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Despite the debate and lack of agreement as to a formal definition of causality, we argue that people use systematic rules for assessing cause, both in science and everyday inference. By explicating the processes that underlie the judgment of causation, we review and integrate various theories of causality proposed by psychologists, philosophers, statisticians, and others. Because causal judgment involves inference and uncertainty, the literature on judgment-under-uncertainty is also considered. Our review is organized around four concepts, (a) The idea of a "causal field" is central for determining causal relevance, differentiating causes from conditions, determining the salience of alternative explanations, and affecting molar versus molecular explanations, (b) Various "cues-to-causality," such as covariation, temporal order, contiguity in time and space, and similarity of cause and effect, are discussed. In doing so, we show how these cues can conflict with probabilistic ideas, (c) A model for combining the cues and the causal field allows us to discuss a wide range of studies on causal judgments and explicates methodological issues such as spurious correlation, "causalation," and causal inference in case studies, (d) The discounting of an explanation by specific alternatives is discussed as a special case of the sequential updating of beliefs. Finally, we extend our approach to consider conjunctive explanations in multiple causation.

Notes that an accumulating body of research on clinical judgment, decision making, and probability estimation has documented a substantial lack of ability of both experts and nonexperts. However, evidence shows that people have great confidence in their fallible judgment. This article examines how this contradiction can be resolved and, in so doing, discusses the relationship between learning and experience. The basic tasks that are considered involve judgments made for the purpose of choosing between actions. At some later time, outcome feedback is used for evaluating the accuracy of judgment. The manner in which judgments of the contingency between predictions and outcomes are made is discussed and is related to the difficulty people have in searching for disconfirming information to test hypotheses. A model for learning and maintaining confidence in one's own judgment is developed that includes the effects of experience and both the frequency and importance of positive and negative feedback. (78 ref)

Ambiguity results from having limited knowledge of the process that generates outcomes. As Ellsberg (1961) demonstrated, this poses problems for theories of probability operationally denned via choices amongst gambles. A descriptive model of how people make judgments under ambiguity is proposed. The model assumes an anchoring-and-adjustment process in which an initial estimate provides the anchor, and adjustments are made for what might be. The latter is modeled as the result of a mental simulation process that reflects two factors: (a) the amount of ambiguity, which affects the size of the simulation, and (b) one's attitude toward ambiguity, which affects the differential weighting of imagined probabilities. A twoparameter model of this process is shown to be consistent with Ellsberg's original paradox, the nonadditivity of complementary probabilities, current psychological theories of risk, and Keynes's idea of the "weight of evidence." The model is tested in four experiments using individual and group analyses. In Experiments 1 and 2, the model accurately predicts inferential judgments; in Experiment 3, the model predicts choices between gambles; and in Experiment 4 it shows how buying and selling prices for insurance are influenced by one's attitude toward ambiguity. The results are then discussed with respect to (a) the importance of ambiguity in assessing uncertainty, (b) the use of cognitive strategies in judgments under ambiguity, (c) the role of ambiguity in risky choice, and (d) extensions of the model.The literature on how people make judg-1977). One reason for the controversy is that ments under uncertainty is large, complex, and although there is agreement that uncertainty rife with controversy (see, e.g., Cohen, 1981; is a crucial factor in inference, there is much