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Rationality
Rationality
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Rationality is the quality of being guided by or based on reason. In this regard, a person acts rationally if they have a good reason for what they do, or a belief is rational if it is based on strong evidence. This quality can apply to an ability, as in a rational animal, to a psychological process, like reasoning, to mental states, such as beliefs and intentions, or to persons who possess these other forms of rationality. A thing that lacks rationality is either arational, if it is outside the domain of rational evaluation, or irrational, if it belongs to this domain but does not fulfill its standards.

There are many discussions about the essential features shared by all forms, or accounts, of rationality. According to reason-responsiveness accounts, to be rational is to be responsive to reasons. For example, dark clouds are a reason for taking an umbrella, which is why it is rational for an agent to do so in response. An important rival to this approach are coherence-based accounts, which define rationality as internal coherence among the agent's mental states. Many rules of coherence have been suggested in this regard, for example, that one should not hold contradictory beliefs or that one should intend to do something if one believes that one should do it.

Goal-based accounts characterize rationality in relation to goals, such as acquiring truth in the case of theoretical rationality. Internalists believe that rationality depends only on the person's mind. Externalists contend that external factors may also be relevant. Debates about the normativity of rationality concern the question of whether one should always be rational. A further discussion is whether rationality requires that all beliefs be reviewed from scratch rather than trusting pre-existing beliefs.

Various types of rationality are discussed in the academic literature. The most influential distinction is between theoretical and practical rationality. Theoretical rationality concerns the rationality of beliefs. Rational beliefs are based on evidence that supports them. Practical rationality pertains primarily to actions. This includes certain mental states and events preceding actions, like intentions and decisions. In some cases, the two can conflict, as when practical rationality requires that one adopts an irrational belief. Another distinction is between ideal rationality, which demands that rational agents obey all the laws and implications of logic, and bounded rationality, which takes into account that this is not always possible since the computational power of the human mind is too limited. Most academic discussions focus on the rationality of individuals. This contrasts with social or collective rationality, which pertains to collectives and their group beliefs and decisions.

Rationality is important for solving all kinds of problems in order to efficiently reach one's goal. It is relevant to and discussed in many disciplines. In ethics, one question is whether one can be rational without being moral at the same time. Psychology is interested in how psychological processes implement rationality. This also includes the study of failures to do so, as in the case of cognitive biases. Cognitive and behavioral sciences usually assume that people are rational enough to predict how they think and act. Logic studies the laws of correct arguments. These laws are highly relevant to the rationality of beliefs. A very influential conception of practical rationality is given in decision theory, which states that a decision is rational if the chosen option has the highest expected utility. Other relevant fields include game theory, Bayesianism, economics, and artificial intelligence.

Definition and semantic field

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In its most common sense, rationality is the quality of being guided by reasons or being reasonable.[1][2][3] For example, a person who acts rationally has good reasons for what they do. This usually implies that they reflected on the possible consequences of their action and the goal it is supposed to realize. In the case of beliefs, it is rational to believe something if the agent has good evidence for it and it is coherent with the agent's other beliefs.[4][5] While actions and beliefs are the most paradigmatic forms of rationality, the term is used both in ordinary language and in many academic disciplines to describe a wide variety of things, such as persons, desires, intentions, decisions, policies, and institutions.[6][7] Because of this variety in different contexts, it has proven difficult to give a unified definition covering all these fields and usages. In this regard, different fields often focus their investigation on one specific conception, type, or aspect of rationality without trying to cover it in its most general sense.[8]

These different forms of rationality are sometimes divided into abilities, processes, mental states, and persons.[6][2][1][8][9] For example, when it is claimed that humans are rational animals, this usually refers to the ability to think and act in reasonable ways. It does not imply that all humans are rational all the time: this ability is exercised in some cases but not in others.[6][8][9] On the other hand, the term can also refer to the process of reasoning that results from exercising this ability. Often many additional activities of the higher cognitive faculties are included as well, such as acquiring concepts, judging, deliberating, planning, and deciding as well as the formation of desires and intentions. These processes usually affect some kind of change in the thinker's mental states. In this regard, one can also talk of the rationality of mental states, like beliefs and intentions.[6] A person who possesses these forms of rationality to a sufficiently high degree may themselves be called rational.[1] In some cases, also non-mental results of rational processes may qualify as rational. For example, the arrangement of products in a supermarket can be rational if it is based on a rational plan.[6][2]

The term "rational" has two opposites: irrational and arational. Arational things are outside the domain of rational evaluation, like digestive processes or the weather. Things within the domain of rationality are either rational or irrational depending on whether they fulfill the standards of rationality.[10][7] For example, beliefs, actions, or general policies are rational if there is a good reason for them and irrational otherwise. It is not clear in all cases what belongs to the domain of rational assessment. For example, there are disagreements about whether desires and emotions can be evaluated as rational and irrational rather than arational.[6] The term "irrational" is sometimes used in a wide sense to include cases of arationality.[11]

The meaning of the terms "rational" and "irrational" in academic discourse often differs from how they are used in everyday language. Examples of behaviors considered irrational in ordinary discourse are giving into temptations, going out late even though one has to get up early in the morning, smoking despite being aware of the health risks, or believing in astrology.[12][13] In the academic discourse, on the other hand, rationality is usually identified with being guided by reasons or following norms of internal coherence. Some of the earlier examples may qualify as rational in the academic sense depending on the circumstances. Examples of irrationality in this sense include cognitive biases and violating the laws of probability theory when assessing the likelihood of future events.[12] This article focuses mainly on irrationality in the academic sense.

The terms "rationality", "reason", and "reasoning" are frequently used as synonyms. But in technical contexts, their meanings are often distinguished.[7][12][1] Reason is usually understood as the faculty responsible for the process of reasoning.[7][14] This process aims at improving mental states. Reasoning tries to ensure that the norms of rationality obtain. It differs from rationality nonetheless since other psychological processes besides reasoning may have the same effect.[7] Rationality derives etymologically from the Latin term rationalitas.[6]

Disputes about the concept of rationality

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There are many disputes about the essential characteristics of rationality. It is often understood in relational terms: something, like a belief or an intention, is rational because of how it is related to something else.[6][1] But there are disagreements as to what it has to be related to and in what way. For reason-based accounts, the relation to a reason that justifies or explains the rational state is central. For coherence-based accounts, the relation of coherence between mental states matters. There is a lively discussion in the contemporary literature on whether reason-based accounts or coherence-based accounts are superior.[15][5] Some theorists also try to understand rationality in relation to the goals it tries to realize.[1][16]

Other disputes in this field concern whether rationality depends only on the agent's mind or also on external factors, whether rationality requires a review of all one's beliefs from scratch, and whether we should always be rational.[6][1][12]

Based on reason-responsiveness

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A common idea of many theories of rationality is that it can be defined in terms of reasons. In this view, to be rational means to respond correctly to reasons.[2][1][15] For example, the fact that a food is healthy is a reason to eat it. So this reason makes it rational for the agent to eat the food.[15] An important aspect of this interpretation is that it is not sufficient to merely act accidentally in accordance with reasons. Instead, responding to reasons implies that one acts intentionally because of these reasons.[2]

Some theorists understand reasons as external facts. This view has been criticized based on the claim that, in order to respond to reasons, people have to be aware of them, i.e. they have some form of epistemic access.[15][5] But lacking this access is not automatically irrational. In one example by John Broome, the agent eats a fish contaminated with salmonella, which is a strong reason against eating the fish. But since the agent could not have known this fact, eating the fish is rational for them.[17][18] Because of such problems, many theorists have opted for an internalist version of this account. This means that the agent does not need to respond to reasons in general, but only to reasons they have or possess.[2][15][5][19] The success of such approaches depends a lot on what it means to have a reason and there are various disagreements on this issue.[7][15] A common approach is to hold that this access is given through the possession of evidence in the form of cognitive mental states, like perceptions and knowledge. A similar version states that "rationality consists in responding correctly to beliefs about reasons". So it is rational to bring an umbrella if the agent has strong evidence that it is going to rain. But without this evidence, it would be rational to leave the umbrella at home, even if, unbeknownst to the agent, it is going to rain.[2][19] These versions avoid the previous objection since rationality no longer requires the agent to respond to external factors of which they could not have been aware.[2]

A problem faced by all forms of reason-responsiveness theories is that there are usually many reasons relevant and some of them may conflict with each other. So while salmonella contamination is a reason against eating the fish, its good taste and the desire not to offend the host are reasons in favor of eating it. This problem is usually approached by weighing all the different reasons. This way, one does not respond directly to each reason individually but instead to their weighted sum. Cases of conflict are thus solved since one side usually outweighs the other. So despite the reasons cited in favor of eating the fish, the balance of reasons stands against it, since avoiding a salmonella infection is a much weightier reason than the other reasons cited.[17][18] This can be expressed by stating that rational agents pick the option favored by the balance of reasons.[7][20]

However, other objections to the reason-responsiveness account are not so easily solved. They often focus on cases where reasons require the agent to be irrational, leading to a rational dilemma. For example, if terrorists threaten to blow up a city unless the agent forms an irrational belief, this is a very weighty reason to do all in one's power to violate the norms of rationality.[2][21]

Based on rules of coherence

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An influential rival to the reason-responsiveness account understands rationality as internal coherence.[15][5] On this view, a person is rational to the extent that their mental states and actions are coherent with each other.[15][5] Diverse versions of this approach exist that differ in how they understand coherence and what rules of coherence they propose.[7][20][2] A general distinction in this regard is between negative and positive coherence.[12][22] Negative coherence is an uncontroversial aspect of most such theories: it requires the absence of contradictions and inconsistencies. This means that the agent's mental states do not clash with each other. In some cases, inconsistencies are rather obvious, as when a person believes that it will rain tomorrow and that it will not rain tomorrow. In complex cases, inconsistencies may be difficult to detect, for example, when a person believes in the axioms of Euclidean geometry and is nonetheless convinced that it is possible to square the circle. Positive coherence refers to the support that different mental states provide for each other. For example, there is positive coherence between the belief that there are eight planets in the Solar System and the belief that there are less than ten planets in the Solar System: the earlier belief implies the latter belief. Other types of support through positive coherence include explanatory and causal connections.[12][22]

Coherence-based accounts are also referred to as rule-based accounts since the different aspects of coherence are often expressed in precise rules. In this regard, to be rational means to follow the rules of rationality in thought and action. According to the enkratic rule, for example, rational agents are required to intend what they believe they ought to do. This requires coherence between beliefs and intentions. The norm of persistence states that agents should retain their intentions over time. This way, earlier mental states cohere with later ones.[15][12][5] It is also possible to distinguish different types of rationality, such as theoretical or practical rationality, based on the different sets of rules they require.[7][20]

One problem with such coherence-based accounts of rationality is that the norms can enter into conflict with each other, so-called rational dilemmas. For example, if the agent has a pre-existing intention that turns out to conflict with their beliefs, then the enkratic norm requires them to change it, which is disallowed by the norm of persistence. This suggests that, in cases of rational dilemmas, it is impossible to be rational, no matter which norm is privileged.[15][23][24] Some defenders of coherence theories of rationality have argued that, when formulated correctly, the norms of rationality cannot enter into conflict with each other. That means that rational dilemmas are impossible. This is sometimes tied to additional non-trivial assumptions, such that ethical dilemmas also do not exist. A different response is to bite the bullet and allow that rational dilemmas exist. This has the consequence that, in such cases, rationality is not possible for the agent and theories of rationality cannot offer guidance to them.[15][23][24] These problems are avoided by reason-responsiveness accounts of rationality since they "allow for rationality despite conflicting reasons but [coherence-based accounts] do not allow for rationality despite conflicting requirements". Some theorists suggest a weaker criterion of coherence to avoid cases of necessary irrationality: rationality requires not to obey all norms of coherence but to obey as many norms as possible. So in rational dilemmas, agents can still be rational if they violate the minimal number of rational requirements.[15]

Another criticism rests on the claim that coherence-based accounts are either redundant or false. On this view, either the rules recommend the same option as the balance of reasons or a different option. If they recommend the same option, they are redundant. If they recommend a different option, they are false since, according to its critics, there is no special value in sticking to rules against the balance of reasons.[7][20]

Based on goals

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A different approach characterizes rationality in relation to the goals it aims to achieve.[1][16] In this regard, theoretical rationality aims at epistemic goals, like acquiring truth and avoiding falsehood. Practical rationality, on the other hand, aims at non-epistemic goals, like moral, prudential, political, economic, or aesthetic goals. This is usually understood in the sense that rationality follows these goals but does not set them. So rationality may be understood as a "minister without portfolio" since it serves goals external to itself.[1] This issue has been the source of an important historical discussion between David Hume and Immanuel Kant. The slogan of Hume's position is that "reason is the slave of the passions". This is often understood as the claim that rationality concerns only how to reach a goal but not whether the goal should be pursued at all. So people with perverse or weird goals may still be perfectly rational. This position is opposed by Kant, who argues that rationality requires having the right goals and motives.[7][25][26][27][1]

According to William Frankena there are four conceptions of rationality based on the goals it tries to achieve. They correspond to egoism, utilitarianism, perfectionism, and intuitionism.[1][28][29] According to the egoist perspective, rationality implies looking out for one's own happiness. This contrasts with the utilitarian point of view, which states that rationality entails trying to contribute to everyone's well-being or to the greatest general good. For perfectionism, a certain ideal of perfection, either moral or non-moral, is the goal of rationality. According to the intuitionist perspective, something is rational "if and only if [it] conforms to self-evident truths, intuited by reason".[1][28] These different perspectives diverge a lot concerning the behavior they prescribe. One problem for all of them is that they ignore the role of the evidence or information possessed by the agent. In this regard, it matters for rationality not just whether the agent acts efficiently towards a certain goal but also what information they have and how their actions appear reasonable from this perspective. Richard Brandt responds to this idea by proposing a conception of rationality based on relevant information: "Rationality is a matter of what would survive scrutiny by all relevant information."[1] This implies that the subject repeatedly reflects on all the relevant facts, including formal facts like the laws of logic.[1]

