Is our case strong enough to go to trial? Will interest rates go up? Can I trust this person? Such questions - and the judgments required to answer them - are woven into the fabric of everyday experience. This book, first published in 2002, examines how people make such judgments. The study of human judgment was transformed in the 1970s, when Kahneman and Tversky introduced their 'heuristics and biases' approach and challenged the dominance of strictly rational models. Their work highlighted (...) the reflexive mental operations used to make complex problems manageable and illuminated how the same processes can lead to both accurate and dangerously flawed judgments. The heuristics and biases framework generated a torrent of influential research in psychology - research that reverberated widely and affected scholarship in economics, law, medicine, management, and political science. This book compiles the most influential research in the heuristics and biases tradition since the initial collection of 1982. (shrink)

Humans and animals make inferences about the world under limited time and knowledge. In contrast, many models of rational inference treat the mind as a Laplacean Demon, equipped with unlimited time, knowledge, and computational might. Following H. Simon's notion of satisficing, the authors have proposed a family of algorithms based on a simple psychological mechanism: one-reason decision making. These fast and frugal algorithms violate fundamental tenets of classical rationality: They neither look up nor integrate all information. By computer simulation, the (...) authors held a competition between the satisficing "Take The Best" algorithm and various "rational" inference procedures. The Take The Best algorithm matched or outperformed all competitors in inferential speed and accuracy. This result is an existence proof that cognitive mechanisms capable of successful performance in the real world do not need to satisfy the classical norms of rational inference. (shrink)

This article described three heuristics that are employed in making judgements under uncertainty: representativeness, which is usually employed when people are asked to judge the probability that an object or event A belongs to class or process B; availability of instances or scenarios, which is often employed when people are asked to assess the frequency of a class or the plausibility of a particular development; and adjustment from an anchor, which is usually employed in numerical prediction when a relevant value (...) is available. These heuristics are highly economical and usually effective, but they lead to systematic and predictable errors. A better understanding of these heuristics and of the biases to which they lead could improve judgements and decisions in situations of uncertainty. (shrink)

Without Good Reason offers a clear critical account of the debate in philosophy and cognitive science about whether humans are rational. Various experiments performed over the last several decades have been interpreted as showing that humans are irrational; certain philosophers, on the other hand, have argued that it is a conceptual truth that humans must be rational. Edward Stein concludes that the question of human rationality should be answered not conceptually but empirically: the resources of a fully developed cognitive science (...) need to be used not only to answer this question but generally in investigations of the nature of human knowledge and understanding. (shrink)

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Economic theory reduces the concept of rationality to internal consistency. As far as beliefs are concerned, rationality is equated with having a prior belief over a “Grand State Space”, describing all possible sources of uncertainties. We argue that this notion is too weak in some senses and too strong in others. It is too weak because it does not distinguish between rational and irrational beliefs. Relatedly, the Bayesian approach, when applied to the Grand State Space, is inherently incapable of describing (...) the formation of prior beliefs. On the other hand, this notion of rationality is too strong because there are many situations in which there is not sufficient information for an individual to generate a Bayesian prior. It follows that the Bayesian approach is neither sufficient not necessary for the rationality of beliefs. (shrink)

From an evolutionary standpoint, a default presumption is that true beliefs are adaptive and misbeliefs maladaptive. But if humans are biologically engineered to appraise the world accurately and to form true beliefs, how are we to explain the routine exceptions to this rule? How can we account for mistaken beliefs, bizarre delusions, and instances of self-deception? We explore this question in some detail. We begin by articulating a distinction between two general types of misbelief: those resulting from a breakdown in (...) the normal functioning of the belief formation system (e.g., delusions) and those arising in the normal course of that system's operations (e.g., beliefs based on incomplete or inaccurate information). The former are instances of biological dysfunction or pathology, reflecting “culpable” limitations of evolutionary design. Although the latter category includes undesirable (but tolerable) by-products of “forgivably” limited design, our quarry is a contentious subclass of this category: misbeliefs best conceived as design features. Such misbeliefs, unlike occasional lucky falsehoods, would have been systematically adaptive in the evolutionary past. Such misbeliefs, furthermore, would not be reducible to judicious – but doxastically1noncommittal – action policies. Finally, such misbeliefs would have been adaptive in themselves, constituting more than mere by-products of adaptively biased misbelief-producing systems. We explore a range of potential candidates for evolved misbelief, and conclude that, of those surveyed, onlypositive illusionsmeet our criteria. (shrink)

