Proper linear models are those in which predictor variables are given weights such that the resulting linear composite optimally predicts some criterion of interest; examples of proper linear models are standard regression analysis, discriminant function analysis, and ridge regression analysis. Research summarized in P. Meehl's book on clinical vs statistical prediction and research stimulated in part by that book indicate that when a numerical criterion variable is to be predicted from numerical predictor variables, proper linear models outperform clinical intuition. Improper (...) linear models are those in which the weights of the predictor variables are obtained by some nonoptimal method. The present article presents evidence that even such improper linear models are superior to clinical intuition when predicting a numerical criterion from numerical predictors. In fact, unit weighting is quite robust for making such predictions. The application of unit weights to decide what bullet the Denver Police Department should use is described; some technical, psychological, and ethical resistances to using linear models in making social decisions are considered; and arguments that could weaken these resistances are presented. (shrink)

A review of the literature indicates that linear models are frequently used in situations in which decisions are made on the basis of multiple codable inputs. These models are sometimes used normatively to aid the decision maker, as a contrast with the decision maker in the clinical vs statistical controversy, to represent the decision maker "paramorphically" and to "bootstrap" the decision maker by replacing him with his representation. Examination of the contexts in which linear models have been successfully employed indicates (...) that the contexts have the following structural characteristics in common: each input variable has a conditionally monotone relationship with the output; there is error of measurement; and deviations from optimal weighting do not make much practical difference. These characteristics ensure the success of linear models, which are so appropriate in such contexts that random linear models may perform quite well. 4 examples involving the prediction of such codable output variables as GPA and psychiatric diagnosis are analyzed in detail. In all 4 examples, random linear models yield predictions that are superior to those of human judges. (shrink)

Incoherence is usually regarded as a bad thing. Incoherence suggests irrationality, confusion, paradox. Incoherentism disagrees: incoherence is not always a bad thing, sometimes we ought to be incoherent. If correct, Incoherentism has important and controversial implications. It implies that rationality does not always require coherence. Dilemmism and Incoherentism both embrace conflict in epistemology. After identifying some important differences between these two ways of embracing conflict, I offer some reasons to prefer Incoherentism over Dilemmism. Namely, that Incoherentism allows us to deliberate (...) about what we ought to believe using ordinary epistemology, and it does a better job of accommodating the positive features of incoherence. (shrink)

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)

Probabilistic coherence is not an absolute requirement of rationality; nevertheless, it is an ideal of rationality with substantive normative import. An idealized rational agent who avoided making implicit logical errors in forming his preferences would be coherent. In response to the challenge, recently made by epistemologists such as Foley and Plantinga, that appeals to ideal rationality render probabilism either irrelevant or implausible, I argue that idealized requirements can be normatively relevant even when the ideals are unattainable, so long as they (...) define a structure that links imperfect and perfect rationality in a way that enables us to make sense of the notion of better approximations to the ideal. I then analyze the notion of approximation to the ideal of coherence by developing a generalized theory of belief functions that allows for incoherence, and showing how such belief functions can be ordered with regard to greater or lesser coherence. (shrink)

This paper offers a new account of how structural rationality, or coherence, is normative. The central challenge to the normativity of coherence – which I term the problem of “making space” for the normativity of coherence – is this: if considerations of coherence matter normatively, it is not clear how we ought to take account of them in our deliberation. Coherence considerations don’t seem to show up in reasoning about what to believe, intend, desire, hope, fear, and so on; moreover, (...) they seem awkward to take account of alongside more “substantive” considerations about the merits of such attitudes. I aim here to solve this problem, and in so doing to offer the aforementioned new account of how coherence is normative. On the view I defend, which I call the Reasons-to-Structure-Deliberation model, considerations of coherence constitute reasons for structuring deliberation in certain ways: more particularly, to treat incoherent combinations of attitudes as off-limits, and so to focus one’s deliberation on choosing between the coherent combinations. (shrink)

