From the Publisher:
Probabilistic Reasoning in Intelligent Systems is a complete andaccessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty. The author provides a coherent explication of probability as a language for reasoning with partial belief and offers a unifying perspective on other AI approaches to uncertainty, such as the Dempster-Shafer formalism, truth maintenance systems, and nonmonotonic logic. The author distinguishes syntactic and semantic approaches to uncertaintyand offers techniques, based on belief networks, that provide a mechanism for making semantics-based systems operational. Specifically, network-propagation techniques serve as a mechanism for combining the theoretical coherence of probability theory with modern demands of reasoning-systems technology: modular declarative inputs, conceptually meaningful inferences, and parallel distributed computation. Application areas include diagnosis, forecasting, image interpretation, multi-sensor fusion, decision support systems, plan recognition, planning, speech recognitionin short, almost every task requiring that conclusions be drawn from uncertain clues and incomplete information.
Probabilistic Reasoning in Intelligent Systems will be of special interest to scholars and researchers in AI, decision theory, statistics, logic, philosophy, cognitive psychology, and the management sciences. Professionals in the areas of knowledge-based systems, operations research, engineering, and statistics will find theoretical and computational tools of immediate practical use. The book can also be used as an excellent text for graduate-level courses in AI, operations research, or applied probability.

Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference

A review is presented of the book “Heuristics and Biases: The Psychology of Intuitive Judgment,” edited by Thomas Gilovich, Dale Griffin, and Daniel Kahneman.

Heuristics and Biases: The Psychology of Intuitive Judgment

Perhaps the simplest and the most basic qualitative law of probability is the conjunction rule: The probability of a conjunction, P (A&B) cannot exceed the probabilities of its constituents, P (A) and P (B), because the extension (or the possibility set) of the conjunction is included in the extension of its constituents. Judgments under uncertainty, however, are often mediated by intuitive heuristics that are not bound by the conjunction rule. A conjunction can be more representative than one of its constituents, and instances of a specific category can be easier to imagine or to retrieve than instances of a more inclusive category. The representativeness and availability heuristics therefore can make a conjunction appear more probable than one of its constituents. This phenomenon is demonstrated in a variety of contexts including estimation of word frequency, personality judgment, medical prognosis, decision under risk, suspicion of criminal acts, and political forecasting. Systematic violations of the conjunction rule are observed in judgments of lay people and of experts in both between-subjects and within-subjects comparisons. Alternative interpretations of the conjunction fallacy are discussed and attempts to combat it are explored.

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Extensional versus intuitive reasoning: The conjunction fallacy in probability judgment.

Much research in the last two decades has demon- strated that human responses deviate from the performance deemed normative according to various models of decision mak- ing and rational judgment (e.g., the basic axioms of utility theory). This gap between the normative and the descriptive can be inter- preted as indicating systematic irrationalities in human cognition. However, four alternative interpretations preserve the assumption that human behavior and cognition is largely rational. These posit that the gap is due to (1) performance errors, (2) computational limitations, (3) the wrong norm being applied by the experi- menter, and (4) a different construal of the task by the subject. In the debates about the viability of these alternative explanations, attention has been focused too narrowly on the modal response. In a series of experiments involving most of the classic tasks in the heuristics and biases literature, we have examined the implica- tions of individual differences in performance for each of the four explanations of the normative/descriptive gap. Performance er- rors are a minor factor in the gap; computational limitations un- derlie non-normative responding on several tasks, particularly those that involve some type of cognitive decontextualization. Un- expected patterns of covariance can suggest when the wrong norm is being applied to a task or when an alternative construal of the task should be considered appropriate.

https://www.cambridge.org/core/services/aop-cambridge-core/content/view/S0140525X00003435

Individual differences in reasoning: Implications for the rationality debate?