Internalism and externalism

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An important contemporary discussion in the field of rationality is between internalists and externalists.[1][30][31] Both sides agree that rationality demands and depends in some sense on reasons. They disagree on what reasons are relevant or how to conceive those reasons. Internalists understand reasons as mental states, for example, as perceptions, beliefs, or desires. In this view, an action may be rational because it is in tune with the agent's beliefs and realizes their desires. Externalists, on the other hand, see reasons as external factors about what is good or right. They state that whether an action is rational also depends on its actual consequences.[1][30][31] The difference between the two positions is that internalists affirm and externalists reject the claim that rationality supervenes on the mind. This claim means that it only depends on the person's mind whether they are rational and not on external factors. So for internalism, two persons with the same mental states would both have the same degree of rationality independent of how different their external situation is. Because of this limitation, rationality can diverge from actuality. So if the agent has a lot of misleading evidence, it may be rational for them to turn left even though the actually correct path goes right.[2][1]

Bernard Williams has criticized externalist conceptions of rationality based on the claim that rationality should help explain what motivates the agent to act. This is easy for internalism but difficult for externalism since external reasons can be independent of the agent's motivation.[1][32][33] Externalists have responded to this objection by distinguishing between motivational and normative reasons.[1] Motivational reasons explain why someone acts the way they do while normative reasons explain why someone ought to act in a certain way. Ideally, the two overlap, but they can come apart. For example, liking chocolate cake is a motivational reason for eating it while having high blood pressure is a normative reason for not eating it.[34][35] The problem of rationality is primarily concerned with normative reasons. This is especially true for various contemporary philosophers who hold that rationality can be reduced to normative reasons.[2][17][18] The distinction between motivational and normative reasons is usually accepted, but many theorists have raised doubts that rationality can be identified with normativity. On this view, rationality may sometimes recommend suboptimal actions, for example, because the agent lacks important information or has false information. In this regard, discussions between internalism and externalism overlap with discussions of the normativity of rationality.[1]

Relativity

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An important implication of internalist conceptions is that rationality is relative to the person's perspective or mental states. Whether a belief or an action is rational usually depends on which mental states the person has. So carrying an umbrella for the walk to the supermarket is rational for a person believing that it will rain but irrational for another person who lacks this belief.[6][36][37] According to Robert Audi, this can be explained in terms of experience: what is rational depends on the agent's experience. Since different people make different experiences, there are differences in what is rational for them.[36]

Normativity

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Rationality is normative in the sense that it sets up certain rules or standards of correctness: to be rational is to comply with certain requirements.[2][15][16] For example, rationality requires that the agent does not have contradictory beliefs. Many discussions on this issue concern the question of what exactly these standards are. Some theorists characterize the normativity of rationality in the deontological terms of obligations and permissions. Others understand them from an evaluative perspective as good or valuable. A further approach is to talk of rationality based on what is praise- and blameworthy.[1] It is important to distinguish the norms of rationality from other types of norms. For example, some forms of fashion prescribe that men do not wear bell-bottom trousers. Understood in the strongest sense, a norm prescribes what an agent ought to do or what they have most reason to do. The norms of fashion are not norms in this strong sense: that it is unfashionable does not mean that men ought not to wear bell-bottom trousers.[2]

Most discussions of the normativity of rationality are interested in the strong sense, i.e. whether agents ought always to be rational.[2][18][17][38] This is sometimes termed a substantive account of rationality in contrast to structural accounts.[2][15] One important argument in favor of the normativity of rationality is based on considerations of praise- and blameworthiness. It states that we usually hold each other responsible for being rational and criticize each other when we fail to do so. This practice indicates that irrationality is some form of fault on the side of the subject that should not be the case.[39][38] A strong counterexample to this position is due to John Broome, who considers the case of a fish an agent wants to eat. It contains salmonella, which is a decisive reason why the agent ought not to eat it. But the agent is unaware of this fact, which is why it is rational for them to eat the fish.[17][18] So this would be a case where normativity and rationality come apart. This example can be generalized in the sense that rationality only depends on the reasons accessible to the agent or how things appear to them. What one ought to do, on the other hand, is determined by objectively existing reasons.[40][38] In the ideal case, rationality and normativity may coincide but they come apart either if the agent lacks access to a reason or if he has a mistaken belief about the presence of a reason. These considerations are summed up in the statement that rationality supervenes only on the agent's mind but normativity does not.[41][42]

But there are also thought experiments in favor of the normativity of rationality. One, due to Frank Jackson, involves a doctor who receives a patient with a mild condition and has to prescribe one out of three drugs: drug A resulting in a partial cure, drug B resulting in a complete cure, or drug C resulting in the patient's death.[43] The doctor's problem is that they cannot tell which of the drugs B and C results in a complete cure and which one in the patient's death. The objectively best case would be for the patient to get drug B, but it would be highly irresponsible for the doctor to prescribe it given the uncertainty about its effects. So the doctor ought to prescribe the less effective drug A, which is also the rational choice. This thought experiment indicates that rationality and normativity coincide since what is rational and what one ought to do depends on the agent's mind after all.[40][38]

Some theorists have responded to these thought experiments by distinguishing between normativity and responsibility.[38] On this view, critique of irrational behavior, like the doctor prescribing drug B, involves a negative evaluation of the agent in terms of responsibility but remains silent on normative issues. On a competence-based account, which defines rationality in terms of the competence of responding to reasons, such behavior can be understood as a failure to execute one's competence. But sometimes we are lucky and we succeed in the normative dimension despite failing to perform competently, i.e. rationally, due to being irresponsible.[38][44] The opposite can also be the case: bad luck may result in failure despite a responsible, competent performance. This explains how rationality and normativity can come apart despite our practice of criticizing irrationality.[38][45]

Normative and descriptive theories

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The concept of normativity can also be used to distinguish different theories of rationality. Normative theories explore the normative nature of rationality. They are concerned with rules and ideals that govern how the mind should work. Descriptive theories, on the other hand, investigate how the mind actually works. This includes issues like under which circumstances the ideal rules are followed as well as studying the underlying psychological processes responsible for rational thought. Descriptive theories are often investigated in empirical psychology while philosophy tends to focus more on normative issues. This division also reflects how different these two types are investigated.[6][46][16][47]

Descriptive and normative theorists usually employ different methodologies in their research. Descriptive issues are studied by empirical research. This can take the form of studies that present their participants with a cognitive problem. It is then observed how the participants solve the problem, possibly together with explanations of why they arrived at a specific solution. Normative issues, on the other hand, are usually investigated in similar ways to how the formal sciences conduct their inquiry.[6][46] In the field of theoretical rationality, for example, it is accepted that deductive reasoning in the form of modus ponens leads to rational beliefs. This claim can be investigated using methods like rational intuition or careful deliberation toward a reflective equilibrium. These forms of investigation can arrive at conclusions about what forms of thought are rational and irrational without depending on empirical evidence.[6][48][49]

An important question in this field concerns the relation between descriptive and normative approaches to rationality.[6][16][47] One difficulty in this regard is that there is in many cases a huge gap between what the norms of ideal rationality prescribe and how people actually reason. Examples of normative systems of rationality are classical logic, probability theory, and decision theory. Actual reasoners often diverge from these standards because of cognitive biases, heuristics, or other mental limitations.[6]

Traditionally, it was often assumed that actual human reasoning should follow the rules described in normative theories. In this view, any discrepancy is a form of irrationality that should be avoided. However, this usually ignores the human limitations of the mind. Given these limitations, various discrepancies may be necessary (and in this sense rational) to get the most useful results.[6][12][1] For example, the ideal rational norms of decision theory demand that the agent should always choose the option with the highest expected value. However, calculating the expected value of each option may take a very long time in complex situations and may not be worth the trouble. This is reflected in the fact that actual reasoners often settle for an option that is good enough without making certain that it is really the best option available.[1][50] A further difficulty in this regard is Hume's law, which states that one cannot deduce what ought to be based on what is.[51][52] So just because a certain heuristic or cognitive bias is present in a specific case, it should not be inferred that it should be present. One approach to these problems is to hold that descriptive and normative theories talk about different types of rationality. This way, there is no contradiction between the two and both can be correct in their own field. Similar problems are discussed in so-called naturalized epistemology.[6][53]

Conservatism and foundationalism

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Rationality is usually understood as conservative in the sense that rational agents do not start from zero but already possess many beliefs and intentions. Reasoning takes place on the background of these pre-existing mental states and tries to improve them. This way, the original beliefs and intentions are privileged: one keeps them unless a reason to doubt them is encountered. Some forms of epistemic foundationalism reject this approach. According to them, the whole system of beliefs is to be justified by self-evident beliefs. Examples of such self-evident beliefs may include immediate experiences as well as simple logical and mathematical axioms.[12][54][55]

An important difference between conservatism and foundationalism concerns their differing conceptions of the burden of proof. According to conservativism, the burden of proof is always in favor of already established belief: in the absence of new evidence, it is rational to keep the mental states one already has. According to foundationalism, the burden of proof is always in favor of suspending mental states. For example, the agent reflects on their pre-existing belief that the Taj Mahal is in Agra but is unable to access any reason for or against this belief. In this case, conservatives think it is rational to keep this belief while foundationalists reject it as irrational due to the lack of reasons. In this regard, conservatism is much closer to the ordinary conception of rationality. One problem for foundationalism is that very few beliefs, if any, would remain if this approach was carried out meticulously. Another is that enormous mental resources would be required to constantly keep track of all the justificatory relations connecting non-fundamental beliefs to fundamental ones.[12][54][55]

Types

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Rationality is discussed in a great variety of fields, often in very different terms. While some theorists try to provide a unifying conception expressing the features shared by all forms of rationality, the more common approach is to articulate the different aspects of the individual forms of rationality. The most common distinction is between theoretical and practical rationality. Other classifications include categories for ideal and bounded rationality as well as for individual and social rationality.[6][56]

Theoretical and practical

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The most influential distinction contrasts theoretical or epistemic rationality with practical rationality. Its theoretical side concerns the rationality of beliefs: whether it is rational to hold a given belief and how certain one should be about it. Practical rationality, on the other hand, is about the rationality of actions, intentions, and decisions.[7][12][56][27] This corresponds to the distinction between theoretical reasoning and practical reasoning: theoretical reasoning tries to assess whether the agent should change their beliefs while practical reasoning tries to assess whether the agent should change their plans and intentions.[12][56][27]

Theoretical

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Theoretical rationality concerns the rationality of cognitive mental states, in particular, of beliefs.[7][4] It is common to distinguish between two factors. The first factor is about the fact that good reasons are necessary for a belief to be rational. This is usually understood in terms of evidence provided by the so-called sources of knowledge, i.e. faculties like perception, introspection, and memory. In this regard, it is often argued that to be rational, the believer has to respond to the impressions or reasons presented by these sources. For example, the visual impression of the sunlight on a tree makes it rational to believe that the sun is shining.[27][7][4] In this regard, it may also be relevant whether the formed belief is involuntary and implicit

The second factor pertains to the norms and procedures of rationality that govern how agents should form beliefs based on this evidence. These norms include the rules of inference discussed in regular logic as well as other norms of coherence between mental states.[7][4] In the case of rules of inference, the premises of a valid argument offer support to the conclusion and make therefore the belief in the conclusion rational.[27] The support offered by the premises can either be deductive or non-deductive.[57][58] In both cases, believing in the premises of an argument makes it rational to also believe in its conclusion. The difference between the two is given by how the premises support the conclusion. For deductive reasoning, the premises offer the strongest possible support: it is impossible for the conclusion to be false if the premises are true. The premises of non-deductive arguments also offer support for their conclusion. But this support is not absolute: the truth of the premises does not guarantee the truth of the conclusion. Instead, the premises make it more likely that the conclusion is true. In this case, it is usually demanded that the non-deductive support is sufficiently strong if the belief in the conclusion is to be rational.[56][27][57]

An important form of theoretical irrationality is motivationally biased belief, sometimes referred to as wishful thinking. In this case, beliefs are formed based on one's desires or what is pleasing to imagine without proper evidential support.[7][59] Faulty reasoning in the form of formal and informal fallacies is another cause of theoretical irrationality.[60]

Practical

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All forms of practical rationality are concerned with how we act. It pertains both to actions directly as well as to mental states and events preceding actions, like intentions and decisions. There are various aspects of practical rationality, such as how to pick a goal to follow and how to choose the means for reaching this goal. Other issues include the coherence between different intentions as well as between beliefs and intentions.[61][62][1]

Some theorists define the rationality of actions in terms of beliefs and desires. In this view, an action to bring about a certain goal is rational if the agent has the desire to bring about this goal and the belief that their action will realize it. A stronger version of this view requires that the responsible beliefs and desires are rational themselves.[6] A very influential conception of the rationality of decisions comes from decision theory. In decisions, the agent is presented with a set of possible courses of action and has to choose one among them. Decision theory holds that the agent should choose the alternative that has the highest expected value.[61] Practical rationality includes the field of actions but not of behavior in general. The difference between the two is that actions are intentional behavior, i.e. they are performed for a purpose and guided by it. In this regard, intentional behavior like driving a car is either rational or irrational while non-intentional behavior like sneezing is outside the domain of rationality.[6][63][64]

