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 likelihood of a statement is often derived by generating an explanation for it and evaluating the plausibility of the explanation. The explanation discounting principle states that people tend to focus on a single explanation; alternative explanations compete with the effect of reducing one another’s credibility. Two experiments tested the hypothesis that this principle applies to inductive inferences concerning the properties of everyday categories. In both experiments, subjects estimated the probability of a series of statements and the conditional probabilities of (...) those conclusions given other related facts. For example, given that most lawyers make good sales people, what is the probability that most psychologists make good sales people? The results showed that when the fact and the conclusion had the same explanation the fact increased people’s willingness to believe the conclusion, but when they had different explanations the fact decreased the conclusion’s credibility. This decrease is attributed to explanation discounting; the explanation for the fact had the effect of reducing the plausibility of the explanation for the conclusion. (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...

We propose that people typically reason about realistic situations using neither content-free syntactic inference rules nor representations of specific experiences. Rather, people reason using knowledge structures that we term pragmatic reasoning schemas, which are generalized sets of rules defined in relation to classes of goals. Three experiments examined the impact of a “permission schema” on deductive reasoning. Experiment 1 demonstrated that by evoking the permission schema it is possible to facilitate performance in Wason's selection paradigm for subjects who have had (...) no experience with the specific content of the problems. Experiment 2 showed that a selection problem worded in terms of an abstract permission elicited better performance than one worded in terms of a concrete but arbitrary situation, providing evidence for an abstract permission schema that is free of domain-specific content. Experiment 3 provided evidence that evocation of a permission schema affects not only tasks requiring procedural knowledge, but also a linguistic rephrasing task requiring declarative knowledge. In particular, statements in the form if p then q were rephrased into the form p only if q with greater frequency for permission than for arbitrary statements, and rephrasings of permission statements produced a pattern of introduction of modals totally unlike that observed for arbitrary conditional statements. Other pragmatic schemas, such as “causal” and “evidence” schemas can account for both linguistic and reasoning phenomena that alternative hypotheses fail to explain. (shrink)

The present study examined the effects of semantic structure on simple inductive judgments about category members. For a particular category, subjects were told that one of the species had a given property and were asked to estimate the proportion of instances in the other species that possessed the property. The results indicated that category structure—in particular, the typicality of the species—influenced subjects' judgments. These results were interpreted by models based on the following assumption: When little is known about the underlying (...) distribution of a property, subjects assume that the distribution mirrors that of better-known properties. For this reason, if subjects learn that an unknown property is possessed by a typical species, they are more likely to generalize than if the same fact had been learned about an atypical species. (shrink)