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

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

Most theories of affective influences on judgement and choice take a valence-based approach, contrasting the effects of positive versus negative feeling states. These approaches have not specified if and when distinct emotions of the same valence have different effects on judgement. In this article, we propose a model of emotion-specific influences on judgement and choice. We posit that each emotion is defined by a tendency to perceive new events and objects in ways that are consistent with the original cognitive-appraisal dimensions (...) of the emotion. To pit the valence and appraisal-tendency approaches against one another, we present a study that addresses whether two emotions of the same valence but differing appraisals—anger and fear—relate in different ways to risk perception. Consistent with the appraisal-tendency hypothesis, fearful people made pessimistic judgements of future events whereas angry people made optimistic judgements. In the Discussion we expand the proposed model and review evidence supporting two social moderators of appraisal-tendency processes. (shrink)