If we know that certain ways of making decisions are associated with real-life success, is this then how we should decide? In this paper the relationship between normative and descriptive theories of decision-making is examined. First, it is shown that the history of the decision sciences ensures that it is impossible to separate descriptive theories from normative ones. Second, recent psychological research implies new ways of arguing from the descriptive to the normative. The paper ends with an evaluation of how (...) this might affect normative theories of decision-making. (shrink)

The Dutch Book argument, like Route 66, is about to turn 80. It is arguably the most celebrated argument for subjective Bayesianism. Start by rejecting the Cartesian idea that doxastic attitudes are ‘all-or-nothing’; rather, they are far more nuanced degrees of belief, for short credences, susceptible to fine-grained numerical measurement. Add a coherentist assumption that the rationality of a doxastic state consists in its internal consistency. The remaining problem is to determine what consistency of credences amounts to. The Dutch Book (...) argument, in a nutshell, says that if your credences do not obey the probability calculus, you are ‘incoherent’—susceptible to sure losses at the hands of a ‘Dutch Bookie’—and thus irrational. Conclusion: rationality requires your credences to obey the probability calculus. And like Route 66, the fortunes of the Dutch Book argument have been mixed. Opinions on the argument are sharply divided. The list of its proponents is quite a ‘who’s who’ of philosophers of probability; they include de Finetti (1937, 1980), Carnap (1950, 1962, and more fully, 1955), Kemeny (1955), Lehman (1955), Shimony (1955), Adams (1962), Mellor (1971), Rosenkrantz (1981), van Fraassen (1989), Jeffrey (1983, 1992). (shrink)

Over recent years, the psychology of concepts has been rejuvenated by new work on prototypes, inventive ideas on causal cognition, the development of neo-empiricist theories of concepts, and the inputs of the budding neuropsychology of concepts. But our empirical knowledge about concepts has yet to be organized in a coherent framework. -/- In Doing without Concepts, Edouard Machery argues that the dominant psychological theories of concepts fail to provide such a framework and that drastic conceptual changes are required to make (...) sense of the research on concepts in psychology and neuropsychology. Machery shows that the class of concepts divides into several distinct kinds that have little in common with one another and that for this very reason, it is a mistake to attempt to encompass all known phenomena within a single theory of concepts. In brief, concepts are not a natural kind. Machery concludes that the theoretical notion of concept should be eliminated from the theoretical apparatus of contemporary psychology and should be replaced with theoretical notions that are more appropriate for fulfilling psychologists' goals. The notion of concept has encouraged psychologists to believe that a single theory of concepts could be developed, leading to useless theoretical controversies between the dominant paradigms of concepts. Keeping this notion would slow down, and maybe prevent, the development of a more adequate classification and would overshadow the theoretical and empirical issues that are raised by this more adequate classification. Anyone interested in cognitive science's emerging view of the mind will find Machery's provocative ideas of interest. (shrink)

In his new foreword to this edition, Hilary Putnam forcefully rejects these nativist claims.

