Intractability and optimality are two sides of one coin: Optimal models are often intractable, that is, they tend to be excessively complex, or NP-hard. We explain the meaning of NP-hardness in detail and discuss how modem computer science circumvents intractability by introducing heuristics and shortcuts to optimality, often replacing optimality by means of sufficient sub-optimality. Since the principles of decision theory dictate balancing the cost of computation against gain in accuracy, statistical inference is currently being reshaped by a vigorous new (...) trend: the science of simplicity. Simple models, as we show for specific cases, are not just tractable, they also tend to be robust. Robustness is the ability of a model to extract relevant information from data, disregarding noise.Recently, Gigerenzer, Todd and the ABC Research Group (1999) have put forward a collection of fast and frugal heuristics as simple, boundedly rational inference strategies used by the unaided mind in real world inference problems. This collection of heuristics has suggestively been called the adaptive toolbox. In this paper we will focus on a comparison task in order to illustrate the simplicity and robustness of some of the heuristics in the adaptive toolbox in contrast to the intractability and the fragility of optimal solutions. We will concentrate on three important classes of models for comparison-based inference and, in each of these classes, search for that to be used as benchmarks to evaluate the performance of fast and frugal heuristics: lexicographic trees, linear modes and Bayesian networks. Lexicographic trees are interesting because they are particularly simple models that have been used by humans throughout the centuries. Linear models have been traditionally used by cognitive psychologists as models for human inference, while Bayesian networks have only recently been introduced in statistics and computer science. Yet it is the Bayesian networks that are the best possible benchmarks for evaluating the fast and frugal heuristics, as we will show in this paper. (shrink)

Cognitive developmental evidence is sometimes conscripted to support ''naturalized epistemology'' arguments to the effect that a general epistemic stance leads children to build theory-like accounts of underlying properties of kinds. A review of the evidence suggests that what prompts conceptual acquisition is not a general epistemic stance but a series of category-specific intuitive principles that constitute an evolved ''natural metaphysics''. This consists in a system of categories and category-specific inferential processes founded on definite biases in prototype formation. Evidence for this (...) system provides a better understanding of the limited ''plasticity'' of ontological commitments as well as a computationally plausible account of their initial state, avoiding ambiguities about innateness. This may provide a starting point for a ''naturalized epistemology'' that takes into account evolved properties of human conceptual structures. (shrink)

Failure to consider base rate is regarded as potentially hazardous, mainly because its consideration is assumed to be determined solely by P(H/D), the probability of the individuating data if the hypothesis is true, and not at all by P(D/˜H), the probability if the hypothesis is false. However, when P(D/˜H) is unconfounded from P(D/H), it turns out to be the stronger determinant of base rate consideration.

Koehler's call for a reanalysis of the base rate fallacy is particularly important in the applied domain of accounting, since base rate data appear to be an important input for many accounting tasks. In this commentary I discuss the use of base rates in accounting and explain why more flexible standards of performance are important when judging the use of base rates.

As illustrated by research Koehler himself cites (Dawes et al. 1993), the purpose of experiments is to choose between contrasting explanations of past observations – rather than to seek statistical generalizations about the prevalence of effects. True external validity results not from sampling various problems that are representative of “real world” decision making, but from reproducing an effect in the laboratory with minimal contamination (including from real world factors).

We have been oversold on the base rate fallacy in probabilistic judgment from an empirical, normative, and methodological standpoint. At the empirical level, a thorough examination of the base rate literature (including the famous lawyer–engineer problem) does not support the conventional wisdom that people routinely ignore base rates. Quite the contrary, the literature shows that base rates are almost always used and that their degree of use depends on task structure and representation. Specifically, base rates play a relatively larger role (...) in tasks where base rates are implicitly learned or can be represented in frequentist terms. Base rates are also used more when they are reliable and relatively more diagnostic than available individuating information. At the normative level, the base rate fallacy should be rejected because few tasks map unambiguously into the narrow framework that is held up as the standard of good decision making. Mechanical applications of Bayes's theorem to identify performance errors are inappropriate when (1) key assumptions of the model are either unchecked or grossly violated, and (2) no attempt is made to identify the decision maker's goals, values, and task assumptions. Methodologically, the current approach is criticized for its failure to consider how the ambiguous, unreliable, and unstable base rates of the real world are and should be used. Where decision makers' assumptions and goals vary, and where performance criteria are complex, the traditional Bayesian standard is insufficient. Even where predictive accuracy is the goal in commonly defined problems, there may be situations (e.g., informationally redundant environments) in which base rates can be ignored with impunity. A more ecologically valid research program is called for. This program should emphasize the development of prescriptive theory in rich, realistic decision environments. (shrink)

