest in something external to philosophy, namely in having your personality articulated or revealed by it. Its point is however still partly intrinsic to it, since your interest is invested in arriving at a philosophical articulation or expression of your personality, in working out philosophical arguments to the point at which you could convince at least yourself. Doing philosophy in order to ‘know thyself’ is a time-honoured task which is sufficient for philosophy to have a point for you , given your interest in gaining self-knowledge, though you will not be pursuing philosophy strictly for its own sake.

In antiquity philosophy was supposed to be of help not only in the acquisition of self-knowledge, but also in learning how to live well. For instance, according to Epicurus’s conception of it (Long & Sedley, 1987, vol I, 156):

‘Philosophy is an activity that secures the flourishing [eudaimon] life by arguments and reasoning.’

A more sombre conception of the kind of advice philosophy could supply is the one that Plato puts into the mouth of Socrates in his dialogue Phaedo : to philosophize is learning how to die. I shall not try to unravel what Plato might have meant by this arresting assertion, which many have puzzled over. My purpose is just to illustrate that provision of practical advice about how to live has been conceived to be an essential element of philosophy since its inception.

This could be put by saying that, alongside living for philosophy and living off philosophy, we should place living by philosophy, or living in accordance with philosophy. Perhaps symptomatically, Schopenhauer omits to mention the latter option. He has been criticized for failing to comply with the stern precepts of his pessimistic world-view which demanded thorough-going asceticism. His defence for this omission referred to his determinism which he took to rule out behavioural reform. In this respect he stands in unflattering contrast to another determinist, Spinoza, who seemed to have succeeded admirably well in living in accordance with an equally demanding philosophy.

Like the aim that your philosophy be personality-revealing, the aim that it be life-guiding is partly extrinsic and partly intrinsic to philosophy. It is partly extrinsic, since the aim of having a philosophy that you could live by is extrinsic to philosophy, but also partly intrinsic, since it is specifically philosophical doctrines that you aim to live by. If you have both of these aims, you will strive to make your philosophy well-rounded by exploring topics where your findings could have practical implications. You will not specialize exclusively in fields like, say, meaning and reference or causation and conditionals, but will ensure that your philosophical repertoire includes such disciplines as normative ethics. Naturally, the outcome of your pursuit of these life-guiding disciplines will also reflect your personality.

I shall soon turn to ethics, but let me first make clear that what is now designated as ethics or morality does not have monopoly of life-guiding doctrines. For example, in Reasons and Persons Derek Parfit (1984, pt III) famously argues that ‘personal identity is not what matters’, that is, that the fact that some person is identical to you cannot rationally justify your being especially concerned about the weal and woe of this person. 1 According to him, what matters is instead the holding of various psychological relations, such as this person sharing your memories, interests, etc. In principle, these are relations that could connect you to somebody to whom you are not identical. Now this doctrine about the insignificance of personal identity is not a distinctively moral doctrine, though it is of moral relevance, since it undermines self-interest which is often in opposition to the morally right course of action. It is not a distinctively moral doctrine, since it harbours implications also for the sphere of prudence where what is at stake is only how your own interests are affected. It tells that you should focus on whether what matters in identity is present—like psychological connections—rather than simply on the fact of identity itself.

For another illustration of life-guiding doctrines that are not specifically moral, consider Parfit’s discussion of temporal biases in Reasons and Persons , pt. II. One such bias is the bias towards the near (future), the fact that we are spontaneously more concerned about good and bad things that we think might happen to us in the near rather than in the more distant future. Another temporal bias is the bias towards the future , the fact that we are spontaneously more concerned about good and bad things that will happen to us in the future—especially the near future—than about such things that have happened to us in the past, so that we regret good things having passed, and are relieved when bad things have passed. It could be argued—though Parfit does not do so—that these temporal biases are irrational and that the rational attitude is one of temporal neutrality . 2 In the domain of prudence, this attitude of neutrality means that what happens at some times of your own life does not matter more than what happens at other times simply in virtue of the difference of timing, whilst in its moral form it lays down that the same holds for each and everyone’s life.

In ancient Greece, ethics comprised the intrapersonal dimension of prudence alongside the interpersonal dimension of morality. Its chief question was ‘How should I live in order to lead a good life?’ where ‘good’ covered what is good for ourselves as well as what is good for others. Nowadays, ethics or morality is rather thought to regulate our conduct only in so far as it impinges on the weal and woe of others, in other words, others for whom things can go well or badly (many would say that this is the category of sentient beings). We are regarded as imprudent or irrational rather than as immoral if we act in manners that are detrimental to our own long-term interest.

Greek and Roman philosophers were much preoccupied with the question of how we could lead good lives in view of the fact that our lives are largely beyond our control, a matter of good or bad luck: at any moment we could be struck down by accidents or injuries which rob us of our fortune, health or even life itself, which we shall eventually lose in any case. 3 Philosophical schools competed against each other with different recommendations as to how to come to terms with this precariousness of our existence. For instance, the Stoic emperor Marcus Aurelius’ recommendations featured a version of temporal neutrality: looking at our lives from the point of view of high above which makes them dwindle to insignificance. 4 The doctrine that your identity does not matter could also be of assistance in muting the anxiety that we might feel because of all the harm that preys on us in every nook and cranny of the world. But whatever the philosophical recommendations, they were not easy to internalize and live by. Thus, many ancient philosophers, not least the Stoics, spent more time on exercises to train themselves to live by their convictions than to argue for their truth.

Turning now to morality, we again encounter doctrines that are demanding and hard to live by. I have maintained that philosophical divides often arise as the result of pre-reflective intuitions being pitted against weighty philosophical arguments. Such arguments challenge doctrines that are firmly entrenched in common-sense morality, for instance, that we have rights to our own body and mind, and property that we acquire by their means, that we deserve to fare better or worse than we in fact do, and the act-omission doctrine and the doctrine of the double effect . 5 The fact that these doctrines are so firmly entrenched will by itself make us disinclined to surrender them. But with respect to at least some of them—e.g. the rights theory and the act-omission doctrine—surrendering them will result in a morality that demands greater sacrifices from us than does common-sense morality, especially in the present, needful world in which those of us who are affluent have resources to mitigate much misery. This naturally makes many of us even more reluctant to surrender these deep-seated doctrines and confront our glaring moral shortcomings. For such reasons, the prospect that moral philosophers could demonstrate that their discipline has achieved its primary, intrinsic point by arriving at a rational consensus about what is morally right and wrong looks glum, though their investigations could serve the subsidiary point of producing a steadily growing understanding of the complexities of our moral notions by an ever-expanding battery of precise distinctions.

Another thing moral philosophers could do is to carry on their investigations until they reach a normative position with which their personality makes them comfortable. Then, apart from trying to live by this position, they could strive to spread the word of it, since other people could accept this position for the same arguments as they have, though these arguments are not conclusive. Moral campaigning of this kind is important because morality is essentially a collective code which, if valid, must be universally valid, valid for everyone capable of understanding it. We must agree about what is morally right or wrong because this concerns how we treat each other, not just ourselves; by contrast, the prudential norms we uphold for the running of our own lives could be entirely individualist, valid only for ourselves with our particular aims. Besides, some moral goals are such that we cannot attain them single-handed; their attainment necessitates the cooperation of a great number of agents. Consequently, if there is no trust that these goals are pursued by a multitude of agents, it might well be futile to contribute to them. Therefore, if moral philosophy cannot achieve its primary, intrinsic point by producing a rational consensus about what is morally right and wrong in the situations we regularly face in our lives, the second best we could do is to aim for as broad an agreement as possible with congenial people.

Far from establishing a rational consensus about what is morally right, and about what the ground and meaning of this rightness is, moral philosophers have produced a perplexing array of possible moral systems—consequentialist, deontological, contractualist, virtue ethical, you name it—but no agreed method to decide which of these system is the sound one. Indeed, it is even controversial what ‘soundness’ here is tantamount to, whether moral judgments can be true in the same sense as factual judgments, and true independently of our affective or conative attitudes, or whether moral judgments are merely non-cognitive expressions of such attitudes.

If it had not been for the fact that moral philosophy is often too esoteric to be grasped by the public, the substantial disagreement that is raging among its practitioners might have had a deleterious effect on public morality. Philosophical disputes about the foundation and content of morality might have eroded the authority that common-sense morality has acquired over centuries as a result of the exposure effect, and weakened the motivation to abide by it. It seems unlikely that this substantial disagreement will subside, for even though our moral responses must converge to some extent if we are to be able to live together in functioning societies—which is a pre-requisite of our evolutionary success—they are surely not so finely attuned that we should expect them to converge with respect to the manifold of fanciful scenarios that our philosophically trained cognitive powers could construct. However, the existing attitudinal convergence might still be sufficient for the campaigning indicated to establish well-grounded agreement on a large number of moral issues.

In fact, I am not sure that moral philosophy has made any noteworthy difference to public morality during the forty years or so that I have been engaged in it. Consider, for instance, what is sometimes hailed as the greatest achievement of modern morality: the recognition of the equal worth of all humans. There is no denying that there are very good philosophical arguments undercutting the view of racists and sexists that the differences between races and sexes by themselves could be grounds for differing moral value. But from the fact that some differences between humans cannot ground value differences between them, it obviously does not follow that no such differences between them can do so.

What about differences in respect of, say, intelligence, rationality or morality, which are often propounded as the basis for humans having higher value than non-human animals? These features certainly appear to be of value, but humans plainly differ as regards them, so an appeal to them is unpromising as a justification for the doctrine that all humans are of equal moral worth or value. Likewise, the appeal to them to justify the idea that humans have a higher value than non-human animals is ill-conceived, since it clearly is not true that all humans—including the most mentally disabled—rank higher with respect to intelligence, rationality and morality than all non-human animals. An appeal to membership of the biological species Homo sapiens by itself as a ground for moral elevation will not improve the situation because it is as little plausible as an appeal to membership of a race or sex as such a ground. All in all, philosophical discussion appears to lend more support to the—to many of us—disappointing conclusion that the value of all humans is not equal and higher than the value of non-human animals. But, for better or worse, this discussion has not noticeably influenced public opinion.

What, then, is the explanation of the popularity of the doctrine that all humans are equal? Perhaps that in a globalized world people of different races come together for commerce and cultural exchange and demand consideration of their interests. Furthermore, due to the advanced technology and the more effective legal regimes of modern societies, the most conspicuous advantages of men over women as regards physical strength and aggressiveness recede into the background. And the invention and legislation of contraceptives have reduced the risk that women are hampered by frequent unwanted pregnancies. So, women are in a better position to claim the same consideration as men. The most obvious way to resolve such competition between claims for consideration from people of different races and sexes seems to be to give equal weight to all of them. If something like this hypothesis is correct, the ideology of human equality is the product of social, technological and other such-like forces having nothing to do with any reflection on the grounds of moral status.

3. Conclusion

Summing up, given the apparently inescapable inconclusiveness of philosophical arguments, philosophy could not have the primary, intrinsic point of establishing a rational consensus about the solutions of its leading problems. It could still have the subsidiary point of promoting greater awareness of the complexities of the conceptual apparatus by means of which we attempt to decipher the world. It could also produce arguments that persuade us if we allow our personalities to slip in and give them a finishing touch. To the extent that this is so, our philosophical position will give vent to our personality. This could provide philosophy with a point for us which is partly, but not entirely intrinsic to it, since it is in part dependent on our interest in having our personality shine through our philosophy. Naturally, for our philosophy to express our personality more fully, it has to be well-rounded. Additionally, if our philosophy contains elements of practical import, it could also have point for us by equipping us with something we could live by. Living by philosophy should be distinguished from living off it in the broad sense here intended, namely pursuing philosophy for wholly extrinsic reasons such as earning our livelihood, getting recognition for smartness and intellectual stimulation, ends to which there could be other means—even better means—than philosophy. There is a perpetual risk of sliding into the living-off philosophy mode because of the elusiveness of philosophical truth and the pressures of conformism.

