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 following values have no corresponding Zotero field: PB - JSTOR.

This paper presents a decision theory which allows subjects to account for the uncertainties of their probability estimates. This is accomplished by modelling beliefs about states of nature by means of a class of probability measures. In order to represent uncertainties of those beliefs a measure of epistemic reliability is introduced. The suggested decision theory is evaluated in the light of empirical evidence on ambiguity and uncertainty in decision making. The theory is also compared to Tversky & Kahneman's prospect theory.

The object of this paper is to show why recent research in the psychology of deductive and probabilistic reasoning does not have.