Science is a form of distributed analysis involving both individual work that produces new knowledge and collaborative work to exchange information with the larger community. There are many particular ways in which individual and community can interact in science, and it is difficult to assess how efficient these are, and what the best way might be to support them. This paper reports on a series of experiments in this area and a prototype implementation using a research platform called CACHE. CACHE (...) both supports experimentation with different structures of interaction between individual and community cognition and serves as a prototype for computational support for those structures. We particularly focus on CACHE‐BC, the Bayes community version of CACHE, within which the community can break up analytical tasks into “mind‐sized” units and use provenance tracking to keep track of the relationship between these units. (shrink)

We report the results of an experiment which investigates the impact of the manner in which likelihood information is presented to decision-makers on valuations assigned to lotteries. We find that subjects who observe representative sequences of outcomes attach higher valuations to lotteries than those who are given only a verbal description of a probability distribution. We interpret this in terms of a reduction in ambiguity about the possible lottery outcomes. These findings suggest that ambiguity aversion may be a confounding factor (...) in reported experimental violations of expected utility theory based on verbal descriptions of probability distributions. (shrink)

Normative theories can be useful in developing descriptive theories, as when normative subjective expected utility theory is used to develop descriptive rational choice theory and behavioral game theory. “Ought” questions are also the essence of theories of moral reasoning, a domain of higher mental processing that could not survive without normative considerations.

In this paper we discuss the new Tweety puzzle. The original Tweety puzzle was addressed by approaches in non-monotonic logic, which aim to adequately represent the Tweety case, namely that Tweety is a penguin and, thus, an exceptional bird, which cannot fly, although in general birds can fly. The new Tweety puzzle is intended as a challenge for probabilistic theories of epistemic states. In the first part of the paper we argue against monistic Bayesians, who assume that epistemic states can (...) at any given time be adequately described by a single subjective probability function. We show that monistic Bayesians cannot provide an adequate solution to the new Tweety puzzle, because this requires one to refer to a frequency-based probability function. We conclude that monistic Bayesianism cannot be a fully adequate theory of epistemic states. In the second part we describe an empirical study, which provides support for the thesis that monistic Bayesianism is also inadequate as a descriptive theory of cognitive states. In the final part of the paper we criticize Bayesian approaches in cognitive science, insofar as their monistic tendency cannot adequately address the new Tweety puzzle. We, further, argue against monistic Bayesianism in cognitive science by means of a case study. In this case study we show that Oaksford and Chater’s (2007, 2008) model of conditional inference—contrary to the authors’ theoretical position—has to refer also to a frequency-based probability function. (shrink)

There have recently been a number of strong claims that normative considerations, broadly construed, influence many philosophically important folk concepts and perhaps are even a constitutive component of various cognitive processes. Many such claims have been made about the influence of such factors on our folk notion of causation. In this paper, we argue that the strong claims found in the recent literature on causal cognition are overstated, as they are based on one narrow type of data about a particular (...) type of causal cognition; the extant data do not warrant any wide-ranging conclusions about the pervasiveness of normative considerations in causal cognition. Of course, almost all empirical investigations involve some manner of ampliative inference, and so we provide novel empirical results demonstrating that there are types of causal cognition that do not seem to be influenced by moral considerations. (shrink)

We present a theory of decision by sampling (DbS) in which, in contrast with traditional models, there are no underlying psychoeconomic scales. Instead, we assume that an attribute’s subjective value is constructed from a series of binary, ordinal comparisons to a sample of attribute values drawn from memory and is its rank within the sample. We assume that the sample reflects both the immediate distribution of attribute values from the current decision’s context and also the background, real-world distribution of attribute (...) values. DbS accounts for concave utility functions; losses looming larger than gains; hyperbolic temporal discounting; and the overestimation of small probabilities and the underestimation of large probabilities. Ó 2005 Elsevier Inc. All rights reserved. (shrink)

The target article by Alexander Kon (2009) offers an excellent conceptual toolkit to analyze the current literature on empirical bioethics, a topic which has attracted so much attention in the bioe...