For various other practical phenomena, there is no clear consensus on whether they belong to this domain or not. For example, concerning the rationality of desires, two important theories are proceduralism and substantivism. According to proceduralism, there is an important distinction between instrumental and noninstrumental desires. A desire is instrumental if its fulfillment serves as a means to the fulfillment of another desire.[65][12][6] For example, Jack is sick and wants to take medicine to get healthy again. In this case, the desire to take the medicine is instrumental since it only serves as a means to Jack's noninstrumental desire to get healthy. Both proceduralism and substantivism usually agree that a person can be irrational if they lack an instrumental desire despite having the corresponding noninstrumental desire and being aware that it acts as a means. Proceduralists hold that this is the only way a desire can be irrational. Substantivists, on the other hand, allow that noninstrumental desires may also be irrational. In this regard, a substantivist could claim that it would be irrational for Jack to lack his noninstrumental desire to be healthy.[7][65][6] Similar debates focus on the rationality of emotions.[6]

Relation between the two

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Theoretical and practical rationality are often discussed separately and there are many differences between them. In some cases, they even conflict with each other. However, there are also various ways in which they overlap and depend on each other.[61][6]

It is sometimes claimed that theoretical rationality aims at truth while practical rationality aims at goodness.[61] According to John Searle, the difference can be expressed in terms of "direction of fit".[6][66][67] On this view, theoretical rationality is about how the mind corresponds to the world by representing it. Practical rationality, on the other hand, is about how the world corresponds to the ideal set up by the mind and how it should be changed.[6][7][68][1] Another difference is that arbitrary choices are sometimes needed for practical rationality. For example, there may be two equally good routes available to reach a goal. On the practical level, one has to choose one of them if one wants to reach the goal. It would even be practically irrational to resist this arbitrary choice, as exemplified by Buridan's ass.[12][69] But on the theoretical level, one does not have to form a belief about which route was taken upon hearing that someone reached the goal. In this case, the arbitrary choice for one belief rather than the other would be theoretically irrational. Instead, the agent should suspend their belief either way if they lack sufficient reasons. Another difference is that practical rationality is guided by specific goals and desires, in contrast to theoretical rationality. So it is practically rational to take medicine if one has the desire to cure a sickness. But it is theoretically irrational to adopt the belief that one is healthy just because one desires this. This is a form of wishful thinking.[12]

In some cases, the demands of practical and theoretical rationality conflict with each other. For example, the practical reason of loyalty to one's child may demand the belief that they are innocent while the evidence linking them to the crime may demand a belief in their guilt on the theoretical level.[12][68]

But the two domains also overlap in certain ways. For example, the norm of rationality known as enkrasia links beliefs and intentions. It states that "rationality requires of you that you intend to F if you believe your reasons require you to F". Failing to fulfill this requirement results in cases of irrationality known as akrasia or weakness of the will.[2][1][15][7][59] Another form of overlap is that the study of the rules governing practical rationality is a theoretical matter.[7][70] And practical considerations may determine whether to pursue theoretical rationality on a certain issue as well as how much time and resources to invest in the inquiry.[68][59] It is often held that practical rationality presupposes theoretical rationality. This is based on the idea that to decide what should be done, one needs to know what is the case. But one can assess what is the case independently of knowing what should be done. So in this regard, one can study theoretical rationality as a distinct discipline independent of practical rationality but not the other way round.[6] However, this independence is rejected by some forms of doxastic voluntarism. They hold that theoretical rationality can be understood as one type of practical rationality. This is based on the controversial claim that we can decide what to believe. It can take the form of epistemic decision theory, which states that people try to fulfill epistemic aims when deciding what to believe.[6][71][72] A similar idea is defended by Jesús Mosterín. He argues that the proper object of rationality is not belief but acceptance. He understands acceptance as a voluntary and context-dependent decision to affirm a proposition.[73]

Ideal and bounded

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Various theories of rationality assume some form of ideal rationality, for example, by demanding that rational agents obey all the laws and implications of logic. This can include the requirement that if the agent believes a proposition, they should also believe in everything that logically follows from this proposition. However, many theorists reject this form of logical omniscience as a requirement for rationality. They argue that, since the human mind is limited, rationality has to be defined accordingly to account for how actual finite humans possess some form of resource-limited rationality.[12][6][1]

According to the position of bounded rationality, theories of rationality should take into account cognitive limitations, such as incomplete knowledge, imperfect memory, and limited capacities of computation and representation. An important research question in this field is about how cognitive agents use heuristics rather than brute calculations to solve problems and make decisions. According to the satisficing heuristic, for example, agents usually stop their search for the best option once an option is found that meets their desired achievement level. In this regard, people often do not continue to search for the best possible option, even though this is what theories of ideal rationality commonly demand.[6][1][50] Using heuristics can be highly rational as a way to adapt to the limitations of the human mind, especially in complex cases where these limitations make brute calculations impossible or very time- and resource-intensive.[6][1]

Individual and social

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Most discussions and research in the academic literature focus on individual rationality. This concerns the rationality of individual persons, for example, whether their beliefs and actions are rational. But the question of rationality can also be applied to groups as a whole on the social level. This form of social or collective rationality concerns both theoretical and practical issues like group beliefs and group decisions.[6][74][75] And just like in the individual case, it is possible to study these phenomena as well as the processes and structures that are responsible for them. On the social level, there are various forms of cooperation to reach a shared goal. In theoretical cases, a group of jurors may first discuss and then vote to determine whether the defendant is guilty. Or in the practical case, politicians may cooperate to implement new regulations to combat climate change. These forms of cooperation can be judged on their social rationality depending on how they are implemented and on the quality of the results they bear. Some theorists try to reduce social rationality to individual rationality by holding that the group processes are rational to the extent that the individuals participating in them are rational. But such a reduction is frequently rejected.[6][74]

Various studies indicate that group rationality often outperforms individual rationality. For example, groups of people working together on the Wason selection task usually perform better than individuals by themselves. This form of group superiority is sometimes termed "wisdom of crowds" and may be explained based on the claim that competent individuals have a stronger impact on the group decision than others.[6][76] However, this is not always the case and sometimes groups perform worse due to conformity or unwillingness to bring up controversial issues.[6]

Others

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Many other classifications are discussed in the academic literature. One important distinction is between approaches to rationality based on the output or on the process. Process-oriented theories of rationality are common in cognitive psychology and study how cognitive systems process inputs to generate outputs. Output-oriented approaches are more common in philosophy and investigate the rationality of the resulting states.[6][2] Another distinction is between relative and categorical judgments of rationality. In the relative case, rationality is judged based on limited information or evidence while categorical judgments take all the evidence into account and are thus judgments all things considered.[6][1] For example, believing that one's investments will multiply can be rational in a relative sense because it is based on one's astrological horoscope. But this belief is irrational in a categorical sense if the belief in astrology is itself irrational.[6]

Importance

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Rationality is central to solving many problems, both on the local and the global scale. This is often based on the idea that rationality is necessary to act efficiently and to reach all kinds of goals.[6][16] This includes goals from diverse fields, such as ethical goals, humanist goals, scientific goals, and even religious goals.[6] The study of rationality is very old and has occupied many of the greatest minds since ancient Greek. This interest is often motivated by discovering the potentials and limitations of our minds. Various theorists even see rationality as the essence of being human, often in an attempt to distinguish humans from other animals.[6][8][9] However, this strong affirmation has been subjected to many criticisms, for example, that humans are not rational all the time and that non-human animals also show diverse forms of intelligence.[6]

The topic of rationality is relevant to a variety of disciplines. It plays a central role in philosophy, psychology, Bayesianism, decision theory, and game theory.[7] But it is also covered in other disciplines, such as artificial intelligence, behavioral economics, microeconomics, and neuroscience. Some forms of research restrict themselves to one specific domain while others investigate the topic in an interdisciplinary manner by drawing insights from different fields.[56]

Paradoxes of rationality

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The term paradox of rationality has a variety of meanings. It is often used for puzzles or unsolved problems of rationality. Some are just situations where it is not clear what the rational person should do. Others involve apparent faults within rationality itself, for example, where rationality seems to recommend a suboptimal course of action.[7] A special case are so-called rational dilemmas, in which it is impossible to be rational since two norms of rationality conflict with each other.[23][24] Examples of paradoxes of rationality include Pascal's Wager, the Prisoner's dilemma, Buridan's ass, and the St. Petersburg paradox.[7][77][21]

History

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Max Weber

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German scholar Max Weber notably articulated a theory of rationality that divided human capacity to think through things in four ways.[78]

The German scholar Max Weber proposed an interpretation of social action that distinguished between four different idealized types of rationality.[78]

The first, which he called Zweckrational or purposive/instrumental rationality, is related to the expectations about the behavior of other human beings or objects in the environment. These expectations serve as means for a particular actor to attain ends, ends which Weber noted were "rationally pursued and calculated."[This quote needs a citation] The second type, Weber called Wertrational or value/belief-oriented. Here the action is undertaken for what one might call reasons intrinsic to the actor: some ethical, aesthetic, religious or other motives, independent of whether it will lead to success. The third type was affectual, determined by an actor's specific affect, feeling, or emotion—to which Weber himself said that this was a kind of rationality that was on the borderline of what he considered "meaningfully oriented." The fourth was traditional or conventional, determined by ingrained habituation. Weber emphasized that it was very unusual to find only one of these orientations: combinations were the norm. His usage also makes clear that he considered the first two as more significant than the others, and it is arguable that the third and fourth are subtypes of the first two.

The advantage in Weber's interpretation of rationality is that it avoids a value-laden assessment, say, that certain kinds of beliefs are irrational. Instead, Weber suggests that ground or motive can be given—for religious or affect reasons, for example—that may meet the criterion of explanation or justification even if it is not an explanation that fits the Zweckrational orientation of means and ends. The opposite is therefore also true: some means-ends explanations will not satisfy those whose grounds for action are Wertrational.

Weber's constructions of rationality have been critiqued both from a Habermasian (1984) perspective (as devoid of social context and under-theorised in terms of social power)[79] and also from a feminist perspective (Eagleton, 2003) whereby Weber's rationality constructs are viewed as imbued with masculine values and oriented toward the maintenance of male power.[80] An alternative position on rationality (which includes both bounded rationality,[81] as well as the affective and value-based arguments of Weber) can be found in the critique of Etzioni (1988),[82] who reframes thought on decision-making to argue for a reversal of the position put forward by Weber. Etzioni illustrates how purposive/instrumental reasoning is subordinated by normative considerations (ideas on how people 'ought' to behave) and affective considerations (as a support system for the development of human relationships).

Richard Brandt

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Richard Brandt proposed a "reforming definition" of rationality, arguing someone is rational if their notions survive a form of cognitive-psychotherapy.[83]

Robert Audi

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Robert Audi developed a comprehensive account of rationality that covers both the theoretical and the practical side of rationality.[36][84] This account centers on the notion of a ground: a mental state is rational if it is "well-grounded" in a source of justification.[84]: 19  Irrational mental states, on the other hand, lack a sufficient ground. For example, the perceptual experience of a tree when looking outside the window can ground the rationality of the belief that there is a tree outside.

Audi is committed to a form of foundationalism: the idea that justified beliefs, or in his case, rational states in general, can be divided into two groups: the foundation and the superstructure.[84]: 13, 29–31  The mental states in the superstructure receive their justification from other rational mental states while the foundational mental states receive their justification from a more basic source.[84]: 16–18  For example, the above-mentioned belief that there is a tree outside is foundational since it is based on a basic source: perception. Knowing that trees grow in soil, we may deduce that there is soil outside. This belief is equally rational, being supported by an adequate ground, but it belongs to the superstructure since its rationality is grounded in the rationality of another belief. Desires, like beliefs, form a hierarchy: intrinsic desires are at the foundation while instrumental desires belong to the superstructure. In order to link the instrumental desire to the intrinsic desire an extra element is needed: a belief that the fulfillment of the instrumental desire is a means to the fulfillment of the intrinsic desire.[85]

Audi asserts that all the basic sources providing justification for the foundational mental states come from experience. As for beliefs, there are four types of experience that act as sources: perception, memory, introspection, and rational intuition.[86] The main basic source of the rationality of desires, on the other hand, comes in the form of hedonic experience: the experience of pleasure and pain.[87]: 20  So, for example, a desire to eat ice-cream is rational if it is based on experiences in which the agent enjoyed the taste of ice-cream, and irrational if it lacks such a support. Because of its dependence on experience, rationality can be defined as a kind of responsiveness to experience.[87]: 21 

Actions, in contrast to beliefs and desires, do not have a source of justification of their own. Their rationality is grounded in the rationality of other states instead: in the rationality of beliefs and desires. Desires motivate actions. Beliefs are needed here, as in the case of instrumental desires, to bridge a gap and link two elements.[84]: 62  Audi distinguishes the focal rationality of individual mental states from the global rationality of persons. Global rationality has a derivative status: it depends on the focal rationality.[36] Or more precisely: "Global rationality is reached when a person has a sufficiently integrated system of sufficiently well-grounded propositional attitudes, emotions, and actions".[84]: 232  Rationality is relative in the sense that it depends on the experience of the person in question. Since different people undergo different experiences, what is rational to believe for one person may be irrational to believe for another person.[36] That a belief is rational does not entail that it is true.[85]

In various fields

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Ethics and morality

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The problem of rationality is relevant to various issues in ethics and morality.[7] Many debates center around the question of whether rationality implies morality or is possible without it. Some examples based on common sense suggest that the two can come apart. For example, some immoral psychopaths are highly intelligent in the pursuit of their schemes and may, therefore, be seen as rational. However, there are also considerations suggesting that the two are closely related to each other. For example, according to the principle of universality, "one's reasons for acting are acceptable only if it is acceptable that everyone acts on such reasons".[12] A similar formulation is given in Immanuel Kant's categorical imperative: "act only according to that maxim whereby you can, at the same time, will that it should become a universal law".[88] The principle of universality has been suggested as a basic principle both for morality and for rationality.[12] This is closely related to the question of whether agents have a duty to be rational. Another issue concerns the value of rationality. In this regard, it is often held that human lives are more important than animal lives because humans are rational.[12][8]