To end on a more personal note, I have never managed to live for philosophy in the sense implying that I thought I would eventually find arguments that would conclusively solve any of its big problems. From the start it has seemed to me that it was too late in the history of philosophy—which features so many confident philosophers who have had their convictions resoundingly refuted—to entertain seriously any such hope. I have never doubted that it was unavoidable that my personality would enter somewhere in the game to fix the outcome. Also, a large part of the fascination philosophy has had for me throughout my pursuit of it stems from the fact that it is uniquely able to combine the theoretical and practical: a philosophical vision of the world encompassing practical implications to live by.

Acknowledgements

Many thanks to two reviewers for helpful comments.

James, W, Pragmatism, Lecture I. — The Present Dilemma in Philosophy , 2004 Ebook. Available from http://www.gutenberg.org/files/5116/5116-h/5116-h.htm#link2H_4_0003 [accessed 6 June 2018].

Long, A. A. & Sedley, D. N. (eds.) The Hellenistic Philosopher s, Cambridge: Cambridge U. P., 1987.

Nietzsche, F, Beyond Good and Evil , trans. R. J. Hollingdale, London: Penguin, 1973.

Parfit, D, Reasons and Persons , Oxford: Clarendon Press, 1984.

Persson, I, The Retreat of Reason , Oxford: Oxford University Press, 2005.

——— From Morality to the End of Reason , Oxford: Oxford University Press, 2013.

——— Inclusive Ethics , Oxford: Oxford University Press, 2017.

1. For my take on this issue, see The Retreat of Reason (2005, pt. IV), and Inclusive Ethics (2017, chap. 3.1).

2. I argue thus in The Retreat of Reason (2005, pt. III).

3. I discuss this issue a bit further in Inclusive Ethics (2017, chap. 13).

4. See for instance his Meditations , 9. 30. The view sub species aeternitatis also plays an important role in Spinoza’s Ethics .

5. I argue against the applicability of the concept of desert in The Retreat of Reason (2005, pt. IV), and Inclusive Ethics (2017, chap. 7); against rights, the act-omission doctrine and the doctrine of the double effect in From Morality to the End of Reason (2013, chaps. 1-6).

Into all problem-solving, a little dissent must fall

Events of the past several years have reiterated for executives the importance of collaboration and of welcoming diverse perspectives when trying to solve complicated workplace problems. Companies weren’t fully prepared for the onset of a global pandemic, for instance, and all that it engendered—including supply chain snarls and the resulting Great Attrition and shift to remote (and now hybrid) work, which required employers to fundamentally rethink their talent strategies . But in most cases leaders have been able to collaborate their way through the uncertainty, engage in rigorous debate and analyses about the best steps to take, and work with employees, suppliers, partners, and other critical stakeholders to react and, ultimately, recover.

And It’s not just COVID-19: many organisations have had to rethink their business strategies and practices in the wake of environmental concerns, the war in Ukraine, and social movements sparked by racial injustice, sexual misconduct, and widespread economic inequity . Ours are fast-moving, complex times, rich not just in worrisome challenges but also in exciting potential—organisations that enable innovation will find ample opportunities to thrive. So now more than ever, decision makers can’t act alone; they must bring diverse perspectives to the table and ensure that those voices are fully heard . 1 Sundiatu Dixon-Fyle, Kevin Dolan, Vivian Hunt, and Sara Prince, “ Diversity wins: How inclusion matters ,” McKinsey, May 19, 2020.

But while many leaders say they welcome dissent, their reactions often change when they actually get some. They may feel defensive. They may question their own judgment. They may resent having to take time to revisit the decision-making process. These are natural responses, of course; employees’ loyalty and affirmation are more reassuring to leaders than robust challenges from the group. There is discomfort, too, for potential dissenters; it is much safer to keep your thoughts to yourself and conform than to risk expulsion from the group. 2 Derived from this work on the evolutionary origins of social and political behavior: Christopher Boehm, Hierarchy in the Forest: The Evolution of Egalitarian Behavior , Cambridge, Massachusetts: Harvard University Press, 2001.

What’s missing in many companies, in our experience, is the use of “contributory dissent” or the capabilities required to engage in healthy if divergent discussions about critical business problems. Contributory dissent allows individuals and groups to air their differences in a way that moves the discussion toward a positive outcome and doesn’t undermine leadership or group cohesion . 3 McKinsey itself has established obligation to dissent as one of its core values alongside those focused on client service and talent development. For more, see Bill Taylor, “True leaders believe dissent is an obligation,” Harvard Business Review , January 12, 2017.

McKinsey’s research and experience in the field point to several steps leaders can take to engage in healthy dissent and build a culture where constructive feedback is expected and where communication is forthright. These include modeling “open” behaviors, embedding psychological safety and robust debate into decision-making processes, and equipping employees with the communication skills that will allow them to contribute dissenting opinions effectively.

In this article we outline the steps leaders can take to encourage healthy dissent, and the actions teams and individuals can take to share their voices and perspectives most effectively. It takes both sides, after all, to engage in robust debate, find the right solutions, and enable lasting, positive change.

How leaders can encourage contributory dissent

Senior leaders in an organisation play a central role in ensuring that individuals and teams see contributory dissent as a normal part of any discussion. They can signal the importance of dissent by taking a series of steps to institutionalise the practice within an organisation and empower employees to share their ideas freely and productively. Specifically, senior leaders should strive to inspire rather than direct employees to collaborate, explicitly demand dissent and, taking that one step further, actively engage with naysayers (see sidebar “How to encourage healthy dissent”). 4 Leaders can also draw on McKinsey’s “influence model” for changing mindsets and behaviors: role modeling, fostering understanding and conviction, reinforcing with formal mechanisms, and developing talent and skills. For more, see Tessa Basford and Bill Schaninger, “ The four building blocks of change ,” McKinsey Quarterly , April 11, 2016.

Inspire, don’t direct

How to encourage healthy dissent.

To encourage dissent through personal leadership:

Lead to inspire, not to direct:

Empower the group to come up with ideas: “None of us knows the answer yet, but we can work it out together if we harness the best of everyone’s thinking.”

Foster dissent by actively seeking it:

Explicitly seek dissent; give people permission and encouragement.
Consider including dissent as a stated organisational value.
Make provision for open discussion in the buildup to decisions.

Welcome open discussion when it comes:

Listen to dissenters and naysayers, and thank them for their insights.
Recognise this as a usefully unfiltered channel for understanding the organisation’s perceptions on issues.
Seek to bring dissenters along the decision journey, so they become positive influencers later during implementation.
Employ deliberate techniques such as red teaming and pre-mortems to widen the debate and mitigate groupthink.

As the inspirational speaker Simon Sinek put it, “The role of a leader is not to come up with all the great ideas. The role of a leader is to create an environment in which great ideas can happen.” 5 Simon Sinek, Start with Why: How Great Leaders Inspire Everyone to Take Action , New York, NY: Portfolio, 2009. That is especially important for fostering an atmosphere of collaboration and contributory dissent. Rather than immediately jump into a discussion about solutions, one senior leader in an international organisation addressed his team’s anxiety in the wake of a crisis. “Let me guess,” he said, “you’re all feeling confused and uncertain about the way ahead. Terrific. I’m so glad we are of one mind and that we all understand our situation correctly! I’m sure that we can work it out together, but it’s going to require the best of everyone’s thinking. Let’s get started.” His authenticity and understated humor allowed him to connect with the group and inspired them to keep calm, carry on, and generate solutions that the leader alone couldn’t have come up with. Harvard professor Ron Heifetz describes this as creating a holding environment, a key element of adaptive leadership. 6 Ronald A. Heifetz and Mary Linksy, Leadership on the Line: Staying Alive through the Dangers of Leading , Boston, MA: Harvard Business School Press, 2002; Ronald Heifetz, Alexander Grashow, and Marty Linksy, The Practice of Adaptive Leadership: Tools and Tactics for Changing Your Organization and the World , Boston, MA: Harvard Business Press, 2009.

Explicitly demand dissent

It’s not enough for leaders to give people permission to dissent; they must demand it of people. In many companies, individuals and teams may (understandably) default to collegiality, not realizing that there are ways to challenge ideas while still respecting colleagues’ roles and intellect. It’s on senior leaders, then, to help employees understand where the boundaries are. In World War 1, Australia’s General Sir John Monash was determined to develop better tactics to overcome the catastrophic impasse of trench warfare. He knew there were answers to be found from the experience of soldiers in the trenches, but he needed to loosen the military discipline of blind obedience: “I don’t care a damn for your loyal service when you think I am right; when I really want it most is when you think I am wrong.” Monash scheduled open battle planning sessions and pulled in advice from whoever offered it. In doing so, he built ownership of and confidence in his plans among all ranks. The resulting orchestration of tanks, artillery, aircraft, and troops led to rapid advances along the Somme Valley, and Monash garnered respect and appreciation from his troops, whose chances of survival and ultimate victory had increased markedly.

Actively engage with naysayers

Taking the demand imperative one step further, it’s beneficial for leaders to actively seek out the views of vocal naysayers , who can turn into influential champions just by being part of the conversation. They can immediately improve the nature of business debate and may boost the quality of the final decision, although engaging with naysayers can be tough. Some dissenting opinions can be ill-informed or uncomfortable to hear. The objective for senior leaders, then, is to put their discomfort aside and listen for signs of cognitive dissonance within an organisation. As an example, front-line employees may say things like “We’re not considered strategic thinkers,” or “The company doesn’t put people first,” while senior management may actually feel as though they have made strides in both of those areas. Still, leaders need to absorb such comments, treat them as useful data points, assess their validity, and engage in what may be a challenging discussion. They may want to use red teams and premortems , in which teams at the outset anticipate all the ways a project could fail, to frame up dissenting opinions, mitigate groupthink, and find a positive resolution. These behaviours also serve to enhance organizational agility and resilience .

How leaders can establish psychological safety

Senior leaders need to establish a work environment in which it is safe to offer dissenting views. The McKinsey Health Institute’s work on employee well-being points to a strong correlation between leadership behaviors, collaborative culture, and resistance to mental health problems and burnout : only 15 percent of employees in environments with low inclusivity and low support for personal growth are highly engaged, compared with 38 percent in high-scoring environments. 7 “ Addressing employee burnout: Are you solving the right problem? ,” McKinsey, May 27, 2022. Leaders can build psychological safety (where team members feel they can take interpersonal risks and remain respected and accepted) and set the conditions for contributory dissent by rethinking how they engage in debate—both the dynamics and the choreography of it.

The dynamics of debate

The poet and playwright Oscar Wilde described a healthy debating culture as one in which people are “playing gracefully with ideas”— listening to, and even nourishing, opposing points of view in a measured and respectful way. 8 The Complete Works of Oscar Wilde, Volume 2: De Profundis, “Epistola: In Carcere et Vinculis,” Oxford, United Kingdom: Clarendon Press, 2005. Indeed, the best ideas can emerge at the intersection of cultures and opinions. In 15th century Florence, for instance, the Medici family attracted and funded creators from across the arts and sciences to establish an epicenter of innovative thinking that sparked the Renaissance. 9 Frans Johansson, The Medici Effect: Breakthrough Insights at the Intersection of Ideas, Concepts, and Culture , Boston, MA: Harvard Business School Press, 2004. Closer to this century, we have seen cross-discipline innovations like the application of biologists’ research on ant colonies to solve problems in telecommunications routing. And in the business world, extraordinary innovations have been achieved by open-minded leaders bringing together smart people and creating the conditions for playful exploration.

To achieve a state of “graceful play,” senior leaders must carefully manage group dynamics during debates. Rather than lead with their own opinions, for instance, which might immediately carry outsize weight in the group and stifle discussion, senior leaders can hold back and let others lead the discussion . They can lean in to show genuine curiosity or to explicitly recognise when a dissenting view has changed their thinking. But by letting other, more junior voices carry the agenda and work through ideas, however imperfect, senior leaders can establish a climate of psychological safety—and garner more respect from colleagues long term. 10 Amy C. Edmondson, The Fearless Organization: Creating Psychological Safety in the Workplace for Learning, Innovation, and Growth , Hoboken, NJ: John Wiley & Sons, 2019.