This article provides an overview of recent results on lexicographic, linear, and Bayesian models for paired comparison from a cognitive psychology perspective. Within each class, we distinguish subclasses according to the computational complexity required for parameter setting. We identify the optimal model in each class, where optimality is defined with respect to performance when fitting known data. Although not optimal when fitting data, simple models can be astonishingly accurate when generalizing to new data. A simple heuristic belonging to the class (...) of lexicographic models is Take The Best (Gigerenzer & Goldstein (1996) Psychol. Rev. 102: 684). It is more robust than other lexicographic strategies which use complex procedures to establish a cue hierarchy. In fact, it is robust due to its simplicity, not despite it. Similarly, Take The Best looks up only a fraction of the information that linear and Bayesian models require; yet it achieves performance comparable to that of models which integrate information. Due to its simplicity, frugality, and accuracy, Take The Best is a plausible candidate for a psychological model in the tradition of bounded rationality. We review empirical evidence showing the descriptive validity of fast and frugal heuristics. (shrink)

This paper aims to make explicit the methodological conditions that should be satisfied for the Bayesian model to be used as a normative model of human probability judgment. After noticing the lack of a clear definition of Bayesianism in the psychological literature and the lack of justification for using it, a classic definition of subjective Bayesianism is recalled, based on the following three criteria: an epistemic criterion, a static coherence criterion and a dynamic coherence criterion. Then it is shown that (...) the adoption of this framework has two kinds of implications. The first one regards the methodology of the experimental study of probability judgment. The Bayesian framework creates pragmatic constraints on the methodology that are linked to the interpretation of, and the belief in, the information presented, or referred to, by an experimenter in order for it to be the basis of a probability judgment by individual participants. It is shown that these constraints have not been satisfied in the past, and the question of whether they can be satisfied in principle is raised and answered negatively. The second kind of implications consists of two limitations in the scope of the Bayesian model. They regard (1) the background of revision (the Bayesian model considers only revising situations but not updating situations), and (2) the notorious case of the null priors. In both cases Lewis’ rule is an appropriate alternative to Bayes’ rule, but its use faces the same operational difficulties. (shrink)

Standard Analytic Epistemology (SAE) names a contingently clustered class of methods and theses that have dominated English-speaking epistemology for about the past half-century. The major contemporary theories of SAE include versions of foundationalism, coherentism, reliabilism, and contextualism. While proponents of SAE don’t agree about how to define naturalized epistemology, most agree that a thoroughgoing naturalism in epistemology can’t work. For the purposes of this paper, we will suppose that a naturalistic theory of epistemology takes as its core, as its starting-point, (...) an empirical theory. The standard argument against naturalistic approaches to epistemology is that empirical theories are essentially descriptive, while epistemology is essentially prescriptive, and a descriptive theory cannot yield normative, evaluative prescriptions. In short, naturalistic theories cannot overcome the is-ought divide. Our main goal in this paper is to show that the standard argument against naturalized epistemology has it almost exactly backwards. (shrink)

In his early experimental work with Suppes, Davidson adopted rationality assumptions, not as necessary constraints on interpretation, but as practical conceits in addressing methodological problems faced by experimenters studying decision making under uncertainty. Although the content of their theory has since been undermined, their methodological approach—a Galilean form of methodological rationalism—lives on in contemporary psychological research. This article draws on Max Weber’s verstehen to articulate an account of Galilean methodological rationalism; explains how anomalies faced by Davidson’s early experimental work gave (...) rise to his later, canonical claims about rationality and interpretation; and reclaims this Galilean framework for use in contemporary psychological research. (shrink)

In his debates with Daniel Kahneman and Amos Tversky, Gerd Gigerenzer puts forward a stricter standard for the proper representation of judgment heuristics. I argue that Gigerenzer’s stricter standard contributes to naturalized epistemology in two ways. First, Gigerenzer’s standard can be used to winnow away cognitive processes that are inappropriately characterized and should not be used in the epistemic evaluation of belief. Second, Gigerenzer’s critique helps to recast the generality problem in naturalized epistemology and cognitive psychology as the methodological problem (...) of identifying criteria for the appropriate specification and characterization of cognitive processes in psychological explanations. I conclude that naturalized epistemologists seeking to address the generality problem should turn their focus to methodological questions about the proper characterization of cognitive processes for the purposes of psychological explanation. (shrink)