Psychology

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Many psychological theories have been proposed to describe how reasoning happens and what underlying psychological processes are responsible. One of their goals is to explain how the different types of irrationality happen and why some types are more prevalent than others. They include mental logic theories, mental model theories, and dual process theories.[56][89][90] An important psychological area of study focuses on cognitive biases. Cognitive biases are systematic tendencies to engage in erroneous or irrational forms of thinking, judging, and acting. Examples include the confirmation bias, the self-serving bias, the hindsight bias, and the Dunning–Kruger effect.[91][92][93] Some empirical findings suggest that metacognition is an important aspect of rationality. The idea behind this claim is that reasoning is carried out more efficiently and reliably if the responsible thought processes are properly controlled and monitored.[56]

The Wason selection task is an influential test for studying rationality and reasoning abilities. In it, four cards are placed before the participants. Each has a number on one side and a letter on the opposite side. In one case, the visible sides of the four cards are A, D, 4, and 7. The participant is then asked which cards need to be turned around in order to verify the conditional claim "if there is a vowel on one side of the card, then there is an even number on the other side of the card". The correct answer is A and 7. But this answer is only given by about 10%. Many choose card 4 instead even though there is no requirement on what letters may appear on its opposite side.[6][89][94] An important insight from using these and similar tests is that the rational ability of the participants is usually significantly better for concrete and realistic cases than for abstract or implausible cases.[89][94] Various contemporary studies in this field use Bayesian probability theory to study subjective degrees of belief, for example, how the believer's certainty in the premises is carried over to the conclusion through reasoning.[6]

In the psychology of reasoning, psychologists and cognitive scientists have defended different positions on human rationality. One prominent view, due to Philip Johnson-Laird and Ruth M. J. Byrne among others is that humans are rational in principle but they err in practice, that is, humans have the competence to be rational but their performance is limited by various factors.[95] However, it has been argued that many standard tests of reasoning, such as those on the conjunction fallacy, on the Wason selection task, or the base rate fallacy suffer from methodological and conceptual problems. This has led to disputes in psychology over whether researchers should (only) use standard rules of logic, probability theory and statistics, or rational choice theory as norms of good reasoning. Opponents of this view, such as Gerd Gigerenzer, favor a conception of bounded rationality, especially for tasks under high uncertainty.[96] The concept of rationality continues to be debated by psychologists, economists and cognitive scientists.[97]

The psychologist Jean Piaget gave an influential account of how the stages in human development from childhood to adulthood can be understood in terms of the increase of rational and logical abilities.[6][98][99][100] He identifies four stages associated with rough age groups: the sensorimotor stage below the age of two, the preoperational state until the age of seven, the concrete operational stage until the age of eleven, and the formal operational stage afterward. Rational or logical reasoning only takes place in the last stage and is related to abstract thinking, concept formation, reasoning, planning, and problem-solving.[6]

Emotions

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According to A. C. Grayling, rationality "must be independent of emotions, personal feelings or any kind of instincts".[101] Certain findings[which?] in cognitive science and neuroscience show that no human has ever satisfied this criterion, except perhaps a person with no affective feelings, for example, an individual with a massively damaged amygdala or severe psychopathy. Thus, such an idealized form of rationality is best exemplified by computers, and not people. However, scholars may productively appeal to the idealization as a point of reference. [citation needed] In his book, The Edge of Reason: A Rational Skeptic in an Irrational World, British philosopher Julian Baggini sets out to debunk myths about reason (e.g., that it is "purely objective and requires no subjective judgment").[102]

Cognitive and behavioral sciences

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Cognitive and behavioral sciences try to describe, explain, and predict how people think and act. Their models are often based on the assumption that people are rational. For example, classical economics is based on the assumption that people are rational agents that maximize expected utility. However, people often depart from the ideal standards of rationality in various ways. For example, they may only look for confirming evidence and ignore disconfirming evidence. Another factor studied in this regard are the limitations of human intellectual capacities. Many discrepancies from rationality are caused by limited time, memory, or attention. Often heuristics and rules of thumb are used to mitigate these limitations, but they may lead to new forms of irrationality.[12][1][50]

Logic

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Theoretical rationality is closely related to logic, but not identical to it.[12][6] Logic is often defined as the study of correct arguments. This concerns the relation between the propositions used in the argument: whether its premises offer support to its conclusion. Theoretical rationality, on the other hand, is about what to believe or how to change one's beliefs. The laws of logic are relevant to rationality since the agent should change their beliefs if they violate these laws. But logic is not directly about what to believe. Additionally, there are also other factors and norms besides logic that determine whether it is rational to hold or change a belief.[12] The study of rationality in logic is more concerned with epistemic rationality, that is, attaining beliefs in a rational manner, than instrumental rationality.

Decision theory

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An influential account of practical rationality is given by decision theory.[12][56][6] Decisions are situations where a number of possible courses of action are available to the agent, who has to choose one of them. Decision theory investigates the rules governing which action should be chosen. It assumes that each action may lead to a variety of outcomes. Each outcome is associated with a conditional probability and a utility. The expected gain of an outcome can be calculated by multiplying its conditional probability with its utility. The expected utility of an act is equivalent to the sum of all expected gains of the outcomes associated with it. From these basic ingredients, it is possible to define the rationality of decisions: a decision is rational if it selects the act with the highest expected utility.[12][6] While decision theory gives a very precise formal treatment of this issue, it leaves open the empirical problem of how to assign utilities and probabilities. So decision theory can still lead to bad empirical decisions if it is based on poor assignments.[12]

According to decision theorists, rationality is primarily a matter of internal consistency. This means that a person's mental states like beliefs and preferences are consistent with each other or do not go against each other. One consequence of this position is that people with obviously false beliefs or perverse preferences may still count as rational if these mental states are consistent with their other mental states.[7] Utility is often understood in terms of self-interest or personal preferences. However, this is not a necessary aspect of decisions theory and it can also be interpreted in terms of goodness or value in general.[7][70]

Game theory

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Game theory is closely related to decision theory and the problem of rational choice.[7][56] Rational choice is based on the idea that rational agents perform a cost-benefit analysis of all available options and choose the option that is most beneficial from their point of view. In the case of game theory, several agents are involved. This further complicates the situation since whether a given option is the best choice for one agent may depend on choices made by other agents. Game theory can be used to analyze various situations, like playing chess, firms competing for business, or animals fighting over prey. Rationality is a core assumption of game theory: it is assumed that each player chooses rationally based on what is most beneficial from their point of view. This way, the agent may be able to anticipate how others choose and what their best choice is relative to the behavior of the others.[7][103][104][105] This often results in a Nash equilibrium, which constitutes a set of strategies, one for each player, where no player can improve their outcome by unilaterally changing their strategy.[7][103][104]

Bayesianism

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A popular contemporary approach to rationality is based on Bayesian epistemology.[7][106] Bayesian epistemology sees belief as a continuous phenomenon that comes in degrees. For example, Daniel is relatively sure that the Boston Celtics will win their next match and absolutely certain that two plus two equals four. In this case, the degree of the first belief is weaker than the degree of the second belief. These degrees are usually referred to as credences and represented by numbers between 0 and 1, where 0 corresponds to full disbelief, 1 corresponds to full belief and 0.5 corresponds to suspension of belief. Bayesians understand this in terms of probability: the higher the credence, the higher the subjective probability that the believed proposition is true. As probabilities, they are subject to the laws of probability theory. These laws act as norms of rationality: beliefs are rational if they comply with them and irrational if they violate them.[107][108][109] For example, it would be irrational to have a credence of 0.9 that it will rain tomorrow together with another credence of 0.9 that it will not rain tomorrow. This account of rationality can also be extended to the practical domain by requiring that agents maximize their subjective expected utility. This way, Bayesianism can provide a unified account of both theoretical and practical rationality.[7][106][6]

Economics

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Rationality plays a key role in economics and there are several strands to this.[110] Firstly, there is the concept of instrumentality—basically the idea that people and organisations are instrumentally rational—that is, adopt the best actions to achieve their goals. Secondly, there is an axiomatic concept that rationality is a matter of being logically consistent within your preferences and beliefs. Thirdly, people have focused on the accuracy of beliefs and full use of information—in this view, a person who is not rational has beliefs that do not fully use the information they have.

Debates within economic sociology also arise as to whether or not people or organizations are "really" rational, as well as whether it makes sense to model them as such in formal models. Some have argued that a kind of bounded rationality makes more sense for such models.

Others think that any kind of rationality along the lines of rational choice theory is a useless concept for understanding human behavior; the term homo economicus (economic man: the imaginary man being assumed in economic models who is logically consistent but amoral) was coined largely in honor of this view. Behavioral economics aims to account for economic actors as they actually are, allowing for psychological biases, rather than assuming idealized instrumental rationality.

Artificial intelligence

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The field of artificial intelligence is concerned, among other things, with how problems of rationality can be implemented and solved by computers.[56] Within artificial intelligence, a rational agent is typically one that maximizes its expected utility, given its current knowledge. Utility is the usefulness of the consequences of its actions. The utility function is arbitrarily defined by the designer, but should be a function of "performance", which is the directly measurable consequences, such as winning or losing money. In order to make a safe agent that plays defensively, a nonlinear function of performance is often desired, so that the reward for winning is lower than the punishment for losing. An agent might be rational within its own problem area, but finding the rational decision for arbitrarily complex problems is not practically possible. The rationality of human thought is a key problem in the psychology of reasoning.[111]

International relations

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There is an ongoing debate over the merits of using "rationality" in the study of international relations (IR). Some scholars hold it indispensable.[112] Others are more critical.[113] Still, the pervasive and persistent usage of "rationality" in political science and IR is beyond dispute. "Rationality" remains ubiquitous in this field. Abulof finds that Some 40% of all scholarly references to "foreign policy" allude to "rationality"—and this ratio goes up to more than half of pertinent academic publications in the 2000s. He further argues that when it comes to concrete security and foreign policies, IR employment of rationality borders on "malpractice": rationality-based descriptions are largely either false or unfalsifiable; many observers fail to explicate the meaning of "rationality" they employ; and the concept is frequently used politically to distinguish between "us and them."[114]

Criticism

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The concept of rationality has been subject to criticism by various philosophers who question its universality and capacity to provide a comprehensive understanding of reality and human existence.

Friedrich Nietzsche, in his work "Beyond Good and Evil" (1886), criticized the overemphasis on rationality and argued that it neglects the irrational and instinctual aspects of human nature. Nietzsche advocated for a reevaluation of values based on individual perspectives and the will to power, stating, "There are no facts, only interpretations."[115]

Martin Heidegger, in "Being and Time" (1927), offered a critique of the instrumental and calculative view of reason, emphasizing the primacy of our everyday practical engagement with the world. Heidegger challenged the notion that rationality alone is the sole arbiter of truth and understanding.[116]

Max Horkheimer and Theodor Adorno, in their seminal work "Dialectic of Enlightenment"[117] (1947), questioned the Enlightenment's rationality. They argued that the dominance of instrumental reason in modern society leads to the domination of nature and the dehumanization of individuals. Horkheimer and Adorno highlighted how rationality narrows the scope of human experience and hinders critical thinking.

Michel Foucault, in "Discipline and Punish"[118] (1975) and "The Birth of Biopolitics"[119] (1978), critiqued the notion of rationality as a neutral and objective force. Foucault emphasized the intertwining of rationality with power structures and its role in social control. He famously stated, "Power is not an institution, and not a structure; neither is it a certain strength we are endowed with; it is the name that one attributes to a complex strategic situation in a particular society."[120]

These philosophers' critiques of rationality shed light on its limitations, assumptions, and potential dangers. Their ideas challenge the universal application of rationality as the sole framework for understanding the complexities of human existence and the world.


See also

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References

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Further reading

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Revisions and contributorsEdit on WikipediaRead on Wikipedia
from Grokipedia
![Socrates.png][float-right]
Rationality is the cognitive capacity to form beliefs and make decisions through , probabilistic updating, and evaluation of evidence, prioritizing consistency and effectiveness over intuition or emotion. Originating in , where figures like emphasized self-examination and to pursue truth, the concept evolved during the Enlightenment to champion empirical observation and deduction against dogma and authority. In modern contexts, rationality encompasses epistemic rationality, the pursuit of accurate world models via tools like , and instrumental rationality, selecting actions that reliably achieve objectives under uncertainty. Defining achievements include formal frameworks in , such as expected maximization, which underpin and , enabling predictive models and optimal strategies. Controversies arise from empirical findings of systematic biases, like and framing effects, revealing human deviations from ideal rationality and prompting theories of that account for cognitive constraints and real-world heuristics. Despite these limitations, cultivating rationality through education and deliberate practice demonstrably enhances judgment and societal progress, countering irrationality's role in errors from to policy failures.