Leaders will also need to be aware of cultural differences that may crop up during debates. For example, many Australians speak candidly and are happy to address issues squarely. By contrast, the concept of “face” is so important in many Asian cultures that a more circumspect approach is taken. And the Pacific and Maori cultures emphasize displays of both strength and respect. 11 Erin Meyer, The Culture Map: Breaking through the Invisible Boundaries of Global Business , Philadelphia, PA: PublicAffairs, 2014. These differences in debate dynamics really matter. They can be a great source of hybrid vigour, 12 “Heterosis, also called hybrid vigour: the increase in such characteristics as size, growth rate, fertility, and yield of a hybrid organism over those of its parents. The first-generation offspring generally show, in greater measure, the desired characteristics of both parents.” Encyclopedia Britannica , accessed September 19, 2022. if sensitively managed, or a source of conflict and disenfranchisement if not. To approach these differences in a positive way, senior leaders could undertake a mapping exercise that identifies the different styles of the cultures present, thereby providing validation and enabling pragmatic measures to integrate them.

Choreographing debate

Beyond just managing debate dynamics, business leaders must take a hand in choreographing the debate and, specifically, in helping to design collective-thinking processes so people know how best to play their part. Business leaders may adopt a structured approach to brainstorming, for instance, or plan strategic off-site schedules that combine deliberate thinking with “distracted” thinking—taking time to engage in a social activity, for instance—to take advantage of employees’ deep-thinking processes.

How deliberate choices by the leader can optimise a decision-making process

A leader must consciously assess each new situation and design the collective-thinking process accordingly, then articulate this so that people know how best to play their part.

In doing so, the leader should consider an array of questions, the answers to which will determine the context, for example:

What does success look like?
Will the organisation underwrite initial failures in the interests of agility and innovation?
How broad and freethinking an analysis is required?
What are the explicit expectations for contributory dissent?
Are any topics and behaviours out of bounds?
Who will lead the discussion, and how will comments be captured?
Does urgency mean that it’s better to be directive?
Who will be consulted?
Which decisions can be delegated, and to whom?
Whose support needs to be built?
What parameters and boundaries exist?
Are there interim decisions and communications required?
What form should the deliverable outcomes take?
When are the deliverables required?
Direction setting on these parameters by the leader focuses the team, while also creating space for creativity and iterative learning.

To create a sustainable structure for debate, business leaders will need to consider questions relating to team structure and rules of engagement: What does success look like when it comes to contributory dissent? What topics and behaviors are out of bounds? Who will lead the discussion, and how will comments be captured? Who has the final say on decisions, or which decisions can be delegated, and to whom? (For a more comprehensive explanation, see sidebar “How deliberate choices by the leader can optimise a decision-making process.”)

Having these parameters in place can free up the team to think more creatively about the issue at hand. Establishing such protocols can also make it easier to raise dissenting opinions. At one company, people are asked to call out their underlying values or potential biases when expressing a dissenting view. During meetings of the promotion committee, for instance, a statement like “I think we are making the wrong decision” would be rephrased as “I am someone who values experience over collaboration, and this decision would risk losing too much institutional knowledge.”

How individuals and teams can engage and dissent

As we’ve shared, senior leaders can take steps to set conditions for robust discussion and problem-solving, but individuals and teams themselves must also have the right mindsets and skills for contributory dissent to work well (see sidebar “How teams and individuals can dissent effectively”). In particular, they must embrace the obligation to dissent, actively make space to analyse ideas that are different from their own, and then find ways to either iterate on others’ ideas or respectfully agree to disagree.

Embrace the obligation to dissent

How teams and individuals can dissent effectively.

For dissent to be effective, its delivery requires courage and tactical skills underpinned by sincere respect and grace. Speaking up with respect is the right thing to do, and the responsibility to do so exists, even if there is uncertainty. The following guidelines are useful in enabling effective dissent:

Prepare a welcome for dissenting views:

Understand the context and motivations of others, appreciate their views, and syndicate your own.
Stop and strategise before wading into the conversations, establish a solid platform for agreement, and explicitly seek permission to dissent.

Play the long game:

Be open minded and iterative. Don’t expect to succeed on the first try.
Listen to others for what their views might add rather than to defend your own.

Withhold assent if you need to, but do it carefully:

Withholding assent is a legitimate option if done judiciously.
Minimise offense to and loss of face for the decision maker.
If principles or legality is at stake, document your dissent.

Individuals and teams need to exhibit a certain amount of humility and confidence in order to speak truth to power with respect; they must be sure for themselves that doing so is the right thing to do. To build this confidence, individuals and teams should remember that the very act of dissent can be valuable, even if the contribution itself isn’t 100 percent baked. Others can react or build on the dissenting view—which, in itself, can be a satisfying process for a dissenter. If the ultimate decision isn’t what they proposed, they still helped shape it by offering and testing a worthy possibility.

Make space to analyse different views

Individuals and teams may need time to determine their positions on an issue. During this period, it’s important to be (and seen to be) open-minded and respectful of others’ views. That means asking lots of questions, gathering information, assessing others’ motivations, and acknowledging their views before syndicating alternatives of your own. Much of this fact gathering can be done one-on-one, in a nonconfrontational way, in offline conversations rather than in a tension-filled meeting room. In these conversations, individuals could start by reaffirming a shared commitment to finding a solution to the issue at hand, their respect for the decision-making process and the group, and areas of broad agreement. They could also signal their possible intention to dissent and seek permission to do so rather than confronting people head-on. People will find it harder to refuse that permission, and will be less likely to get defensive, when approached with statements like “This is a great discussion, and I love the vision of where we are headed, but would it be OK for us to explore some alternatives for how to get there?”

Agree to iterate …

Individuals and teams that decide to offer dissenting views should agree to iterate on other solutions, rather than digging in. Their dissenting opinions should be cogent, persuasive, and open-minded—but dissenters shouldn’t expect to change hearts and minds on the first try. They should plant seeds gently and bide their time; they might even see their idea come back as someone else’s. The critical skill required here is active, open listening: dissenters should listen carefully for others’ additive insights and find ways to build on them. In their contributory dissent, individuals and teams can take a moment to summarize what others have said and then use statements like “Can I offer another take?” and then allow the momentum of the conversation to take over.

… or agree to disagree

But what happens if, after all the considered and tactful input, the dissenter still believes a decision is heading in the wrong direction? In our experience, withholding assent then becomes a legitimate option: people shouldn’t agree if they don’t agree. This is where all the careful, respectful groundwork the dissenter has done can pay dividends. In fact, a dissenting view gains even more power when an individual can say something like, “I still believe in my alternate solution, but I’m grateful for the opportunity to contribute to this process, and I respect that you have the final say.” In this case, the dissenter is supporting the leader while flagging that the open debate hasn’t convinced them to change their initial view.

Of course, withholding assent should be a relatively rare action, taken only after an individual or team has shown that they can accommodate other views and have aligned with the consensus when they believe it’s right to do so. Think of US Supreme Court associate justice Ruth Bader Ginsburg, who joined the consensus view on many decisions but who is especially celebrated for the positive changes that arose from her highly influential dissenting opinions on issues such as gender equity, human rights, and religious freedom.

Contributory dissent can help strengthen employee engagement, unlock hidden insights, and help organisations solve tough challenges. But putting it into practice takes courage and humility, and it won’t just happen by accident. Leaders need to be intentional about welcoming challenges to their plans and opinions, even when it’s uncomfortable to do so. They need to establish cultures and structures where respectful debate can occur and where individuals and teams feel free to bring innovative—and often better—alternative solutions to the table.

Ben Fletcher is a senior partner in McKinsey’s Sydney office, Chris Hartley is a partner in the Melbourne office, Rupe Hoskin is a senior expert in the Canberra office, and Dana Maor is a senior partner in the Tel Aviv office.

The authors wish to thank Jacqueline Brassey, Nikki Dines, Richard Fitzgerald, Sam Hemphill, Ayush Jain, Jemma King, and Martin Nimmo for their contributions to this article.

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Philosophy of Education

Philosophy of education is the branch of applied or practical philosophy concerned with the nature and aims of education and the philosophical problems arising from educational theory and practice. Because that practice is ubiquitous in and across human societies, its social and individual manifestations so varied, and its influence so profound, the subject is wide-ranging, involving issues in ethics and social/political philosophy, epistemology, metaphysics, philosophy of mind and language, and other areas of philosophy. Because it looks both inward to the parent discipline and outward to educational practice and the social, legal, and institutional contexts in which it takes place, philosophy of education concerns itself with both sides of the traditional theory/practice divide. Its subject matter includes both basic philosophical issues (e.g., the nature of the knowledge worth teaching, the character of educational equality and justice, etc.) and problems concerning specific educational policies and practices (e.g., the desirability of standardized curricula and testing, the social, economic, legal and moral dimensions of specific funding arrangements, the justification of curriculum decisions, etc.). In all this the philosopher of education prizes conceptual clarity, argumentative rigor, the fair-minded consideration of the interests of all involved in or affected by educational efforts and arrangements, and informed and well-reasoned valuation of educational aims and interventions.

Philosophy of education has a long and distinguished history in the Western philosophical tradition, from Socrates’ battles with the sophists to the present day. Many of the most distinguished figures in that tradition incorporated educational concerns into their broader philosophical agendas (Curren 2000, 2018; Rorty 1998). While that history is not the focus here, it is worth noting that the ideals of reasoned inquiry championed by Socrates and his descendants have long informed the view that education should foster in all students, to the extent possible, the disposition to seek reasons and the ability to evaluate them cogently, and to be guided by their evaluations in matters of belief, action and judgment. This view, that education centrally involves the fostering of reason or rationality, has with varying articulations and qualifications been embraced by most of those historical figures; it continues to be defended by contemporary philosophers of education as well (Scheffler 1973 [1989]; Siegel 1988, 1997, 2007, 2017). As with any philosophical thesis it is controversial; some dimensions of the controversy are explored below.

This entry is a selective survey of important contemporary work in Anglophone philosophy of education; it does not treat in detail recent scholarship outside that context.

1. Problems in Delineating the Field

2. analytic philosophy of education and its influence, 3.1 the content of the curriculum and the aims and functions of schooling, 3.2 social, political and moral philosophy, 3.3 social epistemology, virtue epistemology, and the epistemology of education, 3.4 philosophical disputes concerning empirical education research, 4. concluding remarks, other internet resources, related entries.

The inward/outward looking nature of the field of philosophy of education alluded to above makes the task of delineating the field, of giving an over-all picture of the intellectual landscape, somewhat complicated (for a detailed account of this topography, see Phillips 1985, 2010). Suffice it to say that some philosophers, as well as focusing inward on the abstract philosophical issues that concern them, are drawn outwards to discuss or comment on issues that are more commonly regarded as falling within the purview of professional educators, educational researchers, policy-makers and the like. (An example is Michael Scriven, who in his early career was a prominent philosopher of science; later he became a central figure in the development of the field of evaluation of educational and social programs. See Scriven 1991a, 1991b.) At the same time, there are professionals in the educational or closely related spheres who are drawn to discuss one or another of the philosophical issues that they encounter in the course of their work. (An example here is the behaviorist psychologist B.F. Skinner, the central figure in the development of operant conditioning and programmed learning, who in works such as Walden Two (1948) and Beyond Freedom and Dignity (1972) grappled—albeit controversially—with major philosophical issues that were related to his work.)

What makes the field even more amorphous is the existence of works on educational topics, written by well-regarded philosophers who have made major contributions to their discipline; these educational reflections have little or no philosophical content, illustrating the truth that philosophers do not always write philosophy. However, despite this, works in this genre have often been treated as contributions to philosophy of education. (Examples include John Locke’s Some Thoughts Concerning Education [1693] and Bertrand Russell’s rollicking pieces written primarily to raise funds to support a progressive school he ran with his wife. (See Park 1965.)