Many philosophers hold that the probability axioms constitute norms of rationality governing degrees of belief. This view, known as subjective Bayesianism, has been widely criticized for being too idealized. It is claimed that the norms on degrees of belief postulated by subjective Bayesianism cannot be followed by human agents, and hence have no normative force for beings like us. This problem is especially pressing since the standard framework of subjective Bayesianism only allows us to distinguish between two kinds of credence (...) functions—coherent ones that obey the probability axioms perfectly, and incoherent ones that don’t. An attractive response to this problem is to extend the framework of subjective Bayesianism in such a way that we can measure differences between incoherent credence functions. This lets us explain how the Bayesian ideals can be approximated by humans. I argue that we should look for a measure that captures what I call the ‘overall degree of incoherence’ of a credence function. I then examine various incoherence measures that have been proposed in the literature, and evaluate whether they are suitable for measuring overall incoherence. The competitors are a qualitative measure that relies on finding coherent subsets of incoherent credence functions, a class of quantitative measures that measure incoherence in terms of normalized Dutch book loss, and a class of distance measures that determine the distance to the closest coherent credence function. I argue that one particular Dutch book measure and a corresponding distance measure are particularly well suited for capturing the overall degree of incoherence of a credence function. (shrink)

The study of rationality and practical reason, or rationality in action, has been central to Western intellectual culture. In this invigorating book, John Searle lays out six claims of what he calls the Classical Model of rationality and shows why they are false. He then presents an alternative theory of the role of rationality in thought and action. A central point of Searle's theory is that only irrational actions are directly caused by beliefs and desires—for example, the actions of a (...) person in the grip of an obsession or addiction. In most cases of rational action, there is a gap between the motivating desire and the actual decision making. The traditional name for this gap is "freedom of the will." According to Searle, all rational activity presupposes free will. For rationality is possible only where one has a choice among various rational as well as irrational options. Unlike many philosophical tracts, Rationality in Action invites the reader to apply the author's ideas to everyday life. Searle shows, for example, that contrary to the traditional philosophical view, weakness of will is very common. He also points out the absurdity of the claim that rational decision making always starts from a consistent set of desires. Rational decision making, he argues, is often about choosing between conflicting reasons for action. In fact, humans are distinguished by their ability to be rationally motivated by desire-independent reasons for action. Extending his theory of rationality to the self, Searle shows how rational deliberation presupposes an irreducible notion of the self. He also reveals the idea of free will to be essentially a thesis of how the brain works. (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)

Practical deliberation consists in thinking about what to do. Such deliberation is deemed rational when it conforms to certain normative requirements. What is often ignored is the role that an agent's context can play in so-called ‘failures’ of rationality. In this paper, I use recent cognitive science research investigating the effects of resource-scarcity on decision-making and cognitive function to argue that context plays an important role in determining which norms should structure an agent's deliberation. This evidence undermines the view that (...) the norms of ‘ideal’ rationality are necessary and universal requirements on deliberation. They are a solution to the problems faced by cognitively limited agents in a context of moderate scarcity. In a context of severe scarcity, the problems faced by cognitively limited agents are different and require deliberation structured by different norms. Agents reason rationally when they use the norms best suited to their context and cognitive capacities. (shrink)

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)

Normativity involves two kinds of relation. On the one hand, there is the relation of being a reason for. This is a relation between a fact and an attitude. On the other hand, there are relations specified by requirements of rationality. These are relations among a person's attitudes, viewed in abstraction from the reasons for them. I ask how the normativity of rationality—the sense in which we ‘ought’ to comply with requirements of rationality—is related to the normativity of reasons—the sense (...) in which we ‘ought’ to have the attitudes what we have conclusive reason to have. The normativity of rationality is not straightforwardly that of reasons, I argue; there are no reasons to comply with rational requirements in general. First, this would lead to ‘bootstrapping’, because, contrary to the claims of John Broome, not all rational requirements have ‘wide scope’. Second, it is unclear what such reasons to be rational might be. Finally, we typically do not, and in many cases could not, treat rational requirements as reasons. Instead, I suggest, rationality is only apparently normative, and the normativity that it appears to have is that of reasons. According to this ‘Transparency Account’, rational requirements govern our responses to our beliefs about reasons. The normative ‘pressure’ that we feel, when rational requirements apply to us, derives from these beliefs: from the reasons that, as it seems to us, we have. (shrink)