Core Concepts

First-Principles Definition

Rationality, derived from foundational cognitive and logical processes, is the commitment to forming beliefs and selecting actions that align with objective through non-contradictory integration of perceptual data and . This begins with the of existence—that is what it is, independent of human wishes or perceptions—and proceeds via reason, the faculty that identifies causal relationships and distinguishes fact from by adherence to . Epistemic rationality, in this view, evaluates the justification of beliefs as their probable truth-conduciveness, aiming to maximize true beliefs while minimizing falsehoods based on available evidence. Instrumental rationality extends this to action, optimizing means toward specified ends given constraints, without presupposing the rationality of those ends themselves. At its core, first-principles rationality rejects analogical or authority-based in favor of to irreducible truths—such as sensory evidence and logical axioms—and reconstruction via valid rules. For instance, in problem-solving, one breaks complex phenomena into elemental components verifiable by or deduction, then rebuilds solutions free from extraneous assumptions. This approach counters cognitive tendencies toward , as articulated in the principle that the primary safeguard against error is toward one's own faculties and conclusions. Empirical validation is integral: claims must withstand testing against outcomes, privileging causal explanations over correlative or narrative ones, as underpins predictive accuracy in real-world interactions. Such a definition underscores rationality's normative character without ; deviations, like persisting in contradicted beliefs or pursuing inefficient paths despite evident alternatives, constitute , measurable by failure to achieve veridical or goal attainment. Historical formulations, while varied, converge on this realist foundation, distinguishing human from instinctual or emotive responses by its capacity for error-correction through reason. Modern reinforces this by identifying biases—such as , where evidence is selectively interpreted to affirm priors—as systematic departures from first-principles adherence, resolvable through deliberate probabilistic updating akin to grounded in evidence ratios.

Theoretical and Practical Dimensions

Theoretical rationality concerns the epistemic standards for beliefs and judgments, evaluating their alignment with available and logical coherence to approximate truth. It posits that rational credences—degrees of —must satisfy the , such as non-negativity, normalization, and finite additivity, to avoid arbitrage opportunities like Dutch books, where inconsistent probabilities lead to guaranteed losses. Bayesian updating further operationalizes this dimension by requiring agents to revise via conditionalization upon new , formally P(H|E) = P(E|H) P(H) / P(E), ensuring dynamic coherence over time. This framework, rooted in deriving probability from qualitative coherence conditions, treats rationality as conformity to these norms rather than guaranteed accuracy, though violations correlate with empirical inaccuracies in studies. Practical rationality, by contrast, governs deliberation and action, assessing choices by their efficacy in realizing an agent's ends under constraints of uncertainty and limited information. It encompasses instrumental rationality, where reason identifies means to given goals, as in Hume's view of reason as subordinate to passions, ensuring actions like acquiring tools when intending a task. Structural rationality adds requirements for attitudinal coherence, such as wide-scope norms prohibiting intention without means (e.g., intending A and B but not A & B), independent of outcomes. Maximizing conceptions, formalized in , demand selecting acts that maximize expected utility, defined as ∑ p(s_i) u(o_i) over states s_i and outcomes o_i, with axioms like Savage's ensuring state-independent preferences. These dimensions intersect in rational agency: theoretical rationality informs practical by supplying accurate probabilities for calculations, as erroneous beliefs undermine action efficacy, while practical rationality tests theoretical outputs through consequential feedback. For instance, Savage's subjective expected theorem derives unique probability and functions from ordinal satisfying completeness, transitivity, and independence, bridging belief formation to choice under risk. Empirical applications reveal that while these norms prescribe ideal coherence, real-world deliberation often approximates via simplified rules, yet adherence to core axioms like transitivity prevents cycles of preference reversal observed in lab settings with as low as 10-20% violation rates in controlled experiments.

Ideal versus Bounded Rationality

Ideal rationality refers to a normative standard in where agents are assumed to possess unlimited cognitive resources, about probabilities and outcomes, and the computational ability to select the action that maximizes expected utility. This framework, formalized in the expected utility theory of and in their 1944 book Theory of Games and Economic Behavior, posits that rational choice involves calculating the sum of utilities weighted by their probabilities and choosing the option with the highest value. Under ideal conditions, deviations from this maximization are deemed irrational, as they fail to achieve the optimal outcome. Bounded rationality, introduced by Herbert A. Simon in his 1957 paper "Models of Man: Social and Rational," challenges this ideal by emphasizing empirical constraints on human cognition. Simon argued that decision-makers face limits on information acquisition, processing capacity, and time, rendering full optimization infeasible in complex environments. Instead, individuals engage in satisficing—selecting the first option that meets an acceptable threshold of aspiration levels, rather than exhaustively searching for the global maximum. This approach aligns with observed behaviors in administrative and economic settings, where computational demands exceed human faculties, as Simon demonstrated through studies of organizational decision-making. The contrast highlights a tension between normative ideals and descriptive reality: ideal rationality serves as a benchmark for economic modeling, assuming agents like who compute precisely under uncertainty. However, incorporates psychological evidence, such as cognitive heuristics and incomplete search, showing that humans achieve effective outcomes through adaptive procedures despite imperfections. Simon's in Economics in 1978 recognized this shift, influencing fields like by replacing unattainable perfection with realistic models of procedural rationality. Empirical tests, including Simon's analyses of problem-solving in chess and , confirm that bounded strategies suffice for survival and success in ill-structured problems, where ideal computation would be prohibitively costly.

Philosophical Foundations

Coherence, Reason-Responsiveness, and Goals

In philosophical accounts of rationality, coherence refers to the internal among an agent's mental states, such as beliefs, intentions, and desires, which prevents arbitrary or self-undermining attitudes. Structural rationality, often equated with coherence, imposes requirements like the enkratic principle, which prohibits intending an action while believing it unlikely to succeed without sufficient reason, as such incoherence undermines the agent's own commitments. Violations of coherence, such as forming intentions that conflict with probabilistic beliefs in a manner susceptible to Dutch books, are taken to indicate irrationality because they reflect failures in wide-scope norms that govern attitude sets holistically rather than individually. Reason-responsiveness, by contrast, characterizes rationality as sensitivity to normative reasons, where agents update beliefs or adjust actions in light of evidence or practical considerations that bear on their correctness. This view, prominent in substantive theories of rationality, holds that mere internal coherence is insufficient if attitudes fail to track external reasons; for instance, a coherent but evidence-ignoring lacks rationality because it does not respond appropriately to available information. Philosophers like Nora Heinzelmann argue that coherence accounts falter in cases where coherent mental states lead to poor outcomes, such as systematically ignoring decisive counter, whereas reason-responsiveness better captures rationality's normative force by prioritizing alignment with objective standards over mere systemic harmony. Critics of coherence, including John Broome, contend that rationality supervenes on mental states in a way that reason-responsiveness explains, as it links attitudes to broader causal and justificatory relations rather than isolated consistency. Goals integrate these elements in practical rationality, particularly through instrumental norms that demand coherence between ends and means, ensuring actions efficiently promote adopted objectives given beliefs about causal pathways. rationality requires, for example, that if an agent intends a goal like improvement, they must intend feasible means like exercise when believing it effective, avoiding "" objections where irrational means adoption inflates goal probabilities without evidential basis. However, substantive reason-responsiveness extends beyond given goals, incorporating scrutiny of ends themselves; challenges pure as a "," arguing that rationality involves responsiveness to reasons for selecting goals, not just executing them, since unreflective pursuit of arbitrary ends can conflict with broader normative demands like or . This tension highlights that while coherence ensures goal-directed consistency, reason-responsiveness demands empirical and causal alignment, preventing rationalization of flawed objectives through means-ends hygiene alone.

Internalism, Externalism, and Relativity

In the philosophy of practical rationality, internalism posits that reasons for action or belief are grounded in an agent's subjective motivational set, comprising desires, goals, commitments, and other internal psychological states accessible to the agent. This view, prominently advanced by Bernard Williams in his 1979 essay "Internal and External Reasons," maintains that rationality requires alignment with these internal factors, as external impositions disconnected from an agent's motivations fail to provide genuine normative force. Williams argued that claims of external reasons—those independent of any agent's psychology—either reduce to internal ones or lack rational authority, emphasizing that rationality cannot compel action without some motivational link, thereby avoiding the "one thought too many" problem where moral or objective demands override personal integrity. Externalism counters that rationality incorporates objective or external reasons, which exist irrespective of an agent's current motivations and may demand revision or expansion of those motivations. Proponents such as and defend this by asserting that rationality involves responsiveness to value or truth, where external reasons, like factual or imperatives, justify actions even if they conflict with subjective inclinations. For instance, Scanlon's contractualist framework holds that reasons derive from principles no one could reasonably reject, providing an external standard for rational deliberation that transcends individual psychology. Empirical support for externalism draws from , where bounded rationality models, such as those in Herbert Simon's work, incorporate environmental reliabilities and objective outcomes, suggesting that purely internal processes often yield suboptimal results without external calibration. Relativity in rationality emerges as a consequence or variant of internalism, rendering rational standards context-dependent and agent-relative rather than universally absolute. Under this perspective, what counts as rational varies with an individual's informational horizon, cultural priors, or temporal constraints, as rationality is not fixed but adaptive to the agent's situated perspective—echoing historical analyses where past rationalities, like medieval , appear irrational only retrospectively with new . Critics of strong relativity, including externalists, argue it undermines intersubjective norms, potentially excusing biases or errors as "rational for me," yet proponents like Williams contend it preserves authenticity by rejecting paternalistic universals. This debate intersects with causal realism, as externalist accounts better account for how objective causal structures, verifiable through empirical testing, constrain rational choice beyond subjective whim, though internalists prioritize causal efficacy within the agent's deliberative process.

Normativity and Foundational Debates

The normativity of centers on whether requirements of rationality—such as maintaining coherence among beliefs or intentions—impose genuine oughts or reasons upon agents to comply, beyond mere descriptive patterns of thought. This debate distinguishes rationality from hypothetical imperatives tied to goals, questioning if incurs pro tanto reasons against it irrespective of outcomes like truth or success. Proponents maintain that rationality's norms are primitive or derived from reason-responsiveness, while skeptics argue they lack independent force, often collapsing into wider substantive norms like or accuracy. Defenders of normativity, exemplified by Benjamin Kiesewetter, posit that rationality demands correct response to evidence-relative reasons, rendering structural incoherence (e.g., believing p and not-p) a guaranteed substantive that provides decisive reasons against the attitudes involved. Kiesewetter contends this holds even in permissive cases where multiple rational options exist, as undermines reason-responsiveness without exception, countering objections that coherence lacks intrinsic value. In contrast, John Broome separates rationality as a mind-supervenient property of coherence from , which incorporates external facts; for example, identical mental states yield equal rationality whether intending effective or futile means, but diverges based on objective efficacy, implying rational requirements supply no standalone reasons absent bridging norms. Foundational debates probe the grounds of any such , particularly whether it stems instrumentally from goal achievement, epistemically from truth as belief's aim, or as an irreducible constraint on agency. Epistemic variants argue rationality's oughts arise because beliefs constitutively aim at truth, yielding reasons to align with over pragmatic utility alone, though critics note this presumes truth's independent without circularity. Practical foundationalism ties to success-maximization, but faces issues where rationality endorses flawed goals without external correction; unresolved tensions persist, as empirical deviations (e.g., human boundedness) challenge ideal norms' prescriptive status without pragmatic dilution.

Historical Development

Ancient and Pre-Modern Origins

The foundations of rationality in Western thought emerged in during the 6th century BCE with the Pre-Socratic philosophers, who pioneered rational inquiry by seeking natural, non-mythological explanations for the . (c. 624–546 BCE), often regarded as the first , hypothesized water as the arche or originating principle, relying on and deduction rather than divine myths. (c. 610–546 BCE) introduced the as an indefinite boundless substance, advancing abstract reasoning about origins and change. This shift established as a discipline grounded in —reasoned discourse—over traditional storytelling. Socrates (c. 469–399 BCE) further developed rationality through the elenctic method, a dialectical process of questioning to expose inconsistencies in beliefs and pursue truth via self-examination. He claimed that "the unexamined life is not worth living," prioritizing rational virtue ethics where knowledge equates to moral goodness, as ignorance causes vice. His approach emphasized epistemic humility and the pursuit of definitions through rigorous dialogue, influencing subsequent philosophy despite leaving no writings. Plato (c. 428–348 BCE), his student, elevated dialectic as the rational path to eternal Forms, arguing in dialogues like The Republic that philosopher-rulers govern justly by intellect detached from sensory illusions. Aristotle (384–322 BCE), Plato's pupil, systematized rationality in his Organon, inventing syllogistic logic as a tool for valid inference from premises, blending empirical induction with deduction to define human flourishing (eudaimonia) as rational activity in accordance with virtue. Hellenistic Stoicism, founded by (c. 334–262 BCE), conceptualized rationality as alignment with the universal , an immanent rational order governing nature and human affairs. Stoics like (c. 279–206 BCE) taught that virtue consists in rational assent to impressions, achieving by subordinating passions to reason, as the wise person lives in harmony with cosmic necessity. Roman exponents, including Seneca (c. 4 BCE–65 CE), (c. 50–135 CE), and (121–180 CE), applied this practical rationality to ethics, emphasizing control over judgments rather than externals. In the pre-modern medieval period, integrated ancient Greek rationality with Christian theology, affirming reason's compatibility with revelation. (1225–1274 CE), drawing heavily on , argued in that human intellect can demonstrate God's existence through five rational proofs (quinque viae), such as motion and causation, while derives from eternal divine reason accessible via unaided reason. viewed faith as perfecting reason, not contradicting it, establishing a framework where rational inquiry elucidates theological truths and moral obligations, countering by insisting philosophy's autonomy in its domain. This synthesis preserved and advanced amid the era's intellectual revival.

