Findings in recent research on the `conjunction fallacy ' have been taken as evidence that our minds are not designed to work by the rules of probability. This conclusion springs from the idea that norms should be content-blind Ð in the present case, the assumption that sound reasoning requires following the conjunction rule of probability theory. But content-blind norms overlook some of the intelligent ways in which humans deal with uncertainty, for instance, when drawing semantic and pragmatic inferences. In a series of studies, we ®rst show that people infer nonmathematical meanings of the polysemous term `probability' in the classic Linda conjunction problem. We then demonstrate that one can design contexts in which people infer mathematical meanings of the term and are therefore more likely to conform to the conjunction rule. Finally, we report evidence that the term `frequency ' narrows the spectrum of possible interpretations of `probability ' down to its mathematical meanings, and that this fact Ð rather than the presence or absence of `extensional cues ' Ð accounts for the low proportion of violations of the conjunction rule when people are asked for

The literature in cognitive psychology has described a variety of shortcomings that prevent individuals from learning effectively. We review this literature and provide examples ofa number oforganizational practices that may effectively repair thecognitive shortcomings of individuals. We call these practices cognitive repairs. We then discuss six tradeoffs that affect the success of cognitive repairs. We close by considering how a cognitive perspective might benefit those who study organizational learning and those who manage it.

Attributing higher "probability" to a sentence of form p-and-q compared to p is a reasoning fallacy only if (a) the word "probability" carries its modern, technical meaning, and (b) the sentence p is interpreted as a conjunct of the conjunction p-and-q. Legitimate doubts arise about both conditions in classic demonstrations of the conjunction fallacy. We use betting paradigms to reduce these sources of ambiguity about conjunctive reasoning. Reality of the conjunction fallacy 1 Introduction The conjunction fallacy Here is the famous Linda story, to be labeled E (for "evidence") in what follows. (E) Linda is 31 years old, single, outspoken, and very bright. She majored in philosophy. As a student, she was deeply concerned with issues of discrimination and social justice, and also participated in antinuclear demonstrations. The task is to rank various statements "by their probability," including these two. (B) Linda is a bank teller. (B # F ) Linda is a bank teller and is activ...

When participants assess the relationship between two variables, each with levels of presence and absence, the two most robust phenomena are that: (a) observing the joint presence of the variables has the largest impact on judgment and observing joint absence has the smallest impact, and (b) participants’ prior beliefs about the variables ’ relationship influence judgment. Both phenomena represent departures from the traditional normative model (the phi coefficient or related measures) and have therefore been interpreted as systematic errors. However, both phenomena are consistent with a Bayesian approach to the task. From a Bayesian perspective: (a) joint presence is normatively more informative than joint absence if the presence of variables is rarer than their absence, and (b) failing to incorporate prior beliefs is a normative error. Empirical evidence is reported showing that joint absence is seen as more informative than joint presence when it is clear that absence of the variables, rather than their presence, is rare.

Practitioners and teachers should be able to justify their chosen techniques by taking into account research results: This is evidence-based practice (EBP). We argue that, specifically, statistical practice and statistics education should be guided by evidence, and we propose statistical cognition (SC) as an integration of theory, research, and application to support EBP. SC is an interdisciplinary research field, and a way of thinking. We identify three facets of SC—normative, descriptive, and prescriptive— and discuss their mutual influences. Unfortunately, the three components are studied by somewhat separate groups of scholars, who publish in different journals. These separations impede the implementation of EBP. SC, however, integrates the facets and provides a basis for EBP in statistical practice and education.

Introduction: Characterizing the Questions of causal reasoning This chapter describes the mechanism approach to the study of causal reasoning. We will first offer a characterization of the central issues in human causal reasoning, and will discuss how the mechanism approach addresses these issues. In the course of this presentation, we will frequently compare the mechanism approach with alternative accounts based on analyses of covariation, or what is often termed the regularity view. The aims of this chapter are the following: to explain why covariation and mechanism are different, to discuss why such a distinction is actually a useful tool for our understanding of causal reasoning, and to explicate the complementary nature of the two views. Before presenting these two approaches, it is necessary first to offer a description of the domain or problem itself : namely, what are these alternative approaches to? Although there are a number of different ways of characterizing the study of

This article is concerned with the implications of the surprisingly different experimental practices in economics and in areas of psychology relevant to both economists and psychologists, such as behavioral decision making. We consider four features of experimentation in economics, namely, script enactment, repeated trials, performance-based monetary payments, and the proscription against deception, and compare them to experimental practices in psychology, primarily in the area of behavioral decision making. Whereas economists bring a precisely defined ìscriptî to experiments for

This article reexamines the assumptions underlying the disease problem used by Tversky and Kahneman (1981) to illustrate gain– loss formulation effects. It is argued that their reported effect may have been due to asymmetries in the ambiguity of the sure and risky prospects and to the entanglement of two distinct types of formulation manipulations: one having to do with the expected outcomes that are made explicit (positive vs negative) and the other having to do with the descriptors used to convey the relevant expected outcomes (lives saved/not saved vs lives lost/not lost). Two experiments using a formally equivalent problem in which these confounds were eliminated revealed no significant predictive effect of either descriptor or outcomes frames on choice, although a marginally significant framing effect was obtained in Experiment 1 when the signs of the two framing manipulations were congruent. Implications for prospect theory are discussed. � 2001 Academic Press Key Words: framing effects; formulation effects; choice; gains and losses. Pessimists see the wine glass half empty, optimists see it half full. As this adage of lay personology suggests, the same event may be viewed in different ways by different people. Moreover, sometimes each of these alternative perspectives are objectively correct. A wine glass half empty is a wine glass half

An important research tradition in the cognitive psychology of reasoning--called the heuristics and biases approach--has firmly established that people’s responses often deviate from the performance considered normative on many reasoning tasks. For example, people assess probabilities incorrectly, they display confirmation bias, they test hypotheses inefficiently, they violate the axioms of utility theory, they do not properly calibrate degrees of belief, they overproject their own opinions onto others, they display illogical framing effects, they uneconomically honor sunk costs, they allow prior knowledge to become implicated in deductive reasoning, and they display numerous other information processing biases (for summaries of the large literature, see

International Business School. The authors gratefully acknowledge the financial support of the ISBM and