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)

Humans and animals make inferences about the world under limited time and knowledge. In contrast, many models of rational inference treat the mind as a Laplacean Demon, equipped with unlimited time, knowledge, and computational might. Following H. Simon's notion of satisficing, the authors have proposed a family of algorithms based on a simple psychological mechanism: one-reason decision making. These fast and frugal algorithms violate fundamental tenets of classical rationality: They neither look up nor integrate all information. By computer simulation, the (...) authors held a competition between the satisficing "Take The Best" algorithm and various "rational" inference procedures. The Take The Best algorithm matched or outperformed all competitors in inferential speed and accuracy. This result is an existence proof that cognitive mechanisms capable of successful performance in the real world do not need to satisfy the classical norms of rational inference. (shrink)

A detailed treatment regarding the diverse properties and uses of evidence and the judgmental tasks they entail. Examines various processes by which evidence may be developed or discovered. Considers the construction of arguments made in defense of the relevance and credibility of individual items and masses of evidence as well as the task of assessing the inferential force of evidence. Includes over 100 numerical examples to illustrate the workings of diverse probabilistic expressions for the inferential force of evidence and the (...) subtleties they reveal. (shrink)

"Fascinating and insightful.... I cannot recall a book that has made me think more about the nature of thinking." -- Richard C. Lewontin Harvard University Everyone knows that optical illusions trick us because of the way we see. Now scientists have discovered that cognitive illusions, a set of biases deeply embedded in the human mind, can actually distort the way we think. In Inevitable Illusions, distinguished cognitive researcher Massimo Piattelli-Palmarini takes us on a provocative, challenging, and thoroughly entertaining exploration of (...) the games our minds play. He opens the doors onto the newly charted realm of the cognitive unconscious to reveal the full range of illusions, showing how they inhibit our ability to reason--no matter what our educational background or IQ. Inevitable Illusions is stimulating, eye-opening food for thought. (shrink)

Four studies examined how people combine social concepts that have conflicting implications (e.g., Harvard‐educated and carpenter). Several kinds of evidence indicated that such combinations are guided by causal reasoning that draws upon both causal relations contained within the constituent concepts and on broader world knowledge. Open‐ended descriptions of members of combinations contained explicit causal descriptors, as well as emergent attributes not used to describe members of constituents. Ratings of the likelihood that combination members possessed various attributes were not fully predicted (...) by comparable ratings of constituents. Causal reasoning appeared to be most pervasive for combinations viewed as more surprising, suggesting that surprise may have triggered the generation of causal accounts. (shrink)

The paper examines constraints and preferences employed by people in learning decision rules from preclassified examples. Results from four experiments with human subjects were analyzed and compared with artificial intelligence (AI) inductive learning programs. The results showed the people's rule inductions tended to emphasize category validity (probability of some property, given a category) more than cue validity (probability that an entity is a member of a category given that it has some property) to a greater extent than did the AI (...) programs. Although the relative proportions of different rule types (e.g., conjunctive vs. disjunctive) changed across experiments, a single process model provided a good account of the data from each study. These observations are used to argue for describing constraints in terms of processes embodied in models rather than in terms of products or outputs. Thus AI induction programs become candidate psychological process models and results from inductive learning experiments can suggest new algorithms. More generally, the results show that human inductive generalizations tend toward greater specificity than would be expected if conceptual simplicity were the key constraint on inductions. This bias toward specificity may be due to the fact that this criterion both maximizes inferences that may be drawn from category membership and protects rule induction systems from developing over‐generalizations. (shrink)

Subjects were requested to choose between gambles, where the outcome of one gamble depended on a single elementary event, and the other depended on an event compounded of a series of such elementary events. The data supported the hypothesis that the subjective probability of a compound event is systematically biased in the direction of the probability of its components resulting in overestimation of conjunctive events and underestimation of disjunctive events. Studies pertaining to this topic are discussed.