We argue that the interaction of biased media coverage and widespread employment of the recognition heuristic can produce epistemic injustices. First, we explain the recognition heuristic as studied by Gerd Gigerenzer and colleagues, highlighting how some of its components are largely external to, and outside the control of, the cognitive agent. We then connect the recognition heuristic with recent work on the hypotheses of embedded, extended, and scaffolded cognition, arguing that the recognition heuristic is best understood as an instance of (...) scaffolded cognition. In section three, we consider the double-edged sword of cognitive scaffolding. On the one hand, scaffolds can reduce the internal processing demands on cognitive agents while increasing their access to information. On the other hand, the use of scaffolding leaves cognitive agents increasingly vulnerable to forming false beliefs or failing to form beliefs at all about particular topics. With respect to the recognition heuristic, agents rely on third parties (such as the media) to report not just what’s true but also what’s important or valuable. This makes cognitive agents relying on these third parties vulnerable to two erroneous influences: 1) because they don’t recognize something, it isn’t important or valuable, and 2) because they do recognize something, it is important or valuable. Call the latter the Kardashian Inference and the former the Darfur Inference. In section four, we use Fricker’s (2007) concept of epistemic injustice to characterize the nature and harm of these false inferences, with special emphasis on the Darfur Inference. In section five, we use data-mining and an empirical study to show how Gigerenzer’s population estimation task is liable to produce Darfur Inferences. We conclude with some speculative remarks on more important Darfur Inferences, and how to avoid them by scaffolding better. One primary way to accomplish this it to shift the burden of embodying the virtue of epistemic justice from the hearer or consumer of media to the media themselves. (shrink)

Richard Pettigrew offers an extended investigation into a particular way of justifying the rational principles that govern our credences. The main principles that he justifies are the central tenets of Bayesian epistemology, though many other related principles are discussed along the way. Pettigrew looks to decision theory in order to ground his argument. He treats an agent's credences as if they were a choice she makes between different options, gives an account of the purely epistemic utility enjoyed by different sets (...) of credences, and then appeals to the principles of decision theory to show that, when epistemic utility is measured in this way, the credences that violate the principles listed above are ruled out as irrational. The account of epistemic utility set out here is the veritist's: the sole fundamental source of epistemic utility for credences is their accuracy. Thus, Pettigrew conducts an investigation in the version of epistemic utility theory known as accuracy-first epistemology. (shrink)

How do we go about weighing evidence, testing hypotheses, and making inferences? According to the model of _Inference to the Best Explanation_, we work out what to infer from the evidence by thinking about what would actually explain that evidence, and we take the ability of a hypothesis to explain the evidence as a sign that the hypothesis is correct. In _Inference to the Best Explanation_, Peter Lipton gives this important and influential idea the development and assessment it deserves. The (...) second edition has been substantially enlarged and reworked, with a new chapter on the relationship between explanation and Bayesianism, and an extension and defence of the account of contrastive explanation. It also includes an expanded defence of the claims that our inferences really are guided by diverse explanatory considerations, and that this pattern of inference can take us towards the truth. This edition of _Inference to the Best Explanation_ has also been updated throughout and includes a new bibliography. (shrink)

This paper defends a principle I call Equal Treatment, according to which the rationality of a belief is determined in precisely the same way as the rationality of any other state. For example, if wearing a raincoat is rational just in case doing so maximizes expected value, then believing some proposition P is rational just in case doing so maximizes expected value. This contrasts with the popular view that the rationality of belief is determined by evidential support. It also contrasts (...) with the common idea that in the case of belief, there are two different incommensurable senses of rationality, one of which is distinctively epistemic. I present considerations that favor Equal Treatment over these two alternatives, reply to objections, and criticize some arguments for Evidentialism. I also show how Equal Treatment opens the door to a distinctive kind of response to skepticism. (shrink)

