A Theory of Case-Based Decisions

A Theory of Case-Based Decisions

by Itzhak Gilboa, David Schmeidler
     
 

ISBN-10: 0521003113

ISBN-13: 9780521003117

Pub. Date: 09/01/2001

Publisher: Cambridge University Press

Gilboa and Schmeidler provide a new paradigm for modeling decision making under uncertainty. Case-based decision theory suggests that people make decisions by analogies to past cases: they tend to choose acts that performed well in the past in similar situations, and to avoid acts that performed poorly. The authors describe the general theory and its relationship to…  See more details below

Overview

Gilboa and Schmeidler provide a new paradigm for modeling decision making under uncertainty. Case-based decision theory suggests that people make decisions by analogies to past cases: they tend to choose acts that performed well in the past in similar situations, and to avoid acts that performed poorly. The authors describe the general theory and its relationship to planning, repeated choice problems, inductive inference, and learning. They highlight its mathematical and philosophical foundations and compare it to expected utility theory as well as to rule-based systems.

Product Details

ISBN-13:
9780521003117
Publisher:
Cambridge University Press
Publication date:
09/01/2001
Edition description:
New Edition
Pages:
210
Product dimensions:
5.43(w) x 8.50(h) x 0.47(d)

Table of Contents

Acknowledgments
1Prologue1
1The scope of this book1
2Meta-theoretical vocabulary4
3Meta-theoretical prejudices22
2Decision rules29
4Elementary formula and interpretations29
5Variations and generalizations47
6CBDT as a behaviorist theory53
7Case-based prediction59
3Axiomatic derivation62
8Highlights62
9Model and result64
10Discussion of the axioms73
11Proofs77
4Conceptual foundations91
12CBDT and expected utility theory91
13CBDT and rule-based systems98
5Planning109
14Representation and evaluation of plans109
15Axiomatic derivation119
6Repeated choice125
16Cumulative utility maximization125
17The potential136
7Learning and induction146
18Learning to maximize expected payoff146
19Learning the similarity function174
20Two views of induction: CBDT and simplicism183
Bibliography189
Index197

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