Finally, as indicated earlier, the domain of education is vast, the issues it raises are almost overwhelmingly numerous and are of great complexity, and the social significance of the field is second to none. These features make the phenomena and problems of education of great interest to a wide range of socially-concerned intellectuals, who bring with them their own favored conceptual frameworks—concepts, theories and ideologies, methods of analysis and argumentation, metaphysical and other assumptions, and the like. It is not surprising that scholars who work in this broad genre also find a home in the field of philosophy of education.

As a result of these various factors, the significant intellectual and social trends of the past few centuries, together with the significant developments in philosophy, all have had an impact on the content of arguments and methods of argumentation in philosophy of education—Marxism, psycho-analysis, existentialism, phenomenology, positivism, post-modernism, pragmatism, neo-liberalism, the several waves of feminism, analytic philosophy in both its ordinary language and more formal guises, are merely the tip of the iceberg.

Conceptual analysis, careful assessment of arguments, the rooting out of ambiguity, the drawing of clarifying distinctions—all of which are at least part of the philosophical toolkit—have been respected activities within philosophy from the dawn of the field. No doubt it somewhat over-simplifies the complex path of intellectual history to suggest that what happened in the twentieth century—early on, in the home discipline itself, and with a lag of a decade or more in philosophy of education—is that philosophical analysis came to be viewed by some scholars as being the major philosophical activity (or set of activities), or even as being the only viable or reputable activity. In any case, as they gained prominence and for a time hegemonic influence during the rise of analytic philosophy early in the twentieth century analytic techniques came to dominate philosophy of education in the middle third of that century (Curren, Robertson, & Hager 2003).

The pioneering work in the modern period entirely in an analytic mode was the short monograph by C.D. Hardie, Truth and Fallacy in Educational Theory (1941; reissued in 1962). In his Introduction, Hardie (who had studied with C.D. Broad and I.A. Richards) made it clear that he was putting all his eggs into the ordinary-language-analysis basket:

The Cambridge analytical school, led by Moore, Broad and Wittgenstein, has attempted so to analyse propositions that it will always be apparent whether the disagreement between philosophers is one concerning matters of fact, or is one concerning the use of words, or is, as is frequently the case, a purely emotive one. It is time, I think, that a similar attitude became common in the field of educational theory. (Hardie 1962: xix)

About a decade after the end of the Second World War the floodgates opened and a stream of work in the analytic mode appeared; the following is merely a sample. D. J. O’Connor published An Introduction to Philosophy of Education (1957) in which, among other things, he argued that the word “theory” as it is used in educational contexts is merely a courtesy title, for educational theories are nothing like what bear this title in the natural sciences. Israel Scheffler, who became the paramount philosopher of education in North America, produced a number of important works including The Language of Education (1960), which contained clarifying and influential analyses of definitions (he distinguished reportive, stipulative, and programmatic types) and the logic of slogans (often these are literally meaningless, and, he argued, should be seen as truncated arguments), Conditions of Knowledge (1965), still the best introduction to the epistemological side of philosophy of education, and Reason and Teaching (1973 [1989]), which in a wide-ranging and influential series of essays makes the case for regarding the fostering of rationality/critical thinking as a fundamental educational ideal (cf. Siegel 2016). B. O. Smith and R. H. Ennis edited the volume Language and Concepts in Education (1961); and R.D. Archambault edited Philosophical Analysis and Education (1965), consisting of essays by a number of prominent British writers, most notably R. S. Peters (whose status in Britain paralleled that of Scheffler in the United States), Paul Hirst, and John Wilson. Topics covered in the Archambault volume were typical of those that became the “bread and butter” of analytic philosophy of education (APE) throughout the English-speaking world—education as a process of initiation, liberal education, the nature of knowledge, types of teaching, and instruction versus indoctrination.

Among the most influential products of APE was the analysis developed by Hirst and Peters (1970) and Peters (1973) of the concept of education itself. Using as a touchstone “normal English usage,” it was concluded that a person who has been educated (rather than instructed or indoctrinated) has been (i) changed for the better; (ii) this change has involved the acquisition of knowledge and intellectual skills and the development of understanding; and (iii) the person has come to care for, or be committed to, the domains of knowledge and skill into which he or she has been initiated. The method used by Hirst and Peters comes across clearly in their handling of the analogy with the concept of “reform”, one they sometimes drew upon for expository purposes. A criminal who has been reformed has changed for the better, and has developed a commitment to the new mode of life (if one or other of these conditions does not hold, a speaker of standard English would not say the criminal has been reformed). Clearly the analogy with reform breaks down with respect to the knowledge and understanding conditions. Elsewhere Peters developed the fruitful notion of “education as initiation”.

The concept of indoctrination was also of great interest to analytic philosophers of education, for, it was argued, getting clear about precisely what constitutes indoctrination also would serve to clarify the border that demarcates it from acceptable educational processes. Thus, whether or not an instructional episode was a case of indoctrination was determined by the content taught, the intention of the instructor, the methods of instruction used, the outcomes of the instruction, or by some combination of these. Adherents of the different analyses used the same general type of argument to make their case, namely, appeal to normal and aberrant usage. Unfortunately, ordinary language analysis did not lead to unanimity of opinion about where this border was located, and rival analyses of the concept were put forward (Snook 1972). The danger of restricting analysis to ordinary language (“normal English usage”) was recognized early on by Scheffler, whose preferred view of analysis emphasized

first, its greater sophistication as regards language, and the interpenetration of language and inquiry, second, its attempt to follow the modern example of the sciences in empirical spirit, in rigor, in attention to detail, in respect for alternatives, and in objectivity of method, and third, its use of techniques of symbolic logic brought to full development only in the last fifty years… It is…this union of scientific spirit and logical method applied toward the clarification of basic ideas that characterizes current analytic philosophy [and that ought to characterize analytic philosophy of education]. (Scheffler 1973 [1989: 9–10])

After a period of dominance, for a number of important reasons the influence of APE went into decline. First, there were growing criticisms that the work of analytic philosophers of education had become focused upon minutiae and in the main was bereft of practical import. (It is worth noting that a 1966 article in Time , reprinted in Lucas 1969, had put forward the same criticism of mainstream philosophy.) Second, in the early 1970’s radical students in Britain accused Peters’ brand of linguistic analysis of conservatism, and of tacitly giving support to “traditional values”—they raised the issue of whose English usage was being analyzed?

Third, criticisms of language analysis in mainstream philosophy had been mounting for some time, and finally after a lag of many years were reaching the attention of philosophers of education; there even had been a surprising degree of interest on the part of the general reading public in the United Kingdom as early as 1959, when Gilbert Ryle, editor of the journal Mind , refused to commission a review of Ernest Gellner’s Words and Things (1959)—a detailed and quite acerbic critique of Wittgenstein’s philosophy and its espousal of ordinary language analysis. (Ryle argued that Gellner’s book was too insulting, a view that drew Bertrand Russell into the fray on Gellner’s side—in the daily press, no less; Russell produced a list of insulting remarks drawn from the work of great philosophers of the past. See Mehta 1963.)

Richard Peters had been given warning that all was not well with APE at a conference in Canada in 1966; after delivering a paper on “The aims of education: A conceptual inquiry” that was based on ordinary language analysis, a philosopher in the audience (William Dray) asked Peters “ whose concepts do we analyze?” Dray went on to suggest that different people, and different groups within society, have different concepts of education. Five years before the radical students raised the same issue, Dray pointed to the possibility that what Peters had presented under the guise of a “logical analysis” was nothing but the favored usage of a certain class of persons—a class that Peters happened to identify with (see Peters 1973, where to the editor’s credit the interaction with Dray is reprinted).

Fourth, during the decade of the seventies when these various critiques of analytic philosophy were in the process of eroding its luster, a spate of translations from the Continent stimulated some philosophers of education in Britain and North America to set out in new directions, and to adopt a new style of writing and argumentation. Key works by Gadamer, Foucault and Derrida appeared in English, and these were followed in 1984 by Lyotard’s The Postmodern Condition . The classic works of Heidegger and Husserl also found new admirers; and feminist philosophers of education were finding their voices—Maxine Greene published a number of pieces in the 1970s and 1980s, including The Dialectic of Freedom (1988); the influential book by Nel Noddings, Caring: A Feminine Approach to Ethics and Moral Education , appeared the same year as the work by Lyotard, followed a year later by Jane Roland Martin’s Reclaiming a Conversation . In more recent years all these trends have continued. APE was and is no longer the center of interest, although, as indicated below, it still retains its voice.

3. Areas of Contemporary Activity

As was stressed at the outset, the field of education is huge and contains within it a virtually inexhaustible number of issues that are of philosophical interest. To attempt comprehensive coverage of how philosophers of education have been working within this thicket would be a quixotic task for a large single volume and is out of the question for a solitary encyclopedia entry. Nevertheless, a valiant attempt to give an overview was made in A Companion to the Philosophy of Education (Curren 2003), which contains more than six-hundred pages divided into forty-five chapters each of which surveys a subfield of work. The following random selection of chapter topics gives a sense of the enormous scope of the field: Sex education, special education, science education, aesthetic education, theories of teaching and learning, religious education, knowledge, truth and learning, cultivating reason, the measurement of learning, multicultural education, education and the politics of identity, education and standards of living, motivation and classroom management, feminism, critical theory, postmodernism, romanticism, the purposes of universities, affirmative action in higher education, and professional education. The Oxford Handbook of Philosophy of Education (Siegel 2009) contains a similarly broad range of articles on (among other things) the epistemic and moral aims of education, liberal education and its imminent demise, thinking and reasoning, fallibilism and fallibility, indoctrination, authenticity, the development of rationality, Socratic teaching, educating the imagination, caring and empathy in moral education, the limits of moral education, the cultivation of character, values education, curriculum and the value of knowledge, education and democracy, art and education, science education and religious toleration, constructivism and scientific methods, multicultural education, prejudice, authority and the interests of children, and on pragmatist, feminist, and postmodernist approaches to philosophy of education.

Given this enormous range, there is no non-arbitrary way to select a small number of topics for further discussion, nor can the topics that are chosen be pursued in great depth. The choice of those below has been made with an eye to highlighting contemporary work that makes solid contact with and contributes to important discussions in general philosophy and/or the academic educational and educational research communities.

The issue of what should be taught to students at all levels of education—the issue of curriculum content—obviously is a fundamental one, and it is an extraordinarily difficult one with which to grapple. In tackling it, care needs to be taken to distinguish between education and schooling—for although education can occur in schools, so can mis-education, and many other things can take place there that are educationally orthogonal (such as the provision of free or subsidized lunches and the development of social networks); and it also must be recognized that education can occur in the home, in libraries and museums, in churches and clubs, in solitary interaction with the public media, and the like.

In developing a curriculum (whether in a specific subject area, or more broadly as the whole range of offerings in an educational institution or system), a number of difficult decisions need to be made. Issues such as the proper ordering or sequencing of topics in the chosen subject, the time to be allocated to each topic, the lab work or excursions or projects that are appropriate for particular topics, can all be regarded as technical issues best resolved either by educationists who have a depth of experience with the target age group or by experts in the psychology of learning and the like. But there are deeper issues, ones concerning the validity of the justifications that have been given for including/excluding particular subjects or topics in the offerings of formal educational institutions. (Why should evolution or creation “science” be included, or excluded, as a topic within the standard high school subject Biology? Is the justification that is given for teaching Economics in some schools coherent and convincing? Do the justifications for including/excluding materials on birth control, patriotism, the Holocaust or wartime atrocities in the curriculum in some school districts stand up to critical scrutiny?)

The different justifications for particular items of curriculum content that have been put forward by philosophers and others since Plato’s pioneering efforts all draw, explicitly or implicitly, upon the positions that the respective theorists hold about at least three sets of issues.