The past 50 years have seen an accumulation of evidence suggesting that associative learning depends on high-level cognitive processes that give rise to propositional knowledge. Yet, many learning theorists maintain a belief in a learning mechanism in which links between mental representations are formed automatically. We characterize and highlight the differences between the propositional and link approaches, and review the relevant empirical evidence. We conclude that learning is the consequence of propositional reasoning processes that cooperate with the unconscious processes involved (...) in memory retrieval and perception. We argue that this new conceptual framework allows many of the important recent advances in associative learning research to be retained, but recast in a model that provides a firmer foundation for both immediate application and future research. (shrink)

Severity of Test (SoT) is an alternative to Popper's logical falsification that solves a number of problems of the logical view. It was presented by Popper himself in 1963. SoT is a less sophisticated probabilistic model of hypothesis testing than Oaksford & Chater's (O&C's) information gain model, but it has a number of striking similarities. Moreover, it captures the intuition of everyday hypothesis testing.

Four experiments investigated the effects of probability manipulations on the indicative four card selection task (Wason, 1966, 1968). All looked at the effects of high and low probability antecedents (p) and consequents (q) on participants' data selections when determining the truth or falsity of a conditional rule, if p then q . Experiments 1 and 2 also manipulated believability. In Experiment 1, 128 participants performed the task using rules with varied contents pretested for probability of occurrence. Probabilistic effects were observed (...) which were partly consistent with some probabilistic accounts but not with non-probabilistic approaches to selection task performance. No effects of believability were observed, a finding replicated in Experiment 2 which used 80 participants with standardised and familiar contents. Some effects in this experiment appeared inconsistent with existing probabilistic approaches. To avoid possible effects of content, Experiments 3 (48 participants) and 4 (20 participants) used abstract material. Both experiments revealed probabilistic effects. In the Discussion we examine the compatibility of these results with the various models of selection task performance. (shrink)

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 response, we clarify several misunderstandings of the understanding/acceptance principle and defend our specific operationalization of that principle. We reiterate the importance of addressing the problem of rational task construal and we elaborate the notion of computational limitations contained in our target article. Our concept of thinking dispositions as variable intentional-level styles of epistemic and behavioral regulation is explained, as is its relation to the rationality debate. Many of the suggestions of the commentators for elaborating two-process models are easily (...) integrated into our generic dual-process account. We further explicate how we view the relation between System 1 and System 2 and evolutionary and normative rationality. We clarify our attempt to fuse the contributions of the cognitive ecologists with the insights of the original heuristics and biases researchers. (shrink)

The conclusions of Barbey & Sloman (B&S) crucially depend on evidence for different representations of statistical information. Unfortunately, a muddled distinction made among these representations calls into question the authors' conclusions. We clarify some notions of statistical representations which are often confused in the literature. These clarifications, combined with new empirical evidence, do not support a dual-process model of judgment.

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)

Humans make mistakes in our decision-making and probability judgments. While the heuristics used for decision-making have been explained as adaptations that are both efficient and fast, the reasons why people deal with probabilities using the reported biases have not been clear. We will see that some of these biases can be understood as heuristics developed to explain a complex world when little information is available. That is, they approximate Bayesian inferences for situations more complex than the ones in laboratory experiments (...) and in this sense might have appeared as an adaptation to those situations. When ideas as uncertainty and limited sample sizes are included in the problem, the correct probabilities are changed to values close to the observed behavior. These ideas will be used to explain the observed weight functions, the violations of coalescing and stochastic dominance reported in the literature. (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)

Does the most common response given by participants presented with Tversky and Kahneman’s famous taxi cab problem amount to a violation of Bayes’ theorem? In other words, do they fall victim to so-called base-rate fallacy? In the present paper, following an earlier suggestion by Crupi and Girotto, we will identify the logical arguments underlying both the original diagnosis of irrationality in this reasoning task under uncertainty and a number of objections that have been raised against such a diagnosis. This will (...) enable us to show firstly that, contrary to the dismissive arguments recently put forward by Elqayam and Evans, empirical evidence turns out to be quite useful in addressing questions of this kind. Therefore, it can make a significant contribution to moving the base-rate fallacy controversy forward. Secondly, the available empirical evidence (though limited and sometimes inconclusive) seems to support the charge of irrationality levelled against the majority of participants presented with the taxi cab problem, and hence suggests that base-rate neglect is a real fallacy − that is the conclusion of our analysis. (shrink)