A theory of norms and normality is presented and applied to some phenomena of emotional responses, social judgment, and conversations about causes. Norms are assumed to be constructed ad hoc by recruiting specific representations. Category norms are derived by recruiting exemplars. Specific objects or events generate their own norms by retrieval of similar experiences stored in memory or by construction of counterfactual alternatives. The normality of a stimulus is evaluated by comparing it to the norms that it evokes after the fact, rather than to precomputed expectations. Norm theory is applied in analyses of the enhanced emotional response to events that have abnormal causes, of the generation of predictions and inferences from observations of behavior, and of the role of norms in causal questions and answers. This article is concerned with category norms that represent knowledge of concepts and with stimulus norms that govern comparative judgments and designate experiences as surprising. In the tradition of adaptation level theory (Appley, 1971; Helson, 1964), the concept of norm is applied to events that range in complexity from single visual displays to social interactions. We first propose a model of an activation process that produces norms, then explore the role of norms in social cognition. The central idea of the present treatment is that norms are computed after the event rather than in advance. We sketch a supplement to the generally accepted idea that events in the stream of experience are interpreted and evaluated by consulting precomputed schemas and frames of reference. The view developed here is that each stimulus selectively recruits its own alternatives (Garner, 1962, 1970) and is interpreted in a rich context of remembered and constructed representations of what it could have been, might have been, or should have been. Thus, each event brings its own frame of reference into being. We also explore the idea that knowledge of categories (e.g., "encounters with Jim") can be derived on-line by selectively evoking stored representations of discrete episodes and exemplars. The present model assumes that a number of representations can be recruited in parallel, by either a stimulus event or an

https://www3.canyons.edu/faculty/rafterm/0%200LLI%20LOVEandLOSS/Day%206%20Files/1986%20-%20Norm%20Theory%20-%20Comparing%20Reality%20to%20Its%20Alternatives.pdf

Norm theory: Comparing reality to its alternatives

In a pair of controversy provoking papers1, Kadane and Larkey argued that the normative or prescriptive understanding of expected utility theory recommended that participants in a game maximize expected utility given their assessments of the probabilities of the moves that other players would make. They observed that no prescription, norm or standard of Bayesian rationality recommends how they should come to make probability judgments about the choices of other players. For any given player, it is an empirical question as to whether other players are Bayes rational, judge him to be Bayes rational, etc., just as it is an empirical question as to what the other players’ goals and beliefs are. Participants in the game should, of course, use all empirical information available to them about how other players have behaved and how they are likely to behave in the given context of choice. Kadane and Larkey pointed out that rational players could participate in a game meeting these requirements without the game’s terminating with a Nash equilibrium or a result satisfying any other particular solution concept.

Prediction, Bayesian Deliberation and Correlated Equilibrium

But Fair to Chance

Truth, Content, and Ties

Charles Peirce insisted a long time ago that justifying beliefs currently held is unnecessary. Only changes in belief require justification. Epistemologists should turn away from the question of justifying what they already believe and focus instead on when and how rational inquirers should change their minds.

Changing Probability Judgements

The recent publication of these four books is testimony to the fact that serious philosophical studies on probability, confirmation and induction are tending more and more to steer clear of Hume's problem and to concentrate on developing systematic accounts of criteria for sound inductive inferences and decision making under uncertainty. In Information and Confirmation, H~tkan TSrnebohm examines the properties and interrelations of several proposed measures of evidential support or confirmation especially when they are applied to arguments from finite samples to finite populations and from finite populations to finite samples. The measures especially selected by Trrnebohm for study include range measures of probability, likelihoods and measures of semantic information. One of the objectives of I. J. Good 's book, The Estimation of Probabilities is " to bridge the gap between the philosopher and the practical statistician".: Whether he has succeeded or not, he has written a book which every philosopher interested in the foundations of statistics will find useful. The central problem tackled by Good is that of determining prior probabili ty distributions for use in deriving posterior probabilities with the aid of likelihoods via Bayes theorem. In examining this question, Good does not find it necessary to commit hostages to one specific interpretation of the calculus of probabilities. Although he offers a valuable catalogue of interpretations and has much to say of interest on this score, he attempts to develop his discussion in a manner which is as neutral as possible regarding interpretation.

Isaac Levi

Papers

Prediction, Bayesian Deliberation and Correlated Equilibrium

But Fair to Chance

Truth, Content, and Ties

Changing Probability Judgements

Recent work in probability and induction (review article)