First, what are the aims and/or functions of education (aims and functions are not necessarily the same)? Many aims have been proposed; a short list includes the production of knowledge and knowledgeable students, the fostering of curiosity and inquisitiveness, the enhancement of understanding, the enlargement of the imagination, the civilizing of students, the fostering of rationality and/or autonomy, and the development in students of care, concern and associated dispositions and attitudes (see Siegel 2007 for a longer list). The justifications offered for all such aims have been controversial, and alternative justifications of a single proposed aim can provoke philosophical controversy. Consider the aim of autonomy. Aristotle asked, what constitutes the good life and/or human flourishing, such that education should foster these (Curren 2013)? These two formulations are related, for it is arguable that our educational institutions should aim to equip individuals to pursue this good life—although this is not obvious, both because it is not clear that there is one conception of the good or flourishing life that is the good or flourishing life for everyone, and it is not clear that this is a question that should be settled in advance rather than determined by students for themselves. Thus, for example, if our view of human flourishing includes the capacity to think and act autonomously, then the case can be made that educational institutions—and their curricula—should aim to prepare, or help to prepare, autonomous individuals. A rival justification of the aim of autonomy, associated with Kant, champions the educational fostering of autonomy not on the basis of its contribution to human flourishing, but rather the obligation to treat students with respect as persons (Scheffler 1973 [1989]; Siegel 1988). Still others urge the fostering of autonomy on the basis of students’ fundamental interests, in ways that draw upon both Aristotelian and Kantian conceptual resources (Brighouse 2005, 2009). It is also possible to reject the fostering of autonomy as an educational aim (Hand 2006).

Assuming that the aim can be justified, how students should be helped to become autonomous or develop a conception of the good life and pursue it is of course not immediately obvious, and much philosophical ink has been spilled on the general question of how best to determine curriculum content. One influential line of argument was developed by Paul Hirst, who argued that knowledge is essential for developing and then pursuing a conception of the good life, and because logical analysis shows, he argued, that there are seven basic forms of knowledge, the case can be made that the function of the curriculum is to introduce students to each of these forms (Hirst 1965; see Phillips 1987: ch. 11). Another, suggested by Scheffler, is that curriculum content should be selected so as “to help the learner attain maximum self-sufficiency as economically as possible.” The relevant sorts of economy include those of resources, teacher effort, student effort, and the generalizability or transfer value of content, while the self-sufficiency in question includes

self-awareness, imaginative weighing of alternative courses of action, understanding of other people’s choices and ways of life, decisiveness without rigidity, emancipation from stereotyped ways of thinking and perceiving…empathy… intuition, criticism and independent judgment. (Scheffler 1973 [1989: 123–5])

Both impose important constraints on the curricular content to be taught.

Second, is it justifiable to treat the curriculum of an educational institution as a vehicle for furthering the socio-political interests and goals of a dominant group, or any particular group, including one’s own; and relatedly, is it justifiable to design the curriculum so that it serves as an instrument of control or of social engineering? In the closing decades of the twentieth century there were numerous discussions of curriculum theory, particularly from Marxist and postmodern perspectives, that offered the sobering analysis that in many educational systems, including those in Western democracies, the curriculum did indeed reflect and serve the interests of powerful cultural elites. What to do about this situation (if it is indeed the situation of contemporary educational institutions) is far from clear and is the focus of much work at the interface of philosophy of education and social/political philosophy, some of which is discussed in the next section. A closely related question is this: ought educational institutions be designed to further pre-determined social ends, or rather to enable students to competently evaluate all such ends? Scheffler argued that we should opt for the latter: we must

surrender the idea of shaping or molding the mind of the pupil. The function of education…is rather to liberate the mind, strengthen its critical powers, [and] inform it with knowledge and the capacity for independent inquiry. (Scheffler 1973 [1989: 139])

Third, should educational programs at the elementary and secondary levels be made up of a number of disparate offerings, so that individuals with different interests and abilities and affinities for learning can pursue curricula that are suitable? Or should every student pursue the same curriculum as far as each is able?—a curriculum, it should be noted, that in past cases nearly always was based on the needs or interests of those students who were academically inclined or were destined for elite social roles. Mortimer Adler and others in the late twentieth century sometimes used the aphorism “the best education for the best is the best education for all.”

The thinking here can be explicated in terms of the analogy of an out-of-control virulent disease, for which there is only one type of medicine available; taking a large dose of this medicine is extremely beneficial, and the hope is that taking only a little—while less effective—is better than taking none at all. Medically, this is dubious, while the educational version—forcing students to work, until they exit the system, on topics that do not interest them and for which they have no facility or motivation—has even less merit. (For a critique of Adler and his Paideia Proposal , see Noddings 2015.) It is interesting to compare the modern “one curriculum track for all” position with Plato’s system outlined in the Republic , according to which all students—and importantly this included girls—set out on the same course of study. Over time, as they moved up the educational ladder it would become obvious that some had reached the limit imposed upon them by nature, and they would be directed off into appropriate social roles in which they would find fulfillment, for their abilities would match the demands of these roles. Those who continued on with their education would eventually become members of the ruling class of Guardians.

The publication of John Rawls’s A Theory of Justice in 1971 was the most notable event in the history of political philosophy over the last century. The book spurred a period of ferment in political philosophy that included, among other things, new research on educationally fundamental themes. The principles of justice in educational distribution have perhaps been the dominant theme in this literature, and Rawls’s influence on its development has been pervasive.

Rawls’s theory of justice made so-called “fair equality of opportunity” one of its constitutive principles. Fair equality of opportunity entailed that the distribution of education would not put the children of those who currently occupied coveted social positions at any competitive advantage over other, equally talented and motivated children seeking the qualifications for those positions (Rawls 1971: 72–75). Its purpose was to prevent socio-economic differences from hardening into social castes that were perpetuated across generations. One obvious criticism of fair equality of opportunity is that it does not prohibit an educational distribution that lavished resources on the most talented children while offering minimal opportunities to others. So long as untalented students from wealthy families were assigned opportunities no better than those available to their untalented peers among the poor, no breach of the principle would occur. Even the most moderate egalitarians might find such a distributive regime to be intuitively repugnant.

Repugnance might be mitigated somewhat by the ways in which the overall structure of Rawls’s conception of justice protects the interests of those who fare badly in educational competition. All citizens must enjoy the same basic liberties, and equal liberty always has moral priority over equal opportunity: the former can never be compromised to advance the latter. Further, inequality in the distribution of income and wealth are permitted only to the degree that it serves the interests of the least advantaged group in society. But even with these qualifications, fair equality of opportunity is arguably less than really fair to anyone. The fact that their education should secure ends other than access to the most selective social positions—ends such as artistic appreciation, the kind of self-knowledge that humanistic study can furnish, or civic virtue—is deemed irrelevant according to Rawls’s principle. But surely it is relevant, given that a principle of educational justice must be responsive to the full range of educationally important goods.

Suppose we revise our account of the goods included in educational distribution so that aesthetic appreciation, say, and the necessary understanding and virtue for conscientious citizenship count for just as much as job-related skills. An interesting implication of doing so is that the rationale for requiring equality under any just distribution becomes decreasingly clear. That is because job-related skills are positional whereas the other educational goods are not (Hollis 1982). If you and I both aspire to a career in business management for which we are equally qualified, any increase in your job-related skills is a corresponding disadvantage to me unless I can catch up. Positional goods have a competitive structure by definition, though the ends of civic or aesthetic education do not fit that structure. If you and I aspire to be good citizens and are equal in civic understanding and virtue, an advance in your civic education is no disadvantage to me. On the contrary, it is easier to be a good citizen the better other citizens learn to be. At the very least, so far as non-positional goods figure in our conception of what counts as a good education, the moral stakes of inequality are thereby lowered.

In fact, an emerging alternative to fair equality of opportunity is a principle that stipulates some benchmark of adequacy in achievement or opportunity as the relevant standard of distribution. But it is misleading to represent this as a contrast between egalitarian and sufficientarian conceptions. Philosophically serious interpretations of adequacy derive from the ideal of equal citizenship (Satz 2007; Anderson 2007). Then again, fair equality of opportunity in Rawls’s theory is derived from a more fundamental ideal of equality among citizens. This was arguably true in A Theory of Justice but it is certainly true in his later work (Dworkin 1977: 150–183; Rawls 1993). So, both Rawls’s principle and the emerging alternative share an egalitarian foundation. The debate between adherents of equal opportunity and those misnamed as sufficientarians is certainly not over (e.g., Brighouse & Swift 2009; Jacobs 2010; Warnick 2015). Further progress will likely hinge on explicating the most compelling conception of the egalitarian foundation from which distributive principles are to be inferred. Another Rawls-inspired alternative is that a “prioritarian” distribution of achievement or opportunity might turn out to be the best principle we can come up with—i.e., one that favors the interests of the least advantaged students (Schouten 2012).

The publication of Rawls’s Political Liberalism in 1993 signaled a decisive turning point in his thinking about justice. In his earlier book, the theory of justice had been presented as if it were universally valid. But Rawls had come to think that any theory of justice presented as such was open to reasonable rejection. A more circumspect approach to justification would seek grounds for justice as fairness in an overlapping consensus between the many reasonable values and doctrines that thrive in a democratic political culture. Rawls argued that such a culture is informed by a shared ideal of free and equal citizenship that provided a new, distinctively democratic framework for justifying a conception of justice. The shift to political liberalism involved little revision on Rawls’s part to the content of the principles he favored. But the salience it gave to questions about citizenship in the fabric of liberal political theory had important educational implications. How was the ideal of free and equal citizenship to be instantiated in education in a way that accommodated the range of reasonable values and doctrines encompassed in an overlapping consensus? Political Liberalism has inspired a range of answers to that question (cf. Callan 1997; Clayton 2006; Bull 2008).

Other philosophers besides Rawls in the 1990s took up a cluster of questions about civic education, and not always from a liberal perspective. Alasdair Macintyre’s After Virtue (1984) strongly influenced the development of communitarian political theory which, as its very name might suggest, argued that the cultivation of community could preempt many of the problems with conflicting individual rights at the core of liberalism. As a full-standing alternative to liberalism, communitarianism might have little to recommend it. But it was a spur for liberal philosophers to think about how communities could be built and sustained to support the more familiar projects of liberal politics (e.g., Strike 2010). Furthermore, its arguments often converged with those advanced by feminist exponents of the ethic of care (Noddings 1984; Gilligan 1982). Noddings’ work is particularly notable because she inferred a cogent and radical agenda for the reform of schools from her conception of care (Noddings 1992).

One persistent controversy in citizenship theory has been about whether patriotism is correctly deemed a virtue, given our obligations to those who are not our fellow citizens in an increasingly interdependent world and the sordid history of xenophobia with which modern nation states are associated. The controversy is partly about what we should teach in our schools and is commonly discussed by philosophers in that context (Galston 1991; Ben-Porath 2006; Callan 2006; Miller 2007; Curren & Dorn 2018). The controversy is related to a deeper and more pervasive question about how morally or intellectually taxing the best conception of our citizenship should be. The more taxing it is, the more constraining its derivative conception of civic education will be. Contemporary political philosophers offer divergent arguments about these matters. For example, Gutmann and Thompson claim that citizens of diverse democracies need to “understand the diverse ways of life of their fellow citizens” (Gutmann & Thompson 1996: 66). The need arises from the obligation of reciprocity which they (like Rawls) believe to be integral to citizenship. Because I must seek to cooperate with others politically on terms that make sense from their moral perspective as well as my own, I must be ready to enter that perspective imaginatively so as to grasp its distinctive content. Many such perspectives prosper in liberal democracies, and so the task of reciprocal understanding is necessarily onerous. Still, our actions qua deliberative citizen must be grounded in such reciprocity if political cooperation on terms acceptable to us as (diversely) morally motivated citizens is to be possible at all. This is tantamount to an imperative to think autonomously inside the role of citizen because I cannot close-mindedly resist critical consideration of moral views alien to my own without flouting my responsibilities as a deliberative citizen.

Civic education does not exhaust the domain of moral education, even though the more robust conceptions of equal citizenship have far-reaching implications for just relations in civil society and the family. The study of moral education has traditionally taken its bearings from normative ethics rather than political philosophy, and this is largely true of work undertaken in recent decades. The major development here has been the revival of virtue ethics as an alternative to the deontological and consequentialist theories that dominated discussion for much of the twentieth century.