:Behavioural public policy has come under fire by critics who claim that it is illiberal. Some authors recently suggest that there is a type of BPP – boosting – that is not as vulnerable to this normative critique. Our paper challenges this claim: there's no non-circular way to draw the distinction between nudge and boost that would make the normative difference required to infer the permissibility of a policy intervention from its type-membership. We consider two strategies: paradigmatic examples and causal (...) mechanisms. We conclude by sketching some suggestions about the right way to approach the normative issues. (shrink)

This article seeks, first, to show that much of the existing normative work on argument from expert opinion (AEO) is problematic for failing to be properly informed by empirical findings on expert performance. Second, it seeks to show how, with the analytic tool of Bayesian reasoning, the problem diagnosed can be remedied to circumvent some of the problems facing the scheme-based treatment of AEOs. To establish the first contention, we will illustrate how empirical studies on factors conditioning expert reliability can (...) be drawn upon to re-construct. Walton’s critical questions matching the scheme of AEOs. To establish the second contention, we will illustrate how Walton’s re-constructed set of critical questions can be formalized within a Bayesian network. Finally, we will highlight how the specific ways in which the Bayesian framework we propose is both continuous with and distinct from the models of source reliability put forward by theorists like Bovens and Hartmann (2003). (shrink)

This work examines the influence of reference dependence, including value selection bias and congruence effects, on diagnostic reasoning. Across two studies, we explored how dependence on the initial problem structure influences the ability to solve simplified precursors to the more traditional Bayesian reasoning problems. Analyses evaluated accuracy and types of response errors as a function of congruence between the problem presentation and question of interest, amount of information, need for computation, and individual differences in numerical abilities. Across all problem variations, (...) there was consistent and strong evidence of a value selection bias in that incorrect responses almost always conformed to values that were provided in the problem rather than other errors including those related to computation. The most consistent and unexpected error across all conditions in the first experiment was that people were often more likely to utilize the superordinate value as part of their solution rather than the anticipated reference class values. This resulted in a weakened effect of congruence, with relatively low accuracy even in congruent conditions, and a dominant response error of the superordinate value. Experiment 2 confirmed that the introduction of a new sample drew attention away from the provided reference class, increasing reliance on the overall sample size. This superordinate preference error, along with the benefit of repeating the PPV reference class within the question, demonstrated the importance of reference dependence based on the salience of information within the response prompt. Throughout, higher numerical skills were generally associated with higher accuracy, whether calculations were required or not. (shrink)

In the present paper we empirically investigate the psychometric properties of some of the most famous statistical and logical cognitive illusions from the “heuristics and biases” research program by Daniel Kahneman and Amos Tversky, who nearly 50 years ago introduced fascinating brain teasers such as the famous Linda problem, the Wason card selection task, and so-called Bayesian reasoning problems (e.g., the mammography task). In the meantime, a great number of articles has been published that empirically examine single cognitive illusions, theoretically (...) explaining people’s faulty thinking, or proposing and experimentally implementing measures to foster insight and to make these problems accessible to the human mind. Yet these problems have thus far usually been empirically analyzed on an individual-item level only (e.g., by experimentally comparing participants’ performance on various versions of one of these problems). In this paper, by contrast, we examine these illusions as a group and look at the ability to solve them as a psychological construct. Based on an sample ofN= 2,643 Luxembourgian school students of age 16–18 we investigate the internal psychometric structure of these illusions (i.e., Are they substantially correlated? Do they form a reflexive or a formative construct?), their connection to related constructs (e.g., Are they distinguishable from intelligence or mathematical competence in a confirmatory factor analysis?), and the question of which of a person’s abilities can predict the correct solution of these brain teasers (by means of a regression analysis). (shrink)

This article develops a new, general account of replication. I argue that a replication is an experiment that resamples the experimental components of an ori...