In section 1, I develop epistemic communism, my view of the function of epistemically evaluative terms such as ‘rational’. The function is to support the coordination of our belief-forming rules, which in turn supports the reliable acquisition of beliefs through testimony. This view is motivated by the existence of valid inferences that we hesitate to call rational. I defend the view against the worry that it fails to account for a function of evaluations within first-personal deliberation. In the rest of (...) the paper, I then argue, on the basis of epistemic communism, for a view about rationality itself. I set up the argument in section 2 by saying what a theory of rational deduction is supposed to do. I claim that such a theory would provide a necessary, sufficient, and explanatorily unifying condition for being a rational rule for inferring deductive consequences. I argue in section 3 that, given epistemic communism and the conventionality that it entails, there is no such theory. Nothing explains why certain rules for deductive reasoning are rational. (shrink)

While the situationist challenge has been prominent in philosophical literature in ethics for over a decade, only recently has it been extended to virtue epistemology . Alfano argues that virtue epistemology is shown to be empirically inadequate in light of a wide range of results in social psychology, essentially succumbing to the same argument as virtue ethics. We argue that this meeting of the twain between virtue epistemology and social psychology in no way signals the end of virtue epistemology, but (...) is rather a boon to naturalized virtue epistemology. We use Gird Gigerenzer’s models for bounded rationality (2011) to present a persuasive line of defense for virtue epistemology, and consider prospects for a naturalized virtue epistemology that is supported by current research in psychology. (shrink)

In some previous work, I tried to give a concept-based account of the nature of our entitlement to certain very basic inferences (see the papers in Part III of Boghossian 2008b). In this previous work, I took it for granted, along with many other philosophers, that we understood well enough what it is for a person to infer. In this paper, I turn to thinking about the nature of inference itself. This topic is of great interest in its own right (...) and surprisingly understudied by philosophers. A correct understanding of inference promises to shed light on a number of important topics. In particular, it threatens to undermine the sort of concept-based story about entitlement to which I had previously been attracted. (shrink)

There is currently no viable alternative to the Bayesian analysis of scientific inference, yet the available versions of Bayesianism fail to do justice to several aspects of the testing and confirmation of scientific hypotheses. Bayes or Bust? provides the first balanced treatment of the complex set of issues involved in this nagging conundrum in the philosophy of science. Both Bayesians and anti-Bayesians will find a wealth of new insights on topics ranging from Bayes's original paper to contemporary formal learning theory. (...) In a paper published posthumously in 1763, the Reverend Thomas Bayes made a seminal contribution to the understanding of "analogical or inductive reasoning." Building on his insights, modem Bayesians have developed an account of scientific inference that has attracted numerous champions as well as numerous detractors. Earman argues that Bayesianism provides the best hope for a comprehensive and unified account of scientific inference, yet the presently available versions of Bayesianisin fail to do justice to several aspects of the testing and confirming of scientific theories and hypotheses. By focusing on the need for a resolution to this impasse, Earman sharpens the issues on which a resolution turns. John Earman is Professor of History and Philosophy of Science at the University of Pittsburgh. (shrink)

Is there such a thing as bounded rationality? I first try to make sense of the question, and then to suggest which of the disambiguated versions might have answers. We need an account of bounded rationality that takes account of detailed contingent facts about the ways in which human beings fail to perform as we might ideally want to. But we should not think in terms of rules or norms which define good responses to an individual's limitations, but rather in (...) terms of desiderata, situations that limited agents can hope to achieve, and corresponding virtues of achieving them. We should not take formal theories defining optimal behavior in watered-down bounded form, even though they can impose enormous computational or cognitive demands. (shrink)

What is the Moral Problem? NORMATIVE ETHICS VS. META-ETHICS It is a common fact of everyday life that we appraise each others' behaviour and attitudes from ...

