Itzhak Gilboa, David Schmeidler's A Theory of Case-Based Decisions PDF

By Itzhak Gilboa, David Schmeidler

ISBN-10: 0521003113

ISBN-13: 9780521003117

Gilboa and Schmeidler offer a brand new paradigm for modeling determination making below uncertainty. Case-based selection conception means that humans make judgements via analogies to previous instances: they have a tendency to decide on acts that played good long ago in related events, and to prevent acts that played poorly. The authors describe the overall conception and its courting to making plans, repeated selection difficulties, inductive inference, and studying. They spotlight its mathematical and philosophical foundations and evaluate it to anticipated application idea in addition to to rule-based systems.

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"This textual content is a wonderful creation to optimization theory." Richard A. Chechile, magazine of Mathematical Psychology

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Read e-book online A Theory of Case-Based Decisions PDF

Gilboa and Schmeidler supply a brand new paradigm for modeling selection making less than uncertainty. Case-based choice idea means that humans make judgements via analogies to previous circumstances: they have a tendency to decide on acts that played good long ago in related occasions, and to prevent acts that played poorly.

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What should we do about observed deviations from the classical notion of rational choice? Should we refine our descriptive theories or dismiss the contradicting data as exceptions that can only clutter the basic rules? Should we teach our normative theories, or modify them? We find the definition of rationality given above useful in making these choices. If an observed mode of behavior is irrational for most people, one may suggest a normative recommendation to avoid that mode of behavior. By definition of irrationality, most people would accept this recommendation, rendering it a successful normative theory.

All she knows are the cases she has experienced, and she follows U-maximization as in (∗). Starting out with an empty memory, all acts are assigned a zero U-value. At this point the decision maker’s choice is arbitrary. Assume that she chose an act a that resulted in an undesirable outcome ra , that is, an outcome with u(ra ) < 0. Let us now consider the same decision maker confronted with the next problem. Suppose that this problem bears some similarity to the first one. In this case, act a will have a negative U-value (u(ra ) multiplied by the similarity of the two problems), whereas all other acts, which have empty histories, still have a U-value of zero.

We show that, under certain assumptions, U-maximization, but not V-maximization, leads to asymptotically optimal choice in a stochastically repeated decision problem. In view of this result, we tend to view U-maximization as the primary formula, despite the fact that it seems inappropriate for repeated problems with a constant aspiration level. 2 Act similarity While it stands to reason that past performance of an act would affect the act’s evaluation in current problems, it is not necessarily the case that past performance is the only relevant factor in the evaluation process.

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A Theory of Case-Based Decisions by Itzhak Gilboa, David Schmeidler


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