DNA evidence is one of the most significant modern advances in the search for truth since the cross examination, but its format as a random-match-probability makes it difficult for people to assign an appropriate probative value (Koehler, 2001). While Frequentist theories propose that the presentation of the match as a frequency rather than a probability facilitates more accurate assessment (e.g., Slovic et al., 2000), Exemplar-Cueing Theory predicts that the subjective weight assigned may be affected by the frequency or probability format, (...) and how easily examples of the event, i.e., ‘exemplars’, are generated from linguistic cues that frame the match in light of further evidence (Koehler & Macchi, 2004). This paper presents two juror research studies to examine the difficulties that jurors have in assigning appropriate probative value to DNA evidence when contradictory evidence is presented. Study 1 showed that refuting evidence significantly reduced guilt judgments when exemplars were linguistically cued, even when the probability match and the refuting evidence had the same objective probative value. Moreover, qualitative reason for judgment responses revealed that interpreting refuting evidence was found to be complex and not necessarily reductive; refutation was found indicative of innocence or guilt depending on whether exemplars have been cued or not. Study 2 showed that the introduction of judges’ directions to linguistically cue exemplars, did not increase the impact of refuting evidence beyond its objective probative value, but less guilty verdicts were returned when jurors were instructed to consider all possible explanations of the evidence. The results are discussed in light of contradictory frequentist and exemplar-cueing theoretical positions, and their real-world consequences. (shrink)

In teaching statistics in secondary schools and at university, two visualizations are primarily used when situations with two dichotomous characteristics are represented: 2×2 tables and tree diagrams. Both visualizations can be depicted either with probabilities or with frequencies. Visualizations with frequencies have been shown to help students significantly more in Bayesian reasoning problems than probability visualizations do. Because tree diagrams or double-trees (which are largely unknown in school) are node-branch-structures, these two visualizations (compared to the 2×2 table) can even simultaneously (...) display probabilities on branches and frequencies inside the nodes. This is a teaching advantage as it allows the frequency concept to be used to better understand probabilities. However, 2×2 tables and (double-) trees have a decisive disadvantage: While joint probabilities (e.g., P(AB)) are represented in 2×2 tables but no conditional probabilities (e.g., P(A|B)), it is exactly the other way around with (double-) trees. Therefore, a visualization that is equally suitable for the representation of joint probabilities and conditional probabilities is desirable. In this article, we present a new visualization—the frequency net—in which all absolute frequencies and all types of probabilities can be depicted. In addition to a detailed theoretical analysis of the frequency net, we report the results of a study with 249 university students that shows that “net diagrams” can improve reasoning without previous instruction to a similar extent as 2×2 tables and double-trees. Regarding questions about conditional probabilities, frequency visualizations (2×2 table, double-tree, or net diagram with natural frequencies) are consistently superior to probability visualizations. The frequency net performs as well as the frequency double-tree. Only the 2×2 table with frequencies—the only visualization that participants were already familiar with —led to higher performance rates. If, on the other hand, a question about a joint probability had to be answered, all implemented visualizations clearly supported participants’ performance, and no uniform format effect becomes visible. Participants reached the highest performance in the versions with probability 2×2 tables and probability net diagrams. Furthermore, after conducting a detailed error analysis, we report interesting error shifts between the two information formats and the different visualizations and give recommendations for teaching probability. (shrink)

This paper examines three programmatic responses to the problem of cognitive bias: virtue epistemology, epistemic paternalism, and epistemic collectivism. Each of these programmes focuses on asinglelevel of epistemic analysis: virtue theorists on individuals, paternalists on environments, and collectivists on groups. I argue that this is a mistake in light of the fact that cognitive biases arise frominteractionsbetween these three domains. Consequently, epistemologists should spend less time defending these programmes, and more timecoordinatingthem. This paper offers empirically based arguments for the interactionist (...) approach, and contends that its adoption is an essential step for minimizing bias in empirical science. (shrink)

The Darwinian theory of evolution is itself evolving and this book presents the details of the core of modern Darwinism and its latest developmental directions. The authors present current scientific work addressing theoretical problems and challenges in four sections, beginning with the concepts of evolution theory, its processes of variation, heredity, selection, adaptation and function, and its patterns of character, species, descent and life. The second part of this book scrutinizes Darwinism in the philosophy of science and its usefulness in (...) understanding ecosystems, whilst the third section deals with its application in disciplines beyond the biological sciences, including evolutionary psychology and evolutionary economics, Darwinian morality and phylolinguistics. The final section addresses anti-Darwinism, the creationist view and issues around teaching evolution in secondary schools. The reader learns how current experimental biology is opening important perspectives on the sources of variation, and thus of the very power of natural selection. This work examines numerous examples of the extension of the principle of natural selection and provides the opportunity to critically reflect on a rich theory, on the methodological rigour that presides in its extensions and exportations, and on the necessity to measure its advantages and also its limits. Scholars interested in modern Darwinism and scientific research, its concepts, research programs and controversies will find this book an excellent read, and those considering how Darwinism might evolve, how it can apply to the human sciences and other disciplines beyond its origins will find it particularly valuable. Originally produced in French (Les Mondes Darwiniens), the scope and usefulness of the book have led to the production of this English text, to reach a wider audience. This book is a milestone in the impressive penetration by Francophone scholars into the world of Darwinian science, its historiography and philosophy over the last two decades. Alex Rosenberg, R. Taylor Cole Professor of Philosophy, Duke University Until now this useful and comprehensive handbook has only been available to francophones. Thanks to this invaluable new translation, this collection of insightful and original essays can reach the global audience it deserves. Tim Lewens, University of Cambridge. (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)