The defining idea of virtue ethics is that our criterion of moral right and wrong must derive from a conception of how the ideally virtuous agent would distinguish between the two. Virtue ethics is thus an alternative to both consequentialism and deontology which locate the relevant criterion in producing good consequences or meeting the requirements of moral duty respectively. The debate about the comparative merits of these theories is not resolved, but from an educational perspective that may be less important than it has sometimes seemed to antagonists in the debate. To be sure, adjudicating between rival theories in normative ethics might shed light on how best to construe the process of moral education, and philosophical reflection on the process might help us to adjudicate between the theories. There has been extensive work on habituation and virtue, largely inspired by Aristotle (Burnyeat 1980; Peters 1981). But whether this does anything to establish the superiority of virtue ethics over its competitors is far from obvious. Other aspects of moral education—in particular, the paired processes of role-modelling and identification—deserve much more scrutiny than they have received (Audi 2017; Kristjánsson 2015, 2017).

Related to the issues concerning the aims and functions of education and schooling rehearsed above are those involving the specifically epistemic aims of education and attendant issues treated by social and virtue epistemologists. (The papers collected in Kotzee 2013 and Baehr 2016 highlight the current and growing interactions among social epistemologists, virtue epistemologists, and philosophers of education.)

There is, first, a lively debate concerning putative epistemic aims. Alvin Goldman argues that truth (or knowledge understood in the “weak” sense of true belief) is the fundamental epistemic aim of education (Goldman 1999). Others, including the majority of historically significant philosophers of education, hold that critical thinking or rationality and rational belief (or knowledge in the “strong” sense that includes justification) is the basic epistemic educational aim (Bailin & Siegel 2003; Scheffler 1965, 1973 [1989]; Siegel 1988, 1997, 2005, 2017). Catherine Z. Elgin (1999a,b) and Duncan Pritchard (2013, 2016; Carter & Pritchard 2017) have independently urged that understanding is the basic aim. Pritchard’s view combines understanding with intellectual virtue ; Jason Baehr (2011) systematically defends the fostering of the intellectual virtues as the fundamental epistemic aim of education. This cluster of views continues to engender ongoing discussion and debate. (Its complex literature is collected in Carter and Kotzee 2015, summarized in Siegel 2018, and helpfully analyzed in Watson 2016.)

A further controversy concerns the places of testimony and trust in the classroom: In what circumstances if any ought students to trust their teachers’ pronouncements, and why? Here the epistemology of education is informed by social epistemology, specifically the epistemology of testimony; the familiar reductionism/anti-reductionism controversy there is applicable to students and teachers. Anti-reductionists, who regard testimony as a basic source of justification, may with equanimity approve of students’ taking their teachers’ word at face value and believing what they say; reductionists may balk. Does teacher testimony itself constitute good reason for student belief?

The correct answer here seems clearly enough to be “it depends”. For very young children who have yet to acquire or develop the ability to subject teacher declarations to critical scrutiny, there seems to be little alternative to accepting what their teachers tell them. For older and more cognitively sophisticated students there seem to be more options: they can assess them for plausibility, compare them with other opinions, assess the teachers’ proffered reasons, subject them to independent evaluation, etc. Regarding “the teacher says that p ” as itself a good reason to believe it appears moreover to contravene the widely shared conviction that an important educational aim is helping students to become able to evaluate candidate beliefs for themselves and believe accordingly. That said, all sides agree that sometimes believers, including students, have good reasons simply to trust what others tell them. There is thus more work to do here by both social epistemologists and philosophers of education (for further discussion see Goldberg 2013; Siegel 2005, 2018).

A further cluster of questions, of long-standing interest to philosophers of education, concerns indoctrination : How if at all does it differ from legitimate teaching? Is it inevitable, and if so is it not always necessarily bad? First, what is it? As we saw earlier, extant analyses focus on the aims or intentions of the indoctrinator, the methods employed, or the content transmitted. If the indoctrination is successful, all have the result that students/victims either don’t, won’t, or can’t subject the indoctrinated material to proper epistemic evaluation. In this way it produces both belief that is evidentially unsupported or contravened and uncritical dispositions to believe. It might seem obvious that indoctrination, so understood, is educationally undesirable. But it equally seems that very young children, at least, have no alternative but to believe sans evidence; they have yet to acquire the dispositions to seek and evaluate evidence, or the abilities to recognize evidence or evaluate it. Thus we seem driven to the views that indoctrination is both unavoidable and yet bad and to be avoided. It is not obvious how this conundrum is best handled. One option is to distinguish between acceptable and unacceptable indoctrination. Another is to distinguish between indoctrination (which is always bad) and non-indoctrinating belief inculcation, the latter being such that students are taught some things without reasons (the alphabet, the numbers, how to read and count, etc.), but in such a way that critical evaluation of all such material (and everything else) is prized and fostered (Siegel 1988: ch. 5). In the end the distinctions required by the two options might be extensionally equivalent (Siegel 2018).

Education, it is generally granted, fosters belief : in the typical propositional case, Smith teaches Jones that p , and if all goes well Jones learns it and comes to believe it. Education also has the task of fostering open-mindedness and an appreciation of our fallibility : All the theorists mentioned thus far, especially those in the critical thinking and intellectual virtue camps, urge their importance. But these two might seem at odds. If Jones (fully) believes that p , can she also be open-minded about it? Can she believe, for example, that earthquakes are caused by the movements of tectonic plates, while also believing that perhaps they aren’t? This cluster of italicized notions requires careful handling; it is helpfully discussed by Jonathan Adler (2002, 2003), who recommends regarding the latter two as meta-attitudes concerning one’s first-order beliefs rather than lessened degrees of belief or commitments to those beliefs.

Other traditional epistemological worries that impinge upon the epistemology of education concern (a) absolutism , pluralism and relativism with respect to knowledge, truth and justification as these relate to what is taught, (b) the character and status of group epistemologies and the prospects for understanding such epistemic goods “universalistically” in the face of “particularist” challenges, (c) the relation between “knowledge-how” and “knowledge-that” and their respective places in the curriculum, (d) concerns raised by multiculturalism and the inclusion/exclusion of marginalized perspectives in curriculum content and the classroom, and (e) further issues concerning teaching and learning. (There is more here than can be briefly summarized; for more references and systematic treatment cf. Bailin & Siegel 2003; Carter & Kotzee 2015; Cleverley & Phillips 1986; Robertson 2009; Siegel 2004, 2017; and Watson 2016.)

The educational research enterprise has been criticized for a century or more by politicians, policymakers, administrators, curriculum developers, teachers, philosophers of education, and by researchers themselves—but the criticisms have been contradictory. Charges of being “too ivory tower and theory-oriented” are found alongside “too focused on practice and too atheoretical”; but in light of the views of John Dewey and William James that the function of theory is to guide intelligent practice and problem-solving, it is becoming more fashionable to hold that the “theory v. practice” dichotomy is a false one. (For an illuminating account of the historical development of educational research and its tribulations, see Lagemann 2000.)

A similar trend can be discerned with respect to the long warfare between two rival groups of research methods—on one hand quantitative/statistical approaches to research, and on the other hand the qualitative/ethnographic family. (The choice of labels here is not entirely risk-free, for they have been contested; furthermore the first approach is quite often associated with “experimental” studies, and the latter with “case studies”, but this is an over-simplification.) For several decades these two rival methodological camps were treated by researchers and a few philosophers of education as being rival paradigms (Kuhn’s ideas, albeit in a very loose form, have been influential in the field of educational research), and the dispute between them was commonly referred to as “the paradigm wars”. In essence the issue at stake was epistemological: members of the quantitative/experimental camp believed that only their methods could lead to well-warranted knowledge claims, especially about the causal factors at play in educational phenomena, and on the whole they regarded qualitative methods as lacking in rigor; on the other hand the adherents of qualitative/ethnographic approaches held that the other camp was too “positivistic” and was operating with an inadequate view of causation in human affairs—one that ignored the role of motives and reasons, possession of relevant background knowledge, awareness of cultural norms, and the like. Few if any commentators in the “paradigm wars” suggested that there was anything prohibiting the use of both approaches in the one research program—provided that if both were used, they were used only sequentially or in parallel, for they were underwritten by different epistemologies and hence could not be blended together. But recently the trend has been towards rapprochement, towards the view that the two methodological families are, in fact, compatible and are not at all like paradigms in the Kuhnian sense(s) of the term; the melding of the two approaches is often called “mixed methods research”, and it is growing in popularity. (For more detailed discussion of these “wars” see Howe 2003 and Phillips 2009.)

The most lively contemporary debates about education research, however, were set in motion around the turn of the millennium when the US Federal Government moved in the direction of funding only rigorously scientific educational research—the kind that could establish causal factors which could then guide the development of practically effective policies. (It was held that such a causal knowledge base was available for medical decision-making.) The definition of “rigorously scientific”, however, was decided by politicians and not by the research community, and it was given in terms of the use of a specific research method—the net effect being that the only research projects to receive Federal funding were those that carried out randomized controlled experiments or field trials (RFTs). It has become common over the last decade to refer to the RFT as the “gold standard” methodology.

The National Research Council (NRC)—an arm of the US National Academies of Science—issued a report, influenced by postpostivistic philosophy of science (NRC 2002), that argued that this criterion was far too narrow. Numerous essays have appeared subsequently that point out how the “gold standard” account of scientific rigor distorts the history of science, how the complex nature of the relation between evidence and policy-making has been distorted and made to appear overly simple (for instance the role of value-judgments in linking empirical findings to policy directives is often overlooked), and qualitative researchers have insisted upon the scientific nature of their work. Nevertheless, and possibly because it tried to be balanced and supported the use of RFTs in some research contexts, the NRC report has been the subject of symposia in four journals, where it has been supported by a few and attacked from a variety of philosophical fronts: Its authors were positivists, they erroneously believed that educational inquiry could be value neutral and that it could ignore the ways in which the exercise of power constrains the research process, they misunderstood the nature of educational phenomena, and so on. This cluster of issues continues to be debated by educational researchers and by philosophers of education and of science, and often involves basic topics in philosophy of science: the constitution of warranting evidence, the nature of theories and of confirmation and explanation, etc. Nancy Cartwright’s important recent work on causation, evidence, and evidence-based policy adds layers of both philosophical sophistication and real world practical analysis to the central issues just discussed (Cartwright & Hardie 2012, Cartwright 2013; cf. Kvernbekk 2015 for an overview of the controversies regarding evidence in the education and philosophy of education literatures).

As stressed earlier, it is impossible to do justice to the whole field of philosophy of education in a single encyclopedia entry. Different countries around the world have their own intellectual traditions and their own ways of institutionalizing philosophy of education in the academic universe, and no discussion of any of this appears in the present essay. But even in the Anglo-American world there is such a diversity of approaches that any author attempting to produce a synoptic account will quickly run into the borders of his or her competence. Clearly this has happened in the present case.

Fortunately, in the last thirty years or so resources have become available that significantly alleviate these problems. There has been a flood of encyclopedia entries, both on the field as a whole and also on many specific topics not well-covered in the present essay (see, as a sample, Burbules 1994; Chambliss 1996b; Curren 1998, 2018; Phillips 1985, 2010; Siegel 2007; Smeyers 1994), two “Encyclopedias” (Chambliss 1996a; Phillips 2014), a “Guide” (Blake, Smeyers, Smith, & Standish 2003), a “Companion” (Curren 2003), two “Handbooks” (Siegel 2009; Bailey, Barrow, Carr, & McCarthy 2010), a comprehensive anthology (Curren 2007), a dictionary of key concepts in the field (Winch & Gingell 1999), and a good textbook or two (Carr 2003; Noddings 2015). In addition there are numerous volumes both of reprinted selections and of specially commissioned essays on specific topics, some of which were given short shrift here (for another sampling see A. Rorty 1998, Stone 1994), and several international journals, including Theory and Research in Education , Journal of Philosophy of Education , Educational Theory , Studies in Philosophy and Education , and Educational Philosophy and Theory . Thus there is more than enough material available to keep the interested reader busy.