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)

How do we go about weighing evidence, testing hypotheses, and making inferences? The model of " inference to the best explanation " -- that we infer the hypothesis that would, if correct, provide the best explanation of the available evidence--offers a compelling account of inferences both in science and in ordinary life. Widely cited by epistemologists and philosophers of science, IBE has nonetheless remained little more than a slogan. Now this influential work has been thoroughly revised and updated, and features (...) a new introduction and two new chapters. Inference to the Best Explanation is an unrivaled exposition of a theory of particular interest in the fields both of epistemology and the philosophy of science. (shrink)

On many of the idealized models of human cognition and behavior in use by philosophers, agents are represented as having a single corpus of beliefs which (a) is consistent and deductively closed, and (b) guides all of their (rational, deliberate, intentional) actions all the time. In graded-belief frameworks, agents are represented as having a single, coherent distribution of credences, which guides all of their (rational, deliberate, intentional) actions all of the time. It's clear that actual human beings don't live up (...) to this idealization. The systems of belief that we in fact have are fragmented. Rather than having a single system of beliefs that guides all of our behavior all of the time, we have a number of distinct, compartmentalized systems of belief, different ones of which drive different aspects of our behavior in different contexts. It's tempting to think that, while of course people are fragmented, it would be better (from the perspective of rationality) if they weren't, and the only reason why our fragmentation is excusable is that we have limited cognitive resources, which prevents us from holding too much information before our minds at a time. Give us enough additional processing capacity, and there'd be no justification for any continued fragmentation. I argue that this is not so. There are good reasons to be fragmented rather than unified, independent of the limitations on our available processing power. In particular, there are ways our belief-forming mechanisms—including our perceptual systems—could be constructed that would make it better to be fragmented than to be unified. And there are reasons to think that some of our belief-forming mechanisms really are constructed that way. (shrink)

Suppose that you and I disagree about some non-straightforward matter of fact (say, about whether capital punishment tends to have a deterrent effect on crime). Psychologists have demonstrated the following striking phenomenon: if you and I are subsequently exposed to a mixed body of evidence that bears on the question, doing so tends to increase the extent of our initial disagreement. That is, in response to exactly the same evidence, each of us grows increasingly confident of his or her original (...) view; we thus become increasingly polarized as our common evidence increases. I consider several alternative models of how people reason about newly-acquired evidence which seems to disconfirm their prior beliefs. I then explore the normative implications of these models for the phenomenon in question. (shrink)

A heuristic is a rule of thumb. In psychology, heuristics are relatively simple rules for making judgments. A fast heuristic is easy to use and allows one to make judgments quickly. A frugal heuristic relies on a small fraction of the available evidence in making judgments. Typically, fast and frugal heuristics (FFHs) have, or are claimed to have, a further property: They are very reliable, yielding judgments that are about as accurate in the long run as ideal non-fast, non-frugal rules. (...) This paper introduces some well-known examples of FFHs, raises some objections to the FFH program, and looks at the implications of those parts of the FFH program about which we can have some reasonable degree of confidence. (shrink)

R. Jay Wallace argues in this book that moral accountability hinges on questions of fairness: When is it fair to hold people morally responsible for what they do? Would it be fair to do so even in a deterministic world? To answer these questions, we need to understand what we are doing when we hold people morally responsible, a stance that Wallace connects with a central class of moral sentiments, those of resentment, indignation, and guilt. To hold someone responsible, he (...) argues, is to be subject to these reactive emotions in one's dealings with that person. Developing this theme with unusual sophistication, he offers a new interpretation of the reactive emotions and traces their role in our practices of blame and moral sanction. With this account in place, Wallace advances a powerful and sustained argument against the common view that accountability requires freedom of will. Instead, he maintains, the fairness of holding people responsible depends on their rational competence: the power to grasp moral reasons and to control their behavior accordingly. He shows how these forms of rational competence are compatible with determinism. At the same time, giving serious consideration to incompatibilist concerns, Wallace develops a compelling diagnosis of the common assumption that freedom is necessary for responsibility. Rigorously argued, eminently readable, this book touches on issues of broad concern to philosophers, legal theorists, political scientists, and anyone with an interest in the nature and limits of responsibility. (shrink)