Current psychology of human reasoning is divided into several different approaches. For instance, there is a major dispute over the question whether human beings are able to apply norms of the formal models of rationality such as rules of logic, or probability and decision theory, correctly. While researchers following the “heuristics and biases” approach argue that we deviate systematically from these norms, and so are perhaps deeply irrational, defenders of the “bounded rationality” approach think not only that the evidence for (...) this conclusion is problematic but also that we should not, at least not very often, use formal norms in reasoning. I argue that while the evidence for heuristics and biases is indeed questionable, the bounded rationality approach has its limits too. Most especially, we should not infer that formal norms play no role in a comprehensive theory of rationality. Instead, formal and bounded rules of reasoning might even be connected in a more comprehensive theory of rationality. (shrink)

A theory of rationality is a theory that evaluates instances of reasoning as rational, irrational, or (ir)rational to some degree. Theories can be categorized as rule-based or consequentialist. Rule-based theories say that rational reasoning accords with certain rules (e.g., of logic or probability). Consequentialist theories say that rational reasoning tends to produce good consequences. For instance, the reliabilist takes rationality to be reasoning that tends to produce mostly true beliefs. The pragmatist takes it to be reasoning that tends to produce (...) mostly useful beliefs. This article reviews some of the features and the challenges of rule-based, reliabilist, and pragmatist theories of rationality. (shrink)

We report a series of experiments examining whether people ascribe knowledge for true beliefs based on probabilistic evidence. Participants were less likely to ascribe knowledge for beliefs based on probabilistic evidence than for beliefs based on perceptual evidence or testimony providing causal information. Denial of knowledge for beliefs based on probabilistic evidence did not arise because participants viewed such beliefs as unjustified, nor because such beliefs leave open the possibility of error. These findings rule out traditional philosophical accounts for why (...) probabilistic evidence does not produce knowledge. The experiments instead suggest that people deny knowledge because they distrust drawing conclusions about an individual based on reasoning about the population to which it belongs, a tendency previously identified by “judgment and decision making” researchers. Consistent with this, participants were more willing to ascribe knowledge for beliefs based on probabilistic evidence that is specific to a particular case. 2016 APA, all rights reserved). (shrink)

Psychologists and philosophers have not yet resolved what they take implicit attitudes to be; and, some, concerned about limitations in the psychometric evidence, have even challenged the predictive and theoretical value of positing implicit attitudes in explanations for social behavior. In the midst of this debate, prominent stakeholders in science have called for scientific communities to recognize and countenance implicit bias in STEM fields. In this paper, I stake out a stakeholder conception of implicit bias that responds to these challenges (...) in ways that are responsive to the psychometric evidence, while also being resilient to the sorts of disagreements and scientific progress that would not undermine the soundness of this call. Along the way, my account advocates for attributing collective (group-level) implicit attitudes rather than individual-level implicit attitudes. This position raises new puzzles for future research on the relationship (metaphysical, epistemic, and ethical) between collective implicit attitudes and individual-level attitudes. (shrink)

This book is concerned with helping you improve your approach to decision-making. The author examines judgement in a selection of managerial contexts and provides important understanding that can help you make better leadership decisions. The book also pinpoints the in-house politics of organisational decision-making. Drawing on the very latest research, it introduces practical techniques that show you how to analyse and develop your own decision-making style. It will help you to deliver sharp and insightful analyses of your business and develop (...) effective solutions. In addition, it presents simple checklists that will give you vital insights throughout the decision-making process. Students and practitioners of leadership, management, and allied fields will find this book useful in order to understand and implement useful methods. (shrink)