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

The enterprise of knowledge

Some men disclaim certainty about anything. I am certain that they deceive themselves. Be that as it may, only the arrogant and foolish maintain that they are certain about everything. It is appropriate, therefore, to consider how judgments of uncertainty discriminate between hypotheses with respect to grades of uncertainty, probability, belief, or credence. Discriminations of this sort are relevant to the conduct of deliberations aimed at making choices between rival policies not only in the context of games of chance, but in moral, political, economic, or scientific decision making. If agent X wishes to promote some aim or system of values, he will (ceteris paribus) favour a policy that guarantees him against failure over a policy that does not. Where no guarantee is to be obtained, he will (or should) favor a policy that reduces the probability of failure to the greatest degree feasible. At any rate, this is so when X is engaged in deliberate decision making (as opposed to habitual or routine choice).

On Indeterminate Probabilities

Preface 1. Moral struggle 2. Dilemmas 3. Values in scientific inquiry 4. Choice and foreknowledge 5. Value structures 6. Values revealed by choices 7. Uncertainty as a source of conflict 8. Conflict and social agency 9. Distributing benefits 10. Utilarianism and conflict 11. Social choice theory 12. Conflict and inquiry Notes Bibliography Name index Subject index.

Hard Choices: Decision Making under Unresolved Conflict

This book presents a major conceptual and speculative philosophic investigation of knowledge, belief, and decision. It offers a distinctive approach to the improvement of knowledge where knowledge is construed as a resource for deliberation and inquiry.The first three chapters of the book address the question of the revision of knowledge from a highly original point of view, one that takes issue with the fallibilist doctrines of Peirce and Popper, and with the views of Dewey, Quine, and Kuhn as well.The next ten chapters are more technical in nature but require relatively little background in mathematical technique. Among the topics discussed are inductive logic and inductive probability, utility theory, rational decision making, value conflict, chance (statistical probability), direct inference, and inverse inference.Chapters 14-17 review alternative approaches to the topic of inverse statistical inference. Much of the discussion focuses on contrasting Bayesian and anti-Bayesian reactions to R. A. Fisher's fiducial argument. This section of the book concludes with a discussion of the Neyman-Pearson-Wald approach to the foundations of statistical inference.The final chapter returns to the epistemological themes with which the book opened, emphasizing the question of the objectivity of human inquiry. An appendix provides a real-world application of Levi's theories of knowledge and probability, offering a critique of some of the methodological procedures employed in the Rasmussen Report to assess risks of major accidents in nuclear power plants. There are also references and an index."The Enterprise of Knowledge" will interest professionals and students in epistemology, philosophy of science, decision theory, probability theory, and statistical inference.

The Enterprise of Knowledge: An Essay on Knowledge, Credal Probability, and Chance

X says ‘It is probable that h’ and Y says ‘It is improbable that h’. No doubt X and Y disagree in some ways. In particular, they disagree in the way they evaluate h with respect to credal (or personal) probability to be used in practical deliberation and scientific inquiry in computing expectations.

Isaac Levi

Papers

The enterprise of knowledge

On Indeterminate Probabilities

Hard Choices: Decision Making under Unresolved Conflict

The Enterprise of Knowledge: An Essay on Knowledge, Credal Probability, and Chance

Subjunctives, dispositions and chances