Recently, the practice of deciding legal cases on purely statistical evidence has been widely criticised. Many feel uncomfortable with finding someone guilty on the basis of bare probabilities, even though the chance of error might be stupendously small. This is an important issue: with the rise of DNA profiling, courts are increasingly faced with purely statistical evidence. A prominent line of argument—endorsed by Blome-Tillmann 2017; Smith 2018; and Littlejohn 2018—rejects the use of such evidence by appealing to epistemic norms that (...) apply to individual inquirers. My aim in this paper is to rehabilitate purely statistical evidence by arguing that, given the broader aims of legal systems, there are scenarios in which relying on such evidence is appropriate. Along the way I explain why popular arguments appealing to individual epistemic norms to reject legal reliance on bare statistics are unconvincing, by showing that courts and individuals face different epistemic predicaments (in short, individuals can hedge when confronted with statistical evidence, whilst legal tribunals cannot). I also correct some misconceptions about legal practice that have found their way into the recent literature. (shrink)

Lipsey and Lancaster's "general theory of second best" is widely thought to have significant implications for applied theorizing about the institutions and policies that most effectively implement abstract normative principles. It is also widely thought to have little significance for theorizing about which abstract normative principles we ought to implement. Contrary to this conventional wisdom, I show how the second-best theorem can be extended to myriad domains beyond applied normative theorizing, and in particular to more abstract theorizing about the normative (...) principles we should aim to implement. I start by separating the mathematical model used to prove the second-best theorem from its familiar economic interpretation. I then develop an alternative normative-theoretic interpretation of the model, which yields a novel second best theorem for idealistic normative theory. My method for developing this interpretation provides a template for developing additional interpretations that can extend the reach of the second-best theorem beyond normative theoretical domains. I also show how, within any domain, the implications of the second-best theorem are more specific than is typically thought. I conclude with some brief remarks on the value of mathematical models for conceptual exploration. (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)

Contemporary cognitive science clearly tells us that attention is modulated for speech and action. While these forms of goal-directed attention are very well researched in psychology, they have not been sufficiently studied by epistemologists. In this book, Abrol Fairweather and Carlos Montemayor develop and defend a theory of epistemic achievements that requires the manifestation of cognitive agency. They examine empirical work on the psychology of attention and assertion, and use it to ground a normative theory of epistemic achievements and virtues. (...) The resulting study is the first sustained naturalized virtue epistemology, and will be of interest to readers in epistemology, cognitive science, and beyond. (shrink)

I argue that inference can tolerate forms of self-ignorance and that these cases of inference undermine canonical models of inference on which inferrers have to appreciate (or purport to appreciate) the support provided by the premises for the conclusion. I propose an alternative model of inference that belongs to a family of rational responses in which the subject cannot pinpoint exactly what she is responding to or why, where this kind of self-ignorance does nothing to undermine the intelligence of the (...) response. (shrink)

Responsibility and the Moral Sentiments offers an account of moral responsibility. It addresses the question: what are the forms of capacity or ability that render us morally accountable for the things we do? A traditional answer has it that the conditions of moral responsibility include freedom of the will, where this in turn involves the availability of robust alternative possibilities. I reject this answer, arguing that the conditions of moral responsibility do not include any condition of alternative possibilities. In the (...) terms that have become familiar, the book thus defends a compatibilist account of moral responsibility. (shrink)

The first section introduces a distinction between 2 families of cognitive operations, called System 1 and System 2. The second section presents an attribute-substitution model of heuristic judgment, which elaborates and extends earlier treatments of the topic. The third section introduces a research design for studying attribute substitution. The fourth section discusses the controversy over the representativeness heuristic. The last section situates representativeness within a broad family of prototype heuristics, in which properties of a prototypical exemplar dominate global judgments concerning (...) an entire set. (shrink)