How can anyone be rational in a world where knowledge is limited, time is pressing, and deep thought is often an unattainable luxury? Traditional models of unbounded rationality and optimization in cognitive science, economics, and animal behavior have tended to view decision-makers as possessing supernatural powers of reason, limitless knowledge, and endless time. But understanding decisions in the real world requires a more psychologically plausible notion of bounded rationality. In Simple heuristics that make us smart (Gigerenzer et al. 1999), we (...) explore fast and frugal heuristics – simple rules in the mind's adaptive toolbox for making decisions with realistic mental resources. These heuristics can enable both living organisms and artificial systems to make smart choices quickly and with a minimum of information by exploiting the way that information is structured in particular environments. In this précis, we show how simple building blocks that control information search, stop search, and make decisions can be put together to form classes of heuristics, including: ignorance-based and one-reason decision making for choice, elimination models for categorization, and satisficing heuristics for sequential search. These simple heuristics perform comparably to more complex algorithms, particularly when generalizing to new data – that is, simplicity leads to robustness. We present evidence regarding when people use simple heuristics and describe the challenges to be addressed by this research program. Key Words: adaptive toolbox; bounded rationality; decision making; elimination models; environment structure; heuristics; ignorance-based reasoning; limited information search; robustness; satisficing; simplicity. (shrink)

In this paper we propose to argue for two claims. The first is that a sizeable group of epistemological projects – a group which includes much of what has been done in epistemology in the analytic tradition – would be seriously undermined if one or more of a cluster of empirical hypotheses about epistemic intuitions turns out to be true. The basis for this claim will be set out in Section 2. The second claim is that, while the jury is (...) still out, there is now a substantial body of evidence suggesting that some of those empirical hypotheses are true. Much of this evidence derives from an ongoing series of experimental studies of epistemic intuitions that we have been conducting. A preliminary report on these studies will be presented in Section 3. In light of these studies, we think it is incumbent on those who pursue the epistemological projects in question to either explain why the truth of the hypotheses does not undermine their projects, or to say why, in light of the evidence we will present, they nonetheless assume that the hypotheses are false. In Section 4, which is devoted to Objections and Replies, we’ll consider some of the ways in which defenders of the projects we are criticizing might reply to our challenge. Our goal, in all of this, is not to offer a conclusive argument demonstrating that the epistemological projects we will be criticizing are untenable. Rather, our aim is to shift the burden of argument. (shrink)

During the last 25 years, researchers studying human reasoning and judgment in what has become known as the “heuristics and biases” tradition have produced an impressive body of experimental work which many have seen as having “bleak implications” for the rationality of ordinary people (Nisbett and Borgida 1975). According to one proponent of this view, when we reason about probability we fall victim to “inevitable illusions” (Piattelli-Palmarini 1994). Other proponents maintain that the human mind is prone to “systematic deviations from (...) rationality” (Bazerman & Neale 1986) and is “not built to work by the rules of probability” (Gould 1992). It has even been suggested that human beings are “a species that is uniformly probability-blind” (Piattelli-Palmarini 1994). This provocative and pessimistic interpretation of the experimental findings has been challenged from many different directions over the years. One of the most recent and energetic of these challenges has come from the newly emerging field of evolutionary psychology, where it has been argued that it’s singularly implausible to claim that our species would have evolved with no “instinct for probability” and, hence, be “blind to chance” (Pinker 1997, 351). Though evolutionary psychologists concede that it is possible to design experiments that “trick our probability.. (shrink)

A fundamental philosophical question that arises in connection with evolutionary theory is whether the fittest patterns of behavior are always the most rational. Are fitness and rationality fully compatible? When behavioral rationality is characterized formally as in classical decision theory, the question becomes mathematically meaningful and can be explored systematically by investigating whether the optimally fit behavior predicted by evolutionary process models is decision-theoretically coherent. Upon investigation, it appears that in nontrivial evolutionary models the expected behavior is not always in (...) accord with the norms of the standard theory of decision as ordinarily applied. Many classically irrational acts, e.g. betting on the occurrence of one event in the knowledge that the probabilities favor another, can under certain circumstances constitute adaptive behavior.One interesting interpretation of this clash is that the criterion of rationality offered by classical decision theory is simply incorrect (or at least incomplete) as it stands, and that evolutionary theory should be called upon to provide a more generally applicable theory of rationality. Such a program, should it prove feasible, would amount to the logical reduction of the theory of rational choice to evolutionary theory. (shrink)

The thirty-five chapters in this book describe various judgmental heuristics and the biases they produce, not only in laboratory experiments but in important...