Enlightenment and Modern Formulations

The Enlightenment, spanning roughly the late 17th to 18th centuries, elevated reason as the principal means for acquiring and directing human affairs, supplanting reliance on tradition, revelation, or arbitrary authority. advanced a foundational rational method in his 1637 , employing systematic doubt to discard all beliefs susceptible to error, thereby establishing indubitable truths such as the cogito (", therefore I am") through clear and distinct perceptions, which he deemed the hallmark of rational certainty. This approach prioritized modeled on , positing that rationality involves methodical analysis to build from self-evident foundations. John Locke, in his 1690 Essay Concerning Human Understanding, countered rationalist innate ideas with an empiricist framework, arguing that the mind begins as a tabula rasa (blank slate) and that all knowledge derives from sensory experience processed by reason. Locke viewed rationality as the faculty enabling reflection on empirical data to form ideas, distinguish primary qualities (inherent properties like shape) from secondary ones (observer-dependent, like color), and thereby achieve probable truths in a probabilistic world. David Hume extended this empiricism skeptically in his 1739–1740 A Treatise of Human Nature, contending that reason alone cannot motivate action or establish causal necessities, serving instead as "the slave of the passions" by calculating means to desire-driven ends, thus limiting rationality to instrumental efficacy rather than independent normativity. Immanuel Kant, responding in his 1781 Critique of Pure Reason, synthesized rationalism and empiricism by delineating pure reason's capacity for a priori synthetic judgments (e.g., space and time as forms of intuition), while delimiting its bounds to avoid metaphysical overreach, such as antinomies arising from unaided speculation. Kant thereby formulated rationality as structured by innate categories of understanding applied to phenomena, but incapable of knowing things-in-themselves. In 19th-century modern formulations, rationality increasingly intertwined with ethical and social calculation, as seen in Jeremy Bentham's , outlined in his 1789 An Introduction to the Principles of Morals and Legislation, which defined rational action as maximizing aggregate pleasure minus pain via a "hedonic calculus" assessing intensity, duration, and other factors of consequences. Bentham's approach treated rationality as a quantitative, impartial for and individual choice, influencing legal reforms by prioritizing measurable utility over intuition or custom. John Stuart Mill refined this in his 1861 Utilitarianism, distinguishing higher intellectual pleasures from base ones and arguing that rationality involves cultivating faculties for qualitatively superior ends, thereby elevating beyond mere to a rule-guided pursuit of long-term societal welfare. These developments framed rationality as outcome-oriented , bridging philosophical inquiry with practical applications in and , though critics noted their vulnerability to aggregating individual utilities without regard for rights or justice.

20th-Century Advances and Key Thinkers

The formalization of rational choice in advanced significantly in the mid-20th century through the work of and , who in their 1944 book Theory of Games and Economic Behavior introduced expected utility theory and game-theoretic frameworks for analyzing strategic interactions under . Their von Neumann-Morgenstern utility theorem established axioms for rational preferences, positing that agents maximize expected utility based on probabilistic outcomes, which became foundational for models of rationality. This approach emphasized logical consistency in choices, influencing fields from to military strategy by providing mathematical tools to predict behavior in competitive settings. Herbert Simon challenged the assumption of perfect rationality in his 1955 paper "A Behavioral Model of Rational ," introducing the concept of to account for cognitive limitations, incomplete information, and time constraints faced by decision-makers. Simon argued that humans "satisfice"—selecting satisfactory rather than optimal options—due to these bounds, drawing from empirical observations in and research. His work, recognized with the 1978 in , shifted focus toward procedural rationality, where decision processes adapt to real-world constraints rather than idealized optimization, laying groundwork for . In philosophy of science, Karl Popper advanced critical rationalism in The Logic of Scientific Discovery (1934, English edition 1959), proposing falsifiability as the demarcation criterion for rational scientific theories, rejecting inductive verification in favor of bold conjectures tested through potential refutation. Popper viewed rationality not as probabilistic confirmation but as openness to criticism and error elimination, critiquing historicist and psychoanalytic approaches for lacking empirical testability. This framework influenced epistemological debates by prioritizing causal mechanisms and empirical disconfirmation over consensus or authority. Bayesian approaches to rationality gained traction later in the century, building on earlier probability work by incorporating subjective priors updated via evidence, as formalized in extensions by figures like Leonard Savage in The Foundations of Statistics (1954). These methods modeled rational as conditionalization, applied in and to handle more flexibly than frequentist alternatives.

Contemporary Developments (2000–Present)

The early saw the emergence of an rationality community dedicated to systematizing techniques for overcoming cognitive biases and applying probabilistic reasoning to everyday and high-stakes decisions. Blogs such as Overcoming Bias, initiated in 2007 by economist and researcher , examined topics like prediction markets, , and signaling, laying groundwork for broader discourse on instrumental rationality. This evolved into the platform, launched in 2009, which centralized Yudkowsky's "Sequences"—a series of essays on Bayesian , pitfalls, and maximization—drawing thousands of participants to refine practical rationality tools. The Sequences, distilled into the 2015 compilation Rationality: From AI to Zombies, emphasized updating beliefs via evidence and avoiding fallacies like conjunction or , influencing subsequent workshops and organizations aimed at debiasing. Concurrently, rationality principles informed the movement, which prioritizes evidence-based interventions to maximize welfare outcomes. Organizations like , established in 2007, pioneered cost-effectiveness analyses of charities using randomized controlled trials and quality-adjusted life years metrics, directing donations toward high-impact causes such as prevention over less efficacious ones. This approach, overlapping with LessWrong's focus on quantification, spurred quantitative frameworks for , including cause prioritization via neglectedness, tractability, and scale assessments, as articulated in community resources from the onward. Critics within and outside the movement noted potential overemphasis on measurable metrics at the expense of unquantifiable goods, yet empirical evaluations demonstrated superior impact compared to intuitive giving. In cognitive and decision sciences, Daniel Kahneman's (2011) synthesized and dual-process models, highlighting how intuitive thinking deviates from Bayesian ideals under , while advocating reflective System 2 interventions for better calibration. This built on Kahneman's 2002 , spurring applications in policy nudges and forecasting tournaments, such as those by (2011–2015), which trained participants in probabilistic aggregation to outperform intelligence analysts by 30% in accuracy. Advances in Bayesian computation, including methods refined in the 2000s, enabled practical implementation of subjective priors in fields from to , though debates persisted on prior selection's subjectivity. Philosophically, explorations of structural rationality—coherence constraints like enkratic norms requiring action alignment with beliefs—gained prominence, distinguishing intra-personal consistency from external reason-responsiveness in works from the .

Empirical Foundations in Cognitive Science

Psychological Mechanisms and Heuristics

Human cognition employs psychological mechanisms that enable efficient under constraints of limited , time, and computational capacity, often diverging from idealized models of rationality such as expected utility maximization. Herbert Simon introduced the concept of in 1957, arguing that individuals satisfice—select the first acceptable option rather than optimizing—due to cognitive limitations and environmental complexity, as evidenced by empirical studies of problem-solving in organizations where decision-makers rely on simplified representations rather than exhaustive search. This framework, supported by Simon's Nobel lecture observations of real-world administrative choices, contrasts with unbounded rationality assumptions by highlighting how humans approximate optimal behavior through procedural mechanisms like search heuristics. A prominent model distinguishing these mechanisms is Daniel Kahneman's dual-process theory, delineating (intuitive, automatic, heuristic-driven) from System 2 (deliberative, effortful, rule-based), where System 1 predominates in everyday judgments but introduces systematic errors when environments mismatch evolved adaptations. Kahneman and Amos Tversky's heuristics-and-biases program, initiated in the 1970s, demonstrated through experiments that reliance on heuristics like availability, representativeness, and anchoring produces predictable deviations from probabilistic norms. For instance, in their 1974 review, they showed how these shortcuts lead to overconfidence in subjective probabilities, with empirical tasks revealing biases in frequency estimation and risk assessment across diverse samples. The availability heuristic involves assessing event likelihood based on the ease of retrieving examples from memory, often overweighting salient or recent instances. In Tversky and Kahneman's 1973 study, participants overestimated the probability of causes of death matching vivid media portrayals (e.g., accidents over diseases) because recall fluency biased judgments away from base rates, with correlations between retrieval latency and perceived frequency confirming the mechanism's causal role. Similarly, the representativeness heuristic prompts evaluations by prototype similarity, neglecting base-rate probabilities; the conjunction fallacy, where "Linda is a bank teller and feminist" was deemed more probable than "Linda is a bank teller" alone, occurred in over 80% of respondents in their experiments, violating logical probability axioms. Anchoring and adjustment, another core heuristic, occurs when an initial value () influences subsequent estimates despite irrelevance, with insufficient adjustment yielding persistent . Tversky and Kahneman's wheel-of-fortune experiments, where arbitrary numbers (e.g., 10 or 65 spun randomly) anchored UN member estimates from (averaging 25% vs. 45% across conditions), demonstrated effect sizes persisting across numerical and verbal tasks, corroborated by meta-analyses aggregating 96 studies showing median shifts of 20-30% toward anchors. , a mechanism favoring hypothesis-confirming evidence, manifests in selective search; Peter Wason's 1968 selection task, requiring cards to verify "if vowel then even number," saw only 10% correct abstract solutions (falsifying via vowel-odd pairs) versus higher rates in social rule scenarios (e.g., drinking-age ), attributing failures to confirmatory tendencies over falsification. These heuristics, while ecologically rational in resource-scarce ancestral environments by enabling rapid approximations, undermine rationality in modern, statistically demanding contexts, as resource-rational analyses model as optimizing under capacity bounds rather than error-free computation. Empirical links biases to activation in framing effects, suggesting emotional underpinnings amplify heuristic dominance over analytical override. Interventions like debiasing prompts activate System 2 to mitigate errors, though base tendencies persist, informing cognitive science's view of rationality as contextually adaptive rather than absolute.

Role of Emotions, Intuition, and Evolutionary Constraints

Emotions play a functional role in human decision-making, serving as somatic markers that guide choices toward adaptive outcomes, particularly under uncertainty. Antonio Damasio's somatic marker hypothesis posits that emotional signals, generated through bodily responses, facilitate rapid evaluation of options by associating past experiences with anticipated somatic states of reward or punishment. Patients with damage to the ventromedial prefrontal cortex (vmPFC), who retain logical reasoning capacity but lack these emotional markers, exhibit impaired real-world decision-making, often selecting high-risk options despite understanding probabilities, as demonstrated in studies using the Iowa Gambling Task where such individuals fail to avoid disadvantageous decks over repeated trials. This evidence indicates that emotions integrate with cognition to constrain exhaustive deliberation, promoting efficiency in environments where full information is unavailable, though unchecked affective states can introduce biases like loss aversion, where individuals overweight potential losses by factors of 2-2.5 times gains in prospect theory experiments. Intuition operates as a fast, associative process complementary to deliberate reasoning, often yielding accurate judgments in domains matching evolved expertise. In dual-process theories, thinking—characterized by automatic, intuition-driven operations—involves emotional and inputs that enable quick responses honed by experience, as seen in expert chess players who achieve 90% accuracy in intuitive position evaluations versus novices' reliance on slower . Empirical studies, such as those on firefighters' "" under stress, show intuitive decisions outperforming analytical ones in time-pressured scenarios, with recognition-primed models explaining success rates up to 80% through from prior exposures. However, intuition falters in novel or statistically complex tasks, as evidenced by base-rate neglect in probabilistic judgments, where participants ignore prior probabilities despite explicit data, committing errors in 60-70% of cases across variants of the lawyer-engineer problem. Evolutionary constraints shape these mechanisms, as human cognition adapted to ancestral environments rather than modern abstract rationality. and John Tooby's framework argues that the mind comprises domain-specific adaptations, such as cheater-detection modules evolved for social exchange, enabling near-ceiling performance (70-90% accuracy) on Wason selection tasks reframed as detecting violators in scenarios, compared to 20-30% failure rates in neutral logical versions. This mismatch explains persistent biases: heuristics like availability, prioritizing vivid events over frequencies, conferred survival advantages in small-scale groups with correlated cues (e.g., rare dangers signaled by immediate threats) but lead to overestimation of low-probability risks, such as airplane crashes versus car accidents, by orders of magnitude in contemporary surveys. While these evolved shortcuts underpin , they impose inherent limits on Bayesian updating or infinite computation, as neural architectures prioritize energy-efficient approximations over optimality, with metabolism constraining deliberation to seconds rather than exhaustive search.

Key Empirical Studies on Human Decision-Making

One foundational empirical demonstration of deviations from expected utility theory came from Maurice Allais's 1953 experiments, where participants faced paired lotteries revealing inconsistent preferences that violated the independence axiom. In the first pair, most chose a certain $1 million over a gamble offering 10% chance of $5 million, 89% chance of $1 million, and 1% chance of nothing; in the correlated second pair, however, participants preferred an 11% chance of $5 million (with 89% nothing) over a 10% chance of $5 million (with 90% nothing), implying in gains but risk-seeking in losses when common outcomes are removed. These results, replicated in subsequent studies, highlighted certainty effects and non-linear weighting of probabilities, challenging the descriptive accuracy of rational choice models. Daniel Kahneman and Amos Tversky's 1979 , developed through experiments with over 300 participants, provided an alternative model explaining such anomalies via a value function that is concave for gains () and convex for losses (risk seeking), combined with where losses loom larger than equivalent gains (coefficient around 2.25). Empirical tests showed subjects overweighting low probabilities and underweighting high ones, with choices like preferring a sure $3,000 over an 80% chance of $4,000 ( $3,200), yet favoring an 85% chance of $4,000 over a sure $3,000 when framed similarly. This framework, supported by diverse lottery tasks, accounted for observed behaviors better than expected utility, influencing fields like despite critiques of parameter stability in some replications. Framing effects, empirically documented by Tversky and Kahneman in 1981, further illustrated context-dependent rationality lapses, as identical outcomes phrased as gains versus losses reversed preferences. In the "Asian disease" scenario, 72% chose a program saving 200 out of 600 lives certainly over one saving 600 with one-third probability, but when framed as deaths (400 die certainly vs. two-thirds die with one-third none die), only 22% stuck with the certain option, with 78% opting for the risky frame despite mathematical equivalence. These shifts, observed across medical and economic hypotheticals, implicated prospect theory's reference dependence, with later linking them to activation for emotional mediation. Peter Wason's 1966 selection task experiments revealed profound failures in , with only about 10% of participants correctly identifying cards to falsify a conditional rule like "if then even number" by selecting and odd-number cards, instead favoring confirmatory checks. Meta-analyses of over 200 studies confirm this low baseline performance in abstract forms, improving to 70-90% in pragmatic versions, suggesting evolved domain-specific reasoning rather than general logic application. Raymond Nickerson's 1998 review synthesized evidence for , where individuals disproportionately seek or interpret confirming evidence for hypotheses, as in Wason's 1960 belief-bias tasks where subjects tested stereotypes by querying confirming instances over disconfirming ones. Experiments across clinical, scientific, and everyday judgments showed this bias persisting even among experts, with rates of disconfirmation-seeking below 20% in neutral tasks, attributable to cognitive economy and motivational factors rather than pure irrationality. Such patterns underscore , where shortcuts prioritize efficiency over exhaustive verification.