There is currently no viable alternative to the Bayesian analysis of scientific inference, yet the available versions of Bayesianism fail to do justice to several aspects of the testing and confirmation of scientific hypotheses. Bayes or Bust? provides the first balanced treatment of the complex set of issues involved in this nagging conundrum in the philosophy of science. Both Bayesians and anti-Bayesians will find a wealth of new insights on topics ranging from Bayes’s original paper to contemporary formal learning theory.In (...) a paper published posthumously in 1763, the Reverend Thomas Bayes made a seminal contribution to the understanding of "analogical or inductive reasoning." Building on his insights, modem Bayesians have developed an account of scientific inference that has attracted numerous champions as well as numerous detractors. Earman argues that Bayesianism provides the best hope for a comprehensive and unified account of scientific inference, yet the presently available versions of Bayesianisin fail to do justice to several aspects of the testing and confirming of scientific theories and hypotheses. By focusing on the need for a resolution to this impasse, Earman sharpens the issues on which a resolution turns. John Earman is Professor of History and Philosophy of Science at the University of Pittsburgh. (shrink)

In this paper I revisit Gilbert Harman's arguments for a "clutter avoidance" norm. The norm -- which says that we ought to avoid cluttering our minds with trivialities -- is widely endorsed. I argue that it has some fairly dramatic consequences for normative epistemology.

How should thinkers cope with uncertainty? Julia Staffel breaks new ground in the study of rationality by answering this question and many others. She also explains how it is better to be less irrational, because less irrational degrees of belief are generally more accurate and better at guiding our actions.

Science depends on judgments of the bearing of evidence on theory. Scientists must judge whether an observation or the result of an experiment supports, disconfirms, or is simply irrelevant to a given hypothesis. Similarly, scientists may judge that, given all the available evidence, a hypothesis ought to be accepted as correct or nearly so, rejected as false, or neither. Occasionally, these evidential judgments can be make on deductive grounds. If an experimental result strictly contradicts a hypothesis, then the truth of (...) the evidence deductively entails the falsity of the hypothesis. In the great majority of cases, however, the connection between evidence and hypothesis is nondemonstrative or inductive. In particular, this is so whenever a general hypothesis is inferred to be correct on the basis of the available data, since the truth of the data will not deductively entail the truth of the hypothesis. It always remains possible that the hypothesis is false even though the data are correct. (shrink)

An account of the virtues of limitation management: intellectual virtues of adapting to the fact that we cannot solve many of the problems that we can describe. I argue that the best response to many problems depends not on the most rationally promising solution, but on the most likely route to success. I argue against techniques that assume that one will fulfil ones intentions, and distinguish between failures of rationality and failures of intelligence. I describe the trap of supposing that (...) one will be capable of following the plan that would best exploit ones limited resources. (shrink)

Gerd Gigerenzer and Thomas Sturm have recently proposed a modest form of what they describe as a normative, ecological and limited naturalism. The basic move in their argument is to infer that certain heuristics we tend to use should be used in the right ecological setting. To address this argument, we first consider the case of a concrete heuristic called Take the Best (TTB). There are at least two variants of the heuristic which we study by making explicit the choice (...) functions they induce, extending these variants of TTB beyond binary choice. We argue that the naturalistic argument can be applied to only one of the two variants of the heuristic; we also argue that the argument for the extension requires paying attention to other “rational” virtues of heuristics aside from efficacy, speed, and frugality. This notwithstanding, we show that there is a way of extending the right variant of TTB to obtain a very well behaved heuristic that could be used to offer a stronger case for the naturalistic argument (in the sense that if this heuristic is used, it is also a heuristic that we should use). The second part of the article considers attempts to extending the naturalistic argument from algorithms dealing with inference to heuristics dealing with choice. Our focus is the so-called Priority Heuristic, which we extend from risk to uncertainty. In this setting, the naturalist argument seems more difficult to formulate, if it remains feasible at all. Normativity seems in this case extrinsic to the heuristic, whose main virtue seems to be its ability to describe actual patterns of choice. But it seems that a new version of the naturalistic argument used with partial success in the case of inference is unavailable to solve the normative problem of whether we should exhibit the patterns of choice that we actually display. (shrink)