The object of this paper is to show why recent research in the psychology of deductive and probabilistic reasoning does not have.

Rational analysis (Anderson 1990, 1991a) is an empiricalprogram of attempting to explain why the cognitive system isadaptive, with respect to its goals and the structure of itsenvironment. We argue that rational analysis has two importantimplications for philosophical debate concerning rationality. First,rational analysis provides a model for the relationship betweenformal principles of rationality (such as probability or decisiontheory) and everyday rationality, in the sense of successfulthought and action in daily life. Second, applying the program ofrational analysis to research on human reasoning (...) leads to a radicalreinterpretation of empirical results which are typically viewed asdemonstrating human irrationality. (shrink)

Simple Heuristics That Make Us Smart invites readers to embark on a new journey into a land of rationality that differs from the familiar territory of cognitive science and economics. Traditional views of rationality tend to see decision makers as possessing superhuman powers of reason, limitless knowledge, and all of eternity in which to ponder choices. To understand decisions in the real world, we need a different, more psychologically plausible notion of rationality, and this book provides it. It is about (...) fast and frugal heuristics--simple rules for making decisions when time is pressing and deep thought an unaffordable luxury. These heuristics can enable both living organisms and artificial systems to make smart choices, classifications, and predictions by employing bounded rationality. But when and how can such fast and frugal heuristics work? Can judgments based simply on one good reason be as accurate as those based on many reasons? Could less knowledge even lead to systematically better predictions than more knowledge? Simple Heuristics explores these questions, developing computational models of heuristics and testing them through experiments and analyses. It shows how fast and frugal heuristics can produce adaptive decisions in situations as varied as choosing a mate, dividing resources among offspring, predicting high school drop out rates, and playing the stock market. As an interdisciplinary work that is both useful and engaging, this book will appeal to a wide audience. It is ideal for researchers in cognitive psychology, evolutionary psychology, and cognitive science, as well as in economics and artificial intelligence. It will also inspire anyone interested in simply making good decisions. (shrink)

The paper shows why and how an empirical study of fast-and-frugal heuristics can provide norms of good reasoning, and thus how (and how far) rationality can be naturalized. We explain the heuristics that humans often rely on in solving problems, for example, choosing investment strategies or apartments, placing bets in sports, or making library searches. We then show that heuristics can lead to judgments that are as accurate as or even more accurate than strategies that use more information and computation, (...) including optimization methods. A standard way to defend the use of heuristics is by reference to accuracy-effort trade-offs. We take a different route, emphasizing ecological rationality (the relationship between cognitive heuristics and environment), and argue that in uncertain environments, more information and computation are not always better (the “less-can-be-more” doctrine). The resulting naturalism about rationality is thus normative because it not only describes what heuristics people use, but also in which specific environments one should rely on a heuristic in order to make better inferences. While we desist from claiming that the scope of ecological rationality is unlimited, we think it is of wide practical use. (shrink)

Economic theory reduces the concept of rationality to internal consistency. As far as beliefs are concerned, rationality is equated with having a prior belief over a “Grand State Space”, describing all possible sources of uncertainties. We argue that this notion is too weak in some senses and too strong in others. It is too weak because it does not distinguish between rational and irrational beliefs. Relatedly, the Bayesian approach, when applied to the Grand State Space, is inherently incapable of describing (...) the formation of prior beliefs. On the other hand, this notion of rationality is too strong because there are many situations in which there is not sufficient information for an individual to generate a Bayesian prior. It follows that the Bayesian approach is neither sufficient not necessary for the rationality of beliefs. (shrink)