Adler, Jonathan E., 2002, Belief’s Own Ethics , Cambridge, MA: MIT Press.
–––, 2003, “Knowledge, Truth and Learning”, in Curren 2003: 285–304. doi:10.1002/9780470996454.ch21
Anderson, Elizabeth, 2007, “Fair Opportunity in Education: A Democratic Equality Perspective”, Ethics , 117(4): 595–622. doi:10.1086/518806
Archambault, Reginald D. (ed.), 1965, Philosophical Analysis and Education , London: Routledge.
Audi, Robert, 2017, “Role Modelling and Reasons: Developmental and Normative Grounds of Moral Virtue”, Journal of Moral Philosophy , 14(6): 646–668. doi:10.1163/17455243-46810063
Baehr, Jason, 2011, The Inquiring Mind: On Intellectual Virtues and Virtue Epistemology , Oxford: Oxford University Press. doi:10.1093/acprof:oso/9780199604074.001.0001
––– (ed.), 2016, Intellectual Virtues and Education: Essays in Applied Virtue Epistemology , New York: Routledge.
Bailey, Richard, Robin Barrow, David Carr, and Christine McCarthy (eds), 2010, The SAGE Handbook of the Philosophy of Education , Los Angeles: Sage. doi:10.4135/9781446200872
Bailin, Sharon and Harvey Siegel, 2003, “Critical Thinking”, in Blake et al. 2003: 181–193. doi:10.1002/9780470996294.ch11
Ben-Porath, Sigal R., 2006. Citizenship Under Fire: Democratic Education in Times of Conflict , Princeton, NJ: Princeton University Press.
Blake, Nigel, Paul Smeyers, Richard Smith, and Paul Standish (eds.), 2003, The Blackwell Guide to the Philosophy of Education , Oxford: Blackwell. doi:10.1002/9780470996294
Brighouse, Harry, 2005, On Education , London: Routledge.
–––, 2009, “Moral and Political Aims of Education”, in Siegel 2009: 35–51.
Brighouse, Harry and Adam Swift, 2009, “Educational Equality versus Educational Adequacy: A Critique of Anderson and Satz”, Journal of Applied Philosophy , 26(2): 117–128. doi:10.1111/j.1468-5930.2009.00438.x
Bull, Barry L., 2008, Social Justice in Education: An Introduction , New York: Palgrave MacMillan.
Burbules, Nicholas C., 1994, “Marxism and Educational Thought”, in The International Encyclopedia of Education , (Volume 6), Torsten Husén and T. Neville Postlethwaite (eds.), Oxford: Pergamon, second edition, pp. 3617–22.
Burnyeat, Myles F., 1980, “Aristotle on Learning to be Good”, in Amélie Oksenberg Rorty (ed.), Essays on Aristotle’s Ethics , Berkeley CA: University of California Press, pp. 69–92.
Callan, Eamonn, 1997, Creating Citizens: Political Education and Liberal Democracy , Oxford: Clarendon Press. doi:10.1093/0198292589.001.0001
–––, 2006, “Love, Idolatry, and Patriotism”, Social Theory and Practice , 32(4): 525–546. doi:10.5840/soctheorpract200632430
Carr, David, 2003, Making Sense of Education: An Introduction to the Philosophy and Theory of Education and Teaching , London: RoutledgeFalmer.
Carter, J. Adam and Ben Kotzee, 2015, “Epistemology of Education”, Oxford Bibliographies Online , last modified: 26 October 2015.
Carter, J.Adam and Duncan Pritchard, 2017, “Epistemic Situationism, Epistemic Dependence, and the Epistemology of Education”, in Abrol Fairweather and Mark Alfano (eds.), Epistemic Situationism , Oxford: Oxford University Press, pp. 168–191. doi:10.1093/oso/9780199688234.003.0010
Cartwright, Nancy D., 2013, Evidence: For Policy and Wheresoever Rigor Is a Must , London: London School of Economics and Political Science.
Cartwright, Nancy D. and Jeremy Hardie, 2012, Evidence-based Policy: A Practical Guide to Doing It Better , Oxford: Oxford University Press.
Chambliss, J.J. (ed.), 1996a, Philosophy of Education: An Encyclopedia , New York: Garland.
Chambliss, J.J., 1996b, “History of Philosophy of Education”, in Chambliss 1996a, pp. 461–472.
Clayton, Matthew, 2006, Justice and Legitimacy in Upbringing , Oxford: Oxford University Press. doi:10.1093/0199268940.001.0001
Cleverley, John and D.C. Phillips, 1986, Visions of Childhood: Influential Models from Locke to Spock , New York: Teachers College Press.
Curren, Randall R., 1998, “Education, Philosophy of”, in E.J. Craig (ed.), Routledge Encyclopedia of Philosophy , vol. 3, pp. 231–240.
–––, 2000, Aristotle on the Necessity of Public Education , Lanham, MD: Rowman & Littlefield.
–––, (ed.), 2003, A Companion to the Philosophy of Education , Oxford: Blackwell. doi:10.1002/9780470996454
–––, (ed.), 2007, Philosophy of Education: An Anthology , Oxford: Blackwell.
–––, 2013, “A Neo-Aristotelian Account of Education, Justice and the Human Good”, Theory and Research in Education , 11(3): 231–249. doi:10.1177/1477878513498182
–––, 2018, “Education, History of Philosophy of”, revised second version, in Routledge Encyclopedia of Philosophy Online . doi:10.4324/9780415249126-N014-2
Curren, Randall, Emily Robertson, and Paul Hager, 2003, “The Analytical Movement”, in Curren 2003: 176–191. doi:10.1002/9780470996454.ch13
Curren, Randall and Charles Dorn, 2018, Patriotic Education in a Global Age , Chicago: University of Chicago Press.
Dworkin, Ronald, 1977, Taking Rights Seriously , Cambridge, MA: Harvard University Press.
Elgin, Catherine Z., 1999a, “Epistemology’s Ends, Pedagogy’s Prospects”, Facta Philosophica , 1: 39–54
–––, 1999b, “Education and the Advancement of Understanding”, in David M. Steiner (ed.), Proceedings of the 20 th World Congress of Philosophy , vol. 3, Philosophy Documentation Center, pp. 131–140.
Galston, William A., 1991, Liberal Purposes: Goods, Virtues, and Diversity in the Liberal State , Cambridge: Cambridge University Press. doi:10.1017/CBO9781139172462
Gellner, Ernest, 1959, Words and Things: A Critical Account of Linguistic Philosophy and a Study in Ideology , London: Gollancz.
Gilligan, Carol, 1982, In a Different Voice: Psychological Theory and Women’s Development , Cambridge, MA: Harvard University Press.
Goldberg, Sanford, 2013, “Epistemic Dependence in Testimonial Belief, in the Classroom and Beyond”, Journal of Philosophy of Education , 47(2): 168–186. doi:10.1111/1467-9752.12019
Goldman, Alvin I., 1999, Knowledge in a Social World , Oxford: Oxford University Press. doi:10.1093/0198238207.001.0001
Greene, Maxine, 1988, The Dialectic of Freedom , New York: Teachers College Press.
Gutmann, Amy and Dennis F. Thompson, 1996, Democracy and Disagreement , Cambridge, MA: Belknap Press of Harvard University Press.
Hand, Michael, 2006, “Against Autonomy as an Educational Aim”, Oxford Review of Education , 32(4): 535–550. doi:10.1080/03054980600884250
Hardie, Charles Dunn, 1941 [1962], Truth and Fallacy in Educational Theory , New York: Teachers College Bureau of Publications.
Hirst, Paul, 1965, “Liberal Education and the Nature of Knowledge”, in Philosophical Analysis and Education , Reginald D. Archambault, (ed.), London: Routledge, pp. 113–138.
Hirst, Paul and R.S. Peters, 1970, The Logic of Education , London: Routledge.
Hollis, Martin, 1982, “Education as A Positional Good”, Journal of Philosophy of Education , 16(2): 235–244. doi:10.1111/j.1467-9752.1982.tb00615.x
Howe, Kenneth R., 2003, Closing Methodological Divides: Toward Democratic Educational Research , Dordrecht: Kluwer. doi:10.1007/0-306-47984-2
Jacobs, Lesley A., 2010, “Equality, Adequacy, And Stakes Fairness: Retrieving the Equal Opportunities in Education Approach”, Theory and Research in Education , 8(3): 249–268. doi:10.1177/1477878510381627
Kotzee, Ben (ed.), 2013, Education and the Growth of Knowledge: Perspectives from Social and Virtue Epistemology , Oxford: Wiley. doi:10.1002/9781118721254
Kristjánsson, Kristján, 2015, Aristotelian Character Education , London: Routledge.
–––, 2017, “Emotions Targeting Moral Exemplarity: Making Sense of the Logical Geography of Admiration, Emulation and Elevation”, Theory and Research in Education , 15(1): 20–37. doi:10.1177/1477878517695679
Kvernbekk, Tone, 2015, Evidence-based Practice in Education: Functions of Evidence and Causal Presuppositions , London: Routledge.
Lagemann, Ellen Condliffe, 2000, An Elusive Science: The Troubling History of Educational Research , Chicago: University of Chicago Press.
Locke, J., 1693, Some Thoughts Concerning Education , London: Black Swan in Paternoster Row.
Lucas, Christopher J. (ed.), 1969, What is Philosophy of Education? , London: Macmillan.
Lyotard, J-F., 1984, The Postmodern Condition: A Report on Knowledge , Minneapolis: University of Minnesota Press.
MacIntyre, Alasdair, 1984, After Virtue: A Study in Moral Theory , second edition, Notre Dame, IN: University of Notre Dame Press.
Martin, Jane Roland, 1985, Reclaiming a Conversation: The Ideal of the Educated Woman , New Haven, CT: Yale University Press.
Mehta, Ved, 1963, Fly and the Fly-Bottle: Encounters with British Intellectuals , London: Weidenfeld and Nicolson.
Miller, Richard W., 2007, “Unlearning American Patriotism”, Theory and Research in Education , 5(1): 7–21. doi:10.1177/1477878507073602
National Research Council (NRC), 2002, Scientific Research in Education , Washington, DC: National Academies Press. [ NRC 2002 available online ]
Noddings, Nel, 1984, Caring: A Feminine Approach to Ethics and Moral Education , Berkeley: University of California Press.
–––, 1992, The Challenge to Care in Schools: An Alternative Approach to Education , New York: Teachers College Press.
–––, 2015, Philosophy of Education , fourth edition, Boulder, CO: Westview.
O’Connor, D.J., 1957, An Introduction to Philosophy of Education , London: Routledge.
Park, J., (ed.), 1965, Bertrand Russell on Education , London: Allen and Unwin.
Peters, R.S., (ed.), 1973, The Philosophy of Education , Oxford: Oxford University Press.
–––, 1981, Moral Development and Moral Education , London: G. Allen & Unwin.
Phillips, D.C., 1985, “Philosophy of Education”, in International Encyclopedia of Education , Torsten Husén and T. Neville Postlethwaite, (eds.), pp. 3859–3877.
–––, 1987, Philosophy, Science, and Social Inquiry: Contemporary Methodological Controversies in Social Science and Related Applied Fields of Research , Oxford: Pergamon.
–––, 2009, “Empirical Educational Research: Charting Philosophical Disagreements in an Undisciplined Field”, in Siegel 2009: 381–406.
–––, 2010, “What Is Philosophy of Education?”, in Bailey et al. 2010: 3–19. doi:10.4135/9781446200872.n1
–––, (ed.), 2014, Encyclopedia of Educational Theory and Philosophy , Los Angeles: Sage.
Pritchard, Duncan, 2013, “Epistemic Virtue and the Epistemology of Education”, Journal of Philosophy of Education , 47(2): 236–247. doi:10.1111/1467-9752.12022
–––, 2016, “Intellectual Virtue, Extended Cognition, and the Epistemology of Education”, in Baehr 2016: 113–127.
Rawls, John, 1971, A Theory of Justice , Cambridge MA: Harvard University Press.
–––, 1993, Political Liberalism , New York: Columbia University Press.
Robertson, Emily, 2009, “The Epistemic Aims of Education”, in Siegel 2009: 11–34.
Rorty, Amélie Oksenberg (ed.), 1998, Philosophers on Education: New Historical Perspectives , New York: Routledge.
Satz, Debra, 2007, “Equality, Adequacy, and Education for Citizenship”, Ethics , 117(4): 623–648. doi:10.1086/518805
Scheffler, Israel, 1960, The Language of Education , Springfield, IL: Thomas.
–––, 1965, Conditions of Knowledge: An Introduction to Epistemology and Education , Chicago: Scott, Foresman.
–––, 1973 [1989], Reason and Teaching , Indianapolis, IN: Hackett.
Schouten, Gina, 2012, “Fair Educational Opportunity and the Distribution of Natural Ability: Toward a Prioritarian Principle of Educational Justice”, Journal of Philosophy of Education , 46(3): 472–491. doi:10.1111/j.1467-9752.2012.00863.x
Scriven, Michael, 1991a, “Beyond Formative and Summative Evaluation”, in Milbrey McLaughlin and D.C. Phillips (eds.), Evaluation and Education: At Quarter Century , Chicago: University of Chicago Press/NSSE, pp. 19–64.
–––, 1991b, Evaluation Thesaurus , Thousand Oaks, CA: Sage.
Siegel, Harvey, 1988, Educating Reason: Rationality, Critical Thinking, and Education , New York: Routledge.
–––, 1997, Rationality Redeemed?: Further Dialogues on an Educational Ideal , New York: Routledge.
–––, 2004, “Epistemology and Education: An Incomplete Guide to the Social-Epistemological Issues”, Episteme , 1(2): 129–137. doi:10.3366/epi.2004.1.2.129
–––, 2005, “Truth, Thinking, Testimony and Trust: Alvin Goldman on Epistemology and Education”, Philosophy and Phenomenological Research , 71(2): 345–366. doi:10.1111/j.1933-1592.2005.tb00452.x
–––, 2007, “Philosophy of Education”, in Britannica Online Encyclopedia , last modified 2 February 2018. URL = <https://academic.eb.com/levels/collegiate/article/philosophy-of-education/108550>
–––, (ed.), 2009, The Oxford Handbook of Philosophy of Education , New York: Oxford University Press. doi:10.1093/oxfordhb/9780195312881.001.0001
–––, 2016, “Israel Scheffler”, In J. A Palmer (ed.), Routledge Encyclopaedia of Educational Thinkers , London: Routledge, pp. 428–432.
–––, 2017, Education’s Epistemology: Rationality, Diversity, and Critical Thinking , New York: Oxford University Press.
–––, 2018, “The Epistemology of Education”, Routledge Encyclopedia of Philosophy Online , doi:10.4324/0123456789-P074-1.
Skinner, B.F., 1948 [1962], Walden Two , New York: Macmillan.
–––, 1972, Beyond Freedom and Dignity , London: Jonathan Cape.
Smeyers, Paulus, 1994, “Philosophy of Education: Western European Perspectives”, in The International Encyclopedia of Education , (Volume 8), Torsten Husén and T. Neville Postlethwaite, (eds.), Oxford: Pergamon, second Edition, pp. 4456–61.
Smith, B. Othanel and Robert H. Ennis (eds.), 1961, Language and Concepts in Education , Chicago: Rand McNally.
Snook, I.A., 1972, Indoctrination and Education , London: Routledge & Kegan Paul.
Stone, Lynda (ed.), 1994, The Education Feminism Reader , New York: Routledge.
Strike, Kenneth A., 2010, Small Schools and Strong Communities: A Third Way of School Reform , New York: Teachers College Press.
Warnick, Bryan R., 2015, “Taming the Conflict over Educational Equality”, Journal of Applied Philosophy , 32(1): 50–66. doi:10.1111/japp.12066
Watson, Lani, 2016, “The Epistemology of Education”, Philosophy Compass , 11(3): 146–159. doi:10.1111/phc3.12316
Winch, Christopher and John Gingell, 1999, Key Concepts in the Philosophy of Education , London: Routledge.

