A Theory of Case-Based Decisions

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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.
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Editorial Reviews

From the Publisher
"This text is an excellent introduction to optimization theory." Richard A. Chechile, Journal of Mathematical Psychology
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Product Details

  • ISBN-13: 9780521003117
  • Publisher: Cambridge University Press
  • Publication date: 9/1/2001
  • Edition description: New Edition
  • Pages: 210
  • Product dimensions: 5.43 (w) x 8.50 (h) x 0.47 (d)

Meet the Author

ITZHAK GILBOA

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Table of Contents

Acknowledgments
1 Prologue 1
1 The scope of this book 1
2 Meta-theoretical vocabulary 4
3 Meta-theoretical prejudices 22
2 Decision rules 29
4 Elementary formula and interpretations 29
5 Variations and generalizations 47
6 CBDT as a behaviorist theory 53
7 Case-based prediction 59
3 Axiomatic derivation 62
8 Highlights 62
9 Model and result 64
10 Discussion of the axioms 73
11 Proofs 77
4 Conceptual foundations 91
12 CBDT and expected utility theory 91
13 CBDT and rule-based systems 98
5 Planning 109
14 Representation and evaluation of plans 109
15 Axiomatic derivation 119
6 Repeated choice 125
16 Cumulative utility maximization 125
17 The potential 136
7 Learning and induction 146
18 Learning to maximize expected payoff 146
19 Learning the similarity function 174
20 Two views of induction: CBDT and simplicism 183
Bibliography 189
Index 197
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