Formal Models and Applications

Decision Theory and Bayesian Approaches

Decision theory formalizes rational choice under uncertainty by positing that agents select actions to maximize expected utility, defined as the sum of each possible outcome's utility weighted by its probability. This framework originated with and Oskar Morgenstern's 1944 axiomatization, which demonstrated that preferences satisfying completeness, transitivity, continuity, and over lotteries can be represented by a utility function where choices maximize the expected value of outcomes. Their work in Theory of Games and Economic Behavior established expected utility as a normative standard for decisions involving risk, assuming objective probabilities are known. Leonard J. Savage extended this in 1954 by deriving subjective expected utility (SEU) theory, incorporating personal probabilities when objective data are unavailable. Savage's axioms—requiring preferences over acts in states of the world to satisfy certain postulates like the —yield both a utility function over consequences and a unique subjective , enabling rational decisions via SEU maximization. This subjective Bayesian foundation treats beliefs as probabilities elicited from betting behavior, linking to probabilistic reasoning without relying on frequencies. Bayesian approaches integrate these elements by prescribing that rational beliefs, represented as credences or degrees of belief, conform to the and update via : the of a given evidence equals the likelihood of the evidence under the times the of the , normalized by the marginal probability of the evidence. In normative terms, this diachronic rule ensures coherence in , preventing issues like Dutch books—sets of bets that guarantee loss for inconsistent probabilities. Bayesian thus combines SEU with probabilistic updating, advocating actions that maximize expected utility relative to current credences, as a benchmark for rationality despite empirical deviations in .

Game Theory and Strategic Rationality

Game theory formalizes strategic rationality as the process by which rational agents select actions to maximize their expected payoffs, accounting for interdependent outcomes in multi-agent settings. Unlike single-agent , strategic rationality requires anticipating opponents' responses, often under assumptions of of rationality—wherein all players know that others are rational, know that this knowledge is shared, and so on. This framework posits that rational players employ best-response strategies, iteratively refining choices to eliminate dominated options until reaching stable profiles. The foundational text, Theory of Games and Economic Behavior by and , published in 1944, introduced rigorous mathematical models for zero-sum games, where one player's gains equal another's losses. proves that in such games, a rational player can guarantee an optimal value by choosing a mixed strategy that minimizes maximum potential loss, assuming the opponent acts adversarially. This work shifted economic analysis from individualistic utility maximization to interactive equilibrium concepts, influencing fields like during . John Nash extended these ideas in his 1950 doctoral dissertation and subsequent paper, defining the for general-sum noncooperative games: a profile where no player can improve their payoff by unilaterally changing , given others' fixed choices. Nash proved the existence of at least one equilibrium in finite games using mixed , providing a benchmark for strategic rationality that generalizes beyond zero-sum conflicts. This concept underpins predictions in auctions, oligopolies, and , though multiple equilibria can complicate unique solutions without additional refinements like subgame perfection. Illustrative of strategic rationality's implications, the models a two-player game with payoffs structured such that mutual yields higher joint than mutual , yet dominates as the individually rational choice under about the other's action. Formulated in the 1950s by Merrill Flood and Melvin Dresher, with dilemma formalized by Albert Tucker, it demonstrates how rational can lead to Pareto-inferior outcomes, challenging assumptions of between individual and collective rationality in non-repeated interactions. Experimental replications, such as those by and Albert Chammah in 1965 involving over 700 iterations, confirm that while single-shot play often results in , repeated play fosters via strategies like tit-for-tat, aligning with evolutionary models of reciprocity. Critiques of unbounded strategic rationality highlight its dependence on precise information and computational feasibility; real agents often deviate due to cognitive limits, as evidenced by extensions in Herbert Simon's work, yet remains prescriptive for ideal rational play in strategic contexts. Applications persist in , where equilibria inform incentive-compatible rules, such as Vickrey auctions ensuring truth-telling as dominant strategies.

Economic Models: Rational Choice versus Bounded Variants

Rational choice theory in posits that individuals act as rational agents who select options to maximize their expected , assuming complete and transitive preferences, full about alternatives and consequences, and the ability to compute optimal solutions. This framework underpins neoclassical models, such as consumer demand theory and general equilibrium analysis, where agents respond predictably to price signals and incentives to achieve . In contrast, , introduced by Herbert Simon in his 1947 book , recognizes that decision-makers operate under constraints including limited cognitive capacity, incomplete information, and finite time for deliberation, leading them to pursue "" behaviors—selecting satisfactory rather than globally optimal outcomes. Simon, who received the Nobel Prize in Economics in 1978 partly for this concept, argued that real-world choices involve procedural heuristics and approximations rather than exhaustive optimization, as evidenced by organizational decision processes where managers rely on routines and rules of thumb. The core divergence lies in optimization versus adaptation: rational choice assumes hyper-rationality with stable utility functions, enabling predictive models like expected utility theory formalized by von Neumann and Morgenstern in 1944, while bounded rationality incorporates psychological realism, predicting deviations such as or observed in experimental settings. Empirical studies, including those by Kahneman and Tversky in the 1970s demonstrating violations of expected utility via , provide evidence against pure rational choice, showing systematic errors in under . For instance, the (1953) revealed inconsistencies in preferences that rational choice cannot accommodate without ad hoc adjustments, supporting bounded models where agents use heuristics like or anchoring. Critics of rational choice highlight its descriptive inaccuracies, as human behavior often fails to align with its assumptions during events like the , where overconfidence and herd effects—unaccounted for in standard models—amplified market failures. addresses these by integrating cognitive limits into economic modeling, with applications in behavioral , such as explaining sticky prices or boundedly rational competition where firms use simple rules rather than Nash equilibria. However, proponents defend rational choice as a normative benchmark or approximation, noting that bounded variants, while empirically richer, complicate formal analysis and prediction, as seen in the persistence of rational models in despite behavioral critiques. In policy contexts, bounded rationality informs interventions like default options in retirement savings plans, which exploit to improve outcomes without assuming perfect rationality, as demonstrated in field experiments yielding 30-60% higher participation rates. Ultimately, while rational choice excels in theoretical tractability, better captures causal mechanisms of under real constraints, though integrating the two remains an active research frontier.

Rationality in Artificial Intelligence

Design of Rational AI Agents

Rational AI agents are computational entities designed to perceive their environment through sensors and select actions via actuators to maximize a specified performance measure, particularly under . This design paradigm, central to , equates rationality with achieving the optimal expected outcome based on available information, as formalized in decision-theoretic terms. Unlike agents constrained by cognitive limits, rational AI agents prioritize consistency in reasoning from percepts to actions, avoiding inconsistencies that arise from incomplete knowledge or resource bounds unless explicitly modeled. The foundational architecture for such agents, as outlined in standard AI frameworks, involves defining the task environment via the PEAS descriptor: the performance measure quantifies success (e.g., points scored in a ), the environment specifies properties like and , actuators enable interaction (e.g., motors or software commands), and sensors provide perceptual input (e.g., cameras or data streams). Environments are classified by attributes such as full versus partial , (where outcomes are fixed given actions) versus stochasticity, episodicity (independent episodes) versus sequential dependencies, static (unchanging during deliberation) versus dynamic, discreteness versus continuity, and single-agent versus multi-agent or . Rational design tailors the agent function—mapping percept histories to actions—to these properties; for instance, partially observable stochastic environments demand model-based representations of hidden states to compute expected utility. Agent structures progress in complexity to approximate rationality: simple reflex agents react to current percepts via condition-action rules, suitable for fully observable, deterministic settings; model-based reflex agents maintain an internal state model to infer unperceived aspects; goal-based agents search for action sequences achieving explicit objectives; and utility-based agents, the pinnacle for full rationality, employ a utility function assigning real-valued preferences to world states, selecting actions that maximize expected utility sum over future states. Utility functions resolve trade-offs, such as preferring a quicker but riskier path over a safer longer one if the expected value aligns with the performance measure, and handle multi-objective scenarios via scalarization or lexicographic ordering. Learning agents extend this by adapting utility estimates or models from experience, incorporating a critic to evaluate performance against a baseline and a problem generator for exploration. In implementation, rational agent design integrates algorithms for (e.g., probabilistic filtering for state estimation), reasoning (e.g., Markov decision processes for sequential decisions under ), and (e.g., policy iteration to derive optimal action mappings). Computational intractability in complex environments—such as the exponential state space in partially observable Markov decision processes—necessitates approximations, including heuristic search, value function approximation via temporal-difference learning, or hierarchical decomposition, while preserving asymptotic rationality where resources permit exact computation. This approach underpins applications like autonomous robotics, where agents navigate dynamic spaces by maximizing derived from and predictive models, and game-playing systems, evidenced by AlphaGo's 2016 victory over human champions through approximating expected in vast game trees.

AI Overcoming Human Bounded Rationality

AI systems address human by exploiting scalable computational resources to perform exhaustive evaluations, simulations, and optimizations that exceed human cognitive limits in time, memory, and information processing. Herbert Simon's framework posits that humans satisfice due to incomplete information and computational constraints, but AI circumvents these through parallel processing and algorithmic approximations, enabling near-optimal decisions in high-complexity domains. In strategic games, AI demonstrates this capability vividly. The game of Go, with approximately 10^170 legal positions, overwhelms human lookahead depth, yet , developed by DeepMind, integrated with deep neural networks to defeat world champion 4-1 in March 2016, computing evaluations across billions of simulated outcomes per move—feats unattainable by human players bounded by selective heuristics. Subsequent iterations like , released in 2017, self-learned Go mastery from scratch in 24 hours using , surpassing prior AI and human benchmarks without domain-specific human knowledge, thus unbounding search limitations inherent to organic cognition. Beyond games, AI extends rationality in organizational and strategic decision-making by aggregating and analyzing vast datasets to mitigate informational bounds. Studies show AI-assisted systems enable leaders to approximate full rationality, processing multivariate scenarios and probabilistic forecasts that humans approximate via biases or shortcuts; for example, models in solve NP-hard problems for real-world instances with millions of variables, yielding solutions 10-20% more efficient than human-planned alternatives in empirical tests from 2020-2023. In hybrid human-AI frameworks, such as those employing unfolding rationality, AI compensates for human search constraints by generating expansive option sets, improving outcomes in uncertain environments like financial forecasting, where AI ensembles reduced error rates by up to 15% over human analysts in controlled experiments. However, AI's overcoming of remains domain-specific and reliant on quality data and model architecture; while excelling in structured, quantifiable tasks, it may propagate secondary bounds from biases or fail in , causal inference-heavy contexts requiring human-like , as evidenced by persistent gaps in open-ended benchmarks through 2024.

Recent Advances and Challenges (2023–2025)

In 2023–2025, advances in AI rationality centered on enhancing probabilistic reasoning and agentic architectures to approximate perfect rationality, defined as maximizing expected utility under . Researchers developed prior-fitted neural networks (PFNs) that amortize by learning task-specific priors during pre-training, reducing computational costs for posterior sampling and improving predictive accuracy over traditional methods. This approach leverages scaling in GPU efficiency to enable scalable Bayesian prediction, allowing AI agents to better handle in tasks. Concurrently, integration of Bayesian reasoning into generative AI models introduced calibrated estimation, mitigating overconfidence in outputs and enhancing by enabling models to "doubt" unreliable predictions. AI systems have demonstrated potential to overcome human , Herbert Simon's concept of decision constraints due to limited and . Studies in organizational argue that large language models (LLMs) and autonomous agents unbound rationality by processing vast datasets and simulating unbounded , shifting paradigms from to optimizing in complex environments like and decisions. For instance, AI-driven frameworks in 2024–2025 have augmented human-AI hybrid systems, where LLMs extend cognitive limits by generating diverse scenarios and evaluating utilities beyond human capacity. Agentic AI, emphasizing autonomous and tool use, advanced through multimodal architectures that incorporate reasoning chains, enabling decomposition of tasks into rational sub-steps. Challenges persist in measuring and ensuring consistent rationality, as AI often exhibits irrationality mirroring human biases, such as base-rate neglect or conjunction fallacies, despite superior computational power. A 2025 survey identifies open questions in defining AI rationality—drawing from economic perfect rationality versus behavioral variants—and notes systemic issues like training data biases leading to non-Bayesian updating. Rational agents falter under incomplete , with real-world deployments revealing fragility in adversarial settings or long-horizon , where error propagation undermines maximization. Security vulnerabilities in agentic systems, including prompt injection and unaligned goals, further complicate rational deployment, as agents may pursue mis-specified objectives. Evaluations emphasize prioritizing rationality over raw , with benchmarks showing frontier models succeeding on IQ-like tasks but failing probabilistic tests, underscoring the need for hybrid human oversight.