Which rules should guide our reasoning? Human reasoners often use reasoning shortcuts, called heuristics, which function well in some contexts but lack the universality of reasoning rules like deductive implication or inference to the best explanation. Does it follow that human reasoning is hopelessly irrational? I argue: no. Heuristic reasoning often represents human reasoners reaching a local rational maximum, reasoning more accurately than if they try to implement more “ideal” rules of reasoning. I argue this is a genuine rational achievement. (...) Our ideal rational advisors would advise us to reason with heuristic rules, not more complicated ideal rules. I argue we do not need a radical new account of epistemic norms to make sense of the success of heuristic reasoning. (shrink)

While Bayesian models have been applied to an impressive range of cognitive phenomena, methodological challenges have been leveled concerning their role in the program of rational analysis. The focus of the current article is on computational impediments to probabilistic inference and related puzzles about empirical confirmation of these models. The proposal is to rethink the role of Bayesian methods in rational analysis, to adopt an independently motivated notion of rationality appropriate for computationally bounded agents, and to explore broad conditions under (...) which Bayesian agents would be rational. The proposal is illustrated with a characterization of costs inspired by thermodynamics. (shrink)

Humans typically display hindsight bias. They are more confident that the evidence available beforehand made some outcome probable when they know the outcome occurred than when they don't. There is broad consensus that hindsight bias is irrational, but this consensus is wrong. Hindsight bias is generally rationally permissible and sometimes rationally required. The fact that a given outcome occurred provides both evidence about what the total evidence available ex ante was, and also evidence about what that evidence supports. Even if (...) you in fact evaluate the ex ante evidence correctly, you should not be certain of this. Then, learning the outcome provides evidence that if you erred, you are more likely to have erred low rather than high in estimating the degree to which the ex ante evidence supported the hypothesis that that outcome would occur. (shrink)

Axiomatic rationality is defined in terms of conformity to abstract axioms. Savage limited axiomatic rationality to small worlds, that is, situations in which the exhaustive and mutually exclusive set of future states S and their consequences C are known. Others have interpreted axiomatic rationality as a categorical norm for how human beings should reason, arguing in addition that violations would lead to real costs such as money pumps. Yet a review of the literature shows little evidence that violations are actually (...) associated with any measurable costs. Limiting axiomatic rationality to small worlds, I propose a naturalized version of rationality for situations of intractability and uncertainty, all of which are not in. In these situations, humans can achieve their goals by relying on heuristics that may violate axiomatic rationality. The study of ecological rationality requires formal models of heuristics and an analysis of the structures of environments these can exploit. It lays the foundation of a moderate naturalism in epistemology, providing statements about heuristics we should use in a given situation. Unlike axiomatic rationality, ecological rationality can explain less-is-more effects, formalize when one should move from ‘is’ to ‘ought,’ and be evaluated by goals beyond coherence, such as predictive accuracy, frugality, and efficiency. Ecological rationality can be seen as a formalization of means–end instrumentalist rationality, based on Herbert Simon’s insight that rational behavior is a function of the mind and its environment. (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)

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

One problem faced in discussions of the evolution of intelligence is the need to get a precise fix on what is to be explained. Terms like "intelligence," "cognition" and "mind" do not have simple and agreed-upon meanings, and the differences between conceptions of intelligence have consequences for evolutionary explanation. I hope the papers in this volume will enable us to make progress on this problem. The present contribution is mostly focused on these basic and foundational issues, although the last section (...) of the paper will look at some specific models and programs of empirical work. (shrink)