Considers that intuitive predictions follow a judgmental heuristic-representativeness. By this heuristic, people predict the outcome that appears most representative of the evidence. Consequently, intuitive predictions are insensitive to the reliability of the evidence or to the prior probability of the outcome, in violation of the logic of statistical prediction. The hypothesis that people predict by representativeness was supported in a series of studies with both naive and sophisticated university students. The ranking of outcomes by likelihood coincided with the ranking by (...) representativeness, and Ss erroneously predicted rare events and extreme values if these happened to be representative. The experience of unjustified confidence in predictions and the prevalence of fallacious intuitions concerning statistical regression are traced to the representativeness heuristic. (shrink)

The book can also be used as an excellent text for graduate-level courses in AI, operations research, or applied probability.

Causality offers the first comprehensive coverage of causal analysis in many sciences, including recent advances using graphical methods. Pearl presents a unified account of the probabilistic, manipulative, counterfactual and structural approaches to causation, and devises simple mathematical tools for analyzing the relationships between causal connections, statistical associations, actions and observations. The book will open the way for including causal analysis in the standard curriculum of statistics, artificial intelligence, business, epidemiology, social science and economics.

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)

A leading expert in informal logic, Douglas Walton turns his attention in this new book to how reasoning operates in trials and other legal contexts, with special emphasis on the law of evidence. The new model he develops, drawing on methods of argumentation theory that are gaining wide acceptance in computing fields like artificial intelligence, can be used to identify, analyze, and evaluate specific types of legal argument. In contrast with approaches that rely on deductive and inductive logic and rule (...) out many common types of argument as fallacious, Walton’s aim is to provide a more expansive view of what can be considered "reasonable" in legal argument when it is construed as a dynamic, rule-governed, and goal-directed conversation. This dialogical model gives new meaning to the key notions of relevance and probative weight, with the latter analyzed in terms of pragmatic criteria for what constitutes plausible evidence rather than truth. (shrink)