Paradoxes and Inherent Limitations

Classic Paradoxes of Rationality

The , introduced by Nicolaus Bernoulli in a 1713 letter to Pierre Raymond de Montmort and later analyzed by in 1738, involves a hypothetical game where a fair coin is flipped until tails appears, paying $2^k where k is the number of heads before the first tail; the is infinite (sum_{k=1}^∞ (1/2)^k * 2^k = ∞), yet empirical willingness to pay for entry is finite, typically under $10 even in controlled studies. This challenges the normative use of expected monetary value in rational decision-making, as or logarithmic utility functions (proposed by Bernoulli) resolve it by bounding utility growth, aligning predictions with observed behavior where rare large payoffs fail to outweigh probable small losses over finite trials. The , formulated by in 1953, exposes inconsistencies in preferences under risk that violate the independence axiom of expected utility theory, which posits that preferences should remain invariant when a constant outcome is added to all lotteries with equal probability. In one pair of choices, most participants prefer a certain $1 million (lottery A) over a 89% chance of $1 million, 10% chance of $5 million, and 1% chance of $0 (lottery B), reflecting certainty effect. In a correlated pair, they then prefer an 11% chance of $1 million (lottery C) over a 10% chance of $1 million (lottery D, with 90% chance of $0 and implicit 0% certainty adjustment), implying a reversal inconsistent with expected utility calculations assuming von Neumann-Morgenstern axioms. Empirical replications, including Allais's original surveys and subsequent experiments, confirm this common consequence effect persists across cultures and stakes, suggesting descriptive models must incorporate probability weighting or rather than assuming linear utility invariance. The , developed by in 1961 through hypothetical experiments, demonstrates where decisions deviate from subjective expected utility by distinguishing known risks from unknown probabilities. Consider two s: one with 50 red and 50 black balls (known), the other with 90 balls of unknown red-black composition (ambiguous); participants typically prefer betting on red from the known urn over the ambiguous one for gains, and conversely prefer ambiguous for losses, violating the as the ambiguous urn's should equal the known under Bayesian updating. Laboratory tests, such as those varying ball counts or real payoffs, replicate this in over 80% of subjects, attributing it to where incomputable probabilities reduce perceived value, prompting non-Bayesian models like maxmin expected utility to capture empirical caution toward unquantifiable risks. Newcomb's paradox, posed by William Newcomb in 1960 and popularized by in 1969, pits against in a predictor scenario: a reliable predictor (accurate 90-100% historically) fills box A with $1 million if it foresees one-boxing (taking only the opaque box B promising $1 million) or leaves it empty for two-boxing (taking B plus transparent empty box A with $1,000); rational agents must choose despite dominance arguing for two-boxing (extra $1,000 regardless of prediction) conflicting with one-boxing's empirical success. Philosophical analyses and agent simulations show one-boxers outperform two-boxers in repeated plays when predictability holds, as causal theories ignore evidential correlations from acausal influences like timeless decision theory, while empirical data from (e.g., 60-70% one-box in surveys) favors evidential approaches for predictive accuracy over causal dominance. These paradoxes collectively reveal that axiomatic rationality—whether expected utility, dominance, or Bayesianism—often prescribes actions diverging from adaptive outcomes, spurring frameworks like Herbert Simon's , where computational limits and ecological frequencies prioritize effective heuristics over idealized consistency.

Critiques from Realism and Evolutionary Perspectives

From an evolutionary standpoint, human processes are adaptations honed by for ancestral environments characterized by immediate survival threats, social coordination, and resource scarcity, rather than for abstract probabilistic reasoning or global utility optimization. These adaptations favor fast, domain-specific heuristics—such as recognition-based choices or frequency judgments—over computationally intensive algorithms assumed in rational choice theory (RCT), as the latter would impose prohibitive metabolic and temporal costs unfit for contexts. has argued that evolutionary enables targeted cognitive adjustments without overhauling entire mental architectures, rendering RCT's postulate of consistent maximization implausible, since selection pressures prioritize ecological fitness over theoretical coherence. Such evolutionary constraints manifest in persistent biases, like base-rate neglect or overreliance on availability heuristics, which deviate from Bayesian norms but enhanced in opaque, nonstationary environments where full information processing was rare. Critics contend that normative models of rationality, by against idealized optimization, mischaracterize these traits as flaws rather than contextually rational solutions; for instance, simple heuristics often outperform complex models in uncertain, real-world tasks due to their robustness to noise and sparsity of data. Empirical studies in support this by demonstrating that nonhuman primates exhibit analogous choice patterns under resource limits, suggesting deep phylogenetic roots incompatible with unbounded rationality assumptions. Realist critiques, emphasizing observable causal structures over axiomatic ideals, challenge rationality models for abstracting away empirical limits on information acquisition, computational capacity, and foresight. Herbert Simon's framework posits that agents, facing irreducible uncertainties and cognitive bounds, engage in —selecting adequate options rather than exhaustively optimizing— as the realistic response to complex environments where perfect calculation exceeds human faculties. This view, grounded in administrative and organizational data from the mid-20th century, reveals how RCT's overlooks procedural realities, such as aspiration levels and search termination rules, leading to predictions mismatched with observed behaviors in firms and markets. Integrating realism with evolutionary insights, these critiques highlight that rationality doctrines often fail causal tests: evolutionary history imposes non-erasable heuristics that satisfice under realistic constraints, yet RCT persists by retrofitting data to functions without falsifiable mechanisms for formation. Experimental , including failures of incentive-compatible elicitations to align choices with predicted optima, underscores this disconnect, as real agents navigate causal webs shaped by and environment, not disembodied logic. Proponents of ecological rationality counter that "irrational" labels stem from decontextualized lab paradigms, advocating instead for evaluations against actual task structures where evolved processes prove superior.

Risks of Over-Emphasizing Rationality

Over-emphasizing rationality can precipitate , wherein excessive deliberation on options inhibits decisive action, as the aversion to imperfect choices overrides the benefits of prompt resolution. This occurs particularly when decision-makers prioritize exhaustive probabilistic evaluation over feasible approximations, leading to delayed or foregone opportunities in time-sensitive contexts. Empirical studies in decision psychology link this to heightened anxiety and reduced efficacy, with overthinkers experiencing stalled progress even on routine tasks. In environments of uncertainty, rigid rational frameworks often falter against adaptive heuristics, which exploit environmental structures for superior real-world performance. Gerd Gigerenzer's research on "ecological rationality" shows that simple rules, such as the recognition heuristic—selecting familiar options—outperform complex Bayesian models in predicting outcomes like soccer match results or stock selections, as they avoid overfitting to noisy . Insisting on unbounded rationality, unconstrained by cognitive limits or informational scarcity, yields strategies vulnerable to model errors, as heuristics align better with actual decision ecologies where full information is absent. Overreliance on rational predictive models heightens exposure to rare, consequential events termed "black swans" by , which defy Gaussian assumptions and historical extrapolation. Taleb documents how financial institutions, wedded to variance-based risk metrics like those in the Black-Scholes formula, incurred massive losses during the 2008 crisis, as these tools systematically underestimate tail risks and promote overleveraging under illusory stability. Such modeling fosters systemic fragility by encouraging interventions that amplify volatility, as seen in value-at-risk practices that mask non-linear shocks. Hyper-rational approaches risk emotional atrophy, diminishing interpersonal bonds and by sidelining affective cues essential for social navigation. Individuals exhibiting hyper-rational traits often appear detached or arrogant, straining relationships through dismissal of intuitive or value-based inputs. This can erode purpose and , as over-rationalization reframes as solvable puzzles, foreclosing engagement with irreducible uncertainties in or . Critiques highlight that pure rationality neglects substantive ends, echoing Max Weber's distinction between zweckrationalität (means-oriented) and wertrationalität (value-oriented) action, where the former dominates at the expense of moral coherence.

Societal and Ethical Dimensions

Importance for Individual Agency and Markets

Rational decision-making empowers individuals to exercise greater agency by systematically evaluating options against personal objectives, thereby reducing susceptibility to cognitive distortions such as or overconfidence, which empirical studies link to suboptimal life outcomes. For instance, research demonstrates that choices aligning closely with expected maximization elicit heightened perceptions of and , reinforcing autonomous over passive reactivity. This capacity for deliberate choice is foundational to achieving sustained personal goals, as evidenced by analyses showing that rationality-integrated yields superior strategic decisions in complex environments compared to reliance on either alone. In professional and entrepreneurial contexts, rationality facilitates agency by enabling and , which correlate with higher success rates in attainment; for example, physicians' rational clinical decisions account for approximately 80% of healthcare expenditures, underscoring how individual rationality scales to influence resource stewardship and health outcomes. Longitudinal further reveal that agents employing evidence-based reasoning in everyday scenarios exhibit improved adaptability and reduced error rates, fostering resilience against . Within markets, rationality among participants drives efficient and , as self-interested agents incorporating available into utility-maximizing behaviors aggregate to produce welfare-enhancing equilibria, per foundational economic modeling. The efficient markets hypothesis posits that rational investors rapidly assimilate new data, minimizing opportunities and promoting capital flows toward productive uses, with deviations from this ideal—often behavioral—leading to inefficiencies like bubbles or mispricings. Empirical validations of rational choice frameworks show that even imperfect approximations of rationality suffice for markets to self-correct via , yielding societal benefits beyond individual gains, such as innovation incentives and consumer surplus. Thus, cultivating rationality bolsters market resilience, as institutional designs rewarding foresight—e.g., through transparent —amplify agency in economic coordination.

Rationality in Politics, Law, and Policy

In political decision-making, rationality is undermined by voter ignorance and systematic biases, leading to suboptimal outcomes. Economist argues that while voters act rationally in the sense of not investing in costly information acquisition due to their negligible impact on elections, this results in "rational irrationality" where biases such as anti-market sentiments, pessimism about , and preference for prevail, causing democracies to favor inefficient policies like trade barriers and excessive regulation. Empirical surveys reveal profound political ignorance; for instance, approximately 50% of cannot identify the three branches of , and only about 15% can name the Chief Justice of the , limiting the electorate's capacity for informed rational choice. Public choice theory further elucidates government failures arising from self-interested behavior among politicians and bureaucrats, akin to market failures but without competitive pressures to correct them. Examples include through subsidies that benefit concentrated interests at diffuse public expense, such as quotas which impose annual costs of over $2 billion on consumers while aiding a small number of producers, and where legislators trade votes for pork-barrel projects, distorting away from broader welfare maximization. These dynamics highlight how institutional incentives prioritize short-term gains over long-term rational efficiency, often exacerbated by ideological commitments that override in policy formulation. In , rational choice theory provides a framework for analyzing how legal rules influence behavior by assuming individuals maximize utility subject to constraints, enabling predictions about compliance, deterrence, and optimal rule design. For example, it underpins economic analyses of and law, where liability rules are evaluated for incentivizing efficient precautions, as seen in the Coase theorem's implications for property rights allocation regardless of initial entitlements when transaction costs are low. Max Weber's concept of complements this by describing modern governance as legitimized through impersonal rules and bureaucratic hierarchies, where authority derives from enacted laws rather than or , facilitating predictable administration but risking rigidity and goal displacement in practice. Policy-making strives for rationality through tools like cost-benefit analysis (CBA), mandated by U.S. for major federal regulations since 1981, requiring agencies to quantify and monetize expected benefits and costs to ensure net positive impacts. of Management and Budget (OMB) oversees this process, as updated in Circular A-4 (2023), which emphasizes distributional effects and long-term discounting, though implementation varies and critics note underestimation of benefits in areas like environmental regulation due to data limitations or selective valuation. Despite these advances, ideological influences often prevail over , as in where entrenched beliefs shape evidence interpretation, underscoring the need for institutional safeguards against to align with causal realities and empirical outcomes.

Ethical Critiques and Balanced Alternatives

Philosophers such as have critiqued modern conceptions of rationality, particularly rationality, for reducing ethical to efficient means-ends without substantive of ends themselves, leading to where claims function as mere assertions of preference rather than reasoned arguments grounded in shared goods. contends that this stems from the Enlightenment's abandonment of Aristotelian , fragmenting into competing, incommensurable systems incapable of rational resolution, as evidenced by ongoing debates in that prioritize procedural neutrality over tradition-embedded virtues. Similarly, has argued that rationalist liberal theories, like John Rawls's veil of ignorance, presuppose an unencumbered self abstracted from communal ties and historical contingencies, rendering them ethically inadequate for addressing real conflicts rooted in identity and belonging. These critiques highlight rationality's ethical shortcomings in promoting moral detachment: by emphasizing impartial calculation, it can justify outcomes that violate intuitive relational duties, such as utilitarian aggregation overriding individual dignity, as seen in historical applications like cost-benefit analyses in policy that undervalue non-quantifiable harms. Rational choice theory, often modeled on economic assumptions of self-interested maximization, has been faulted for lacking ethical neutrality, implicitly endorsing a thin conception of the good that privileges over interdependence, with empirical studies showing deviations in under moral stakes where or fairness prevail over pure . Critics note that such models, while predictive in aggregate , falter in ethical domains, as rational actors frequently exhibit "irrational" commitments to or sanctity, per cross-cultural findings from 2012 onward. As balanced alternatives, revives practical wisdom ()—deliberative judgment attuned to context, character, and communal practices—over abstract rule-following or outcome optimization, positing that ethical rationality emerges from habituated excellence within traditions rather than detached computation. MacIntyre advocates retrieving Aristotelian frameworks, where virtues like and are cultivated through narrative unity of life in social roles, enabling critique of ends via historical rather than assuming their givenness. Complementary approaches include theory, which synthesizes consequentialist foresight, deontological constraints, and virtue cultivation to address rationality's silos, as proposed in integrative ethical analyses emphasizing holistic agent over isolated rational faculties. These alternatives maintain reason's role but subordinate it to thicker evaluative horizons, countering pure rationality's risks while preserving causal accountability in .

References

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