We first describe recent empirical research on racial cognition, particularly work on implicit racial biases that suggests they are widespread, that they can coexist with explicitly avowed anti-racist and tolerant attitudes, and that they influence behavior in a variety of subtle but troubling ways. We then consider a cluster of questions that the existence and character of implicit racial biases raise for moral theory. First, is it morally condemnable to harbor an implicit racial bias? Second, ought each of us to (...) suspect ourselves of racial bias, and therefore correct for it in ordinary activity, such as grading student papers? (shrink)

This paper reviews the largely psychological literature surrounding apparent failures of human rationality (sometimes referred to as 'the Rationality Wars') and locates it with respect to concepts and issues within more traditional epistemological inquiry. The goal is to bridge the gap between these two large and typically disconnected literatures – concerning rationality and the psychology of human reasoning, on the one hand, and epistemological theories of justified or rational belief, on the other – and to do so in such as (...) way as to expose interesting points of contact and convergence between them. (shrink)

Together, these collected papers develop the idea that human thinking - from scientific creativity to simply understanding what a positive HIV test means - "happens" partly outside the mind.".

Are humans rational? Various experiments performed over the last several decades have been interpreted as showing that humans are irrational we make significant and consistent errors in logical reasoning, probabilistic reasoning, similarity judgements, and risk-assessment, to name a few areas. But can these experiments establish human irrationality, or is it a conceptual truth that humans must be rational, as various philosophers have argued? In this book, Edward Stein offers a clear critical account of this debate about rationality in philosophy and (...) cognitive science. He discusses concepts of rationality - the pictures of rationality that the debate centres on - and assesses the empirical evidence used to argue that humans are irrational. He concludes that the question of human rationality must be answered not conceptually but empirically, using the full resources of an advanced cognitive science. Furthermore, he extends this conclusion to argue that empirical considerations are also relevant to the theory of knowedge - in other words, that epistemology should be naturalized. from the reviews: 'Stein has done a great service in bringing together all of the important arguments in the human rationality debate and providing a measured critical assessment of them.... This will be an important book and is essential reading for epistemologists, philosophers of mind, and cognitive and evolutionary psychologists.' Choice 'very considerable value... for professionals' Times Higher Education Supplement. (shrink)

Heuristics are efficient cognitive processes that ignore information. In contrast to the widely held view that less processing reduces accuracy, the study of heuristics shows that less information, computation, and time can in fact improve accuracy. We review the major progress made so far: the discovery of less-is-more effects; the study of the ecological rationality of heuristics, which examines in which environments a given strategy succeeds or fails, and why; an advancement from vague labels to computational models of heuristics; the (...) development of a systematic theory of heuristics that identifies their building blocks and the evolved capacities they exploit, and views the cognitive system as relying on an “adaptive toolbox;” and the development of an empirical methodology that accounts for individual differences, conducts competitive tests, and has provided evidence for people’s adaptive use of heuristics. Homo heuristicus has a biased mind and ignores part of the available information, yet a biased mind can handle uncertainty more efficiently and robustly than an unbiased mind relying on more resource-intensive and general-purpose processing strategies. (shrink)

This paper discusses Stanovich's appeal to individual differences in reasoning and decision-making to undermine the “adaptive rationality” project put forth by Gigerenzer and his co-workers. I discuss two different arguments based on Stanovich's research. First, heterogeneity in the use of heuristics seems to be at odds with the adaptationist background of the project. Second, the existence of correlations between cognitive ability and susceptibility to cognitive bias suggests that the “standard picture of rationality” (Stein, 1996, 4) is normatively adequate. I argue (...) that, as matters stand, none of the arguments can be seen as fully compelling. Nevertheless, my discussion is not only critical of Stanovich's research, as I also show that (and how) his research can push forward the so-called “rationality debate” by encouraging greater theoretical and experimental work. (shrink)

Stanovich & West (S&W) have two goals, one concerned with the evaluation of normative models, the other with development of prescriptive models. Individual differences have no bearing on normative models, which are justified by analysis, not consensus. Individual differences do, however, suggest where it is possible to try to improve human judgments and decisions through education rather than computers.