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The authors and editors would like to thank Randall Curren for sending a number of constructive suggestions for the Summer 2018 update of this entry.

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William Whewell's work, especially the two volumes of Philosophy of the Inductive Sciences of 1840, is a noteworthy and, ... Computational theories of scientific discoveries have helped identify and clarify a number of problem-solving strategies. An example of such a strategy is heuristic means-ends analysis, which involves identifying ...
Problem Solving
Abstract. This chapter follows the historical development of research on problem solving. It begins with a description of two research traditions that addressed different aspects of the problem-solving process: (1) research on problem representation (the Gestalt legacy) that examined how people understand the problem at hand, and (2) research on search in a problem space (the legacy of Newell ...
Pragmatism, Problem Solving, and Strategies for Engaged Philosophy
Abstract. Philosophical pragmatism provides a theory and practical guidance for engaged philosophy. The movement to apply philosophy to real-world problems gained traction in the 1970s and has become an important area of philosophical inquiry. Applied philosophy draws connections between philosophical principles and real-life problems.
PDF 8 Problem-Solving Philosophy
Problem-Solving Philosophy 109 called 'elegant in its simplicity' is dangerous. Although it is self-evident that any method which is simple and effective is better than one which is complicated and effective, this only safely holds when the simplicity is crude rather than elegant. For example, the method of differences is a
List of philosophical problems
The hard problem of consciousness is the question of what consciousness is and why we have consciousness as opposed to being philosophical zombies. The adjective "hard" is to contrast with the "easy" consciousness problems, which seek to explain the mechanisms of consciousness ("why" versus "how", or final cause versus efficient cause ).
Philosophy: A brief guide for undergraduates
General Problem Solving. The study of philosophy enhances, in a way no other activity does, one's problem-solving capacities. It helps students to analyze concepts, definitions, arguments, and problems. ... For others, particularly but not exclusively those planning post-graduate study, here are some examples of valuable courses beyond general ...
Pragmatism as Problem Solving
In this way, to the rationalist tradition, in which the goal is to understand society, pragmatism as problem-solving brings direction and may help resolve the "incoherency problem" and in doing so may help produce better theories of society. To the emancipatory tradition, which seeks to improve society, the pragmatism as problem-solving ...
101 Philosophy Problems
"The book has 101 humorous little stories, each with a philosophical problem (not however, necessarily, the usual Unsolved problems in philosophy). For example, problem 54 is about Mr Megasoft, who dies leaving his fortune to his favourite computer. Megasoft's children take the matter to court, contending that the computer cannot think and so ...
Problem Solving
Cognitive—Problem solving occurs within the problem solver's cognitive system and can only be inferred indirectly from the problem solver's behavior (including biological changes, introspections, and actions during problem solving).. Process—Problem solving involves mental computations in which some operation is applied to a mental representation, sometimes resulting in the creation of ...
The art of complex problem-solving
Nov 26, 2018. 3. Complex problem-solving is difficult. For a start it involves thinking and thinking for problem-solving in itself can be hard. Wrap it up in the context of a complex problem with ...
Moral Dilemmas
Examples as such cannot establish the reality of dilemmas. Surely most will acknowledge that there are situations in which an agent does not know what he ought to do. ... The Problem of Dirty Hands," Philosophy and Public Affairs, 2: 160-180. Williams, Bernard, 1965, "Ethical Consistency," Proceedings of the Aristotelian Society ...
Epistemology for interdisciplinary research
In science policy, it is generally acknowledged that science-based problem-solving requires interdisciplinary research. For example, policy makers invest in funding programs such as Horizon 2020 that aim to stimulate interdisciplinary research. Yet the epistemological processes that lead to effective interdisciplinary research are poorly understood. This article aims at an epistemology for ...
Problem solving
The former is an example of simple problem solving (SPS) addressing one issue, whereas the latter is complex problem solving (CPS) with multiple interrelated obstacles. ... computer science, philosophy, and social organization. The mental techniques to identify, analyze, and solve problems are studied in psychology and cognitive sciences.
The Fundamental Problem of Philosophy: Its Point
1. Why Philosophy Fails in the Fundamental Respect of Having An Intrinsic Point. In The Myth of Sisyfos Albert Camus straightaway claims that suicide is the fundamental problem of philosophy, perhaps even the only really serious philosophical problem. He suggests that the importance of a problem is determined by what actions the problem — or, I suppose, rather an answer to it — commits you to.
Embracing the obligation to dissent
Events of the past several years have reiterated for executives the importance of collaboration and of welcoming diverse perspectives when trying to solve complicated workplace problems. Companies weren't fully prepared for the onset of a global pandemic, for instance, and all that it engendered—including supply chain snarls and the resulting Great Attrition and shift to remote (and now ...
Analogy and Analogical Reasoning
An analogy is a comparison between two objects, or systems of objects, that highlights respects in which they are thought to be similar.Analogical reasoning is any type of thinking that relies upon an analogy. An analogical argument is an explicit representation of a form of analogical reasoning that cites accepted similarities between two systems to support the conclusion that some further ...
Data Science skills 101: How to solve any problem
The same but different. Source: author. Creating a separate but related problem can be a very effective technique in problem solving. It is particularly relevant where you have expertise/resources/skills in a particular area and want to exploit this.
PROBLEM OF THE DAY : 10/05/2024
Welcome to the daily solving of our PROBLEM OF THE DAY with Yash Dwivedi.We will discuss the entire problem step-by-step and work towards developing an optimized solution. This will not only help you brush up on your concepts of Recursion but also build up problem-solving skills. In this problem, we are given an array of integers arr, the length of the array n, and an integer k, find all the ...
Philosophy of Education
Philosophy of education is the branch of applied or practical philosophy concerned with the nature and aims of education and the philosophical problems arising from educational theory and practice. Because that practice is ubiquitous in and across human societies, its social and individual manifestations so varied, and its influence so profound ...
PROBLEM OF THE DAY : 17/05/2024
In this problem, we are given an array arr [] of size n and an integer x, return 1 if there exists a pair of elements in the array whose absolute difference is x, otherwise, return -1. Example : Pair (2, 80) have absolute difference of 78. Give the problem a try before going through the video. All the best!!! A Computer Science portal for geeks.

How to solve real-world problems by thinking philosophically

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Philosophical Problems

Approaching the Problems

Types of Philosophical Problems

Why Is Philosophy Important?

Origin of Philosophical Problems

Controversies in Philosophy

Why Do Philosophical Problems Persist?

Philosophy in Everyday Life

Related Topics

48 Problem Solving

Definition of Problem Solving

Definition of Problem

Types of Problems

Cognitive Processes in Problem Solving

Knowledge for Problem Solving

Historical Approaches to Problem Solving

Early Conceptions

Associationism

Gestalt Psychology

Information Processing

Classic Issues in Problem Solving

Current and Future Issues in Problem Solving

Decision Making

Intelligence and Creativity

Teaching of Thinking Skills

Expert Problem Solving

Analogical Reasoning

Mathematical and Scientific Problem Solving

Everyday Thinking

Cognitive Neuroscience of Problem Solving

Future Directions

Further Reading

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Epistemology for interdisciplinary research – shifting philosophical paradigms of science

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1 Introduction

1.2 Structure

1.3 Terminology

2 Studies of interdisciplinary research

2.2 Methods for organizing interdisciplinary research

2.3 Metaphors of integration: Jigsaw-puzzle, conflict-resolution, and engineering-design

3 Studies of teaching and learning interdisciplinary research

3.2 Epistemological views conveyed in science teaching

4 Philosophical view of science

4.2 Two philosophical paradigms of science: A physics versus an engineering paradigm of science

4.3 The physics paradigm as a cause of epistemological difficulties of interdisciplinary research?

5 Metacognitive scaffolds

5.2 Frameworks: (disciplinary) matrices and metacognitive scaffolds as epistemic tools

5.3 Interdisciplinary research

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