A Theory of Case-Based Decisions.

By: Gilboa, ItzhakContributor(s): Schmeidler, DavidMaterial type: TextTextPublisher: Cambridge : Cambridge University Press, 2001Copyright date: ©2001Description: 1 online resource (211 pages)Content type: text Media type: computer Carrier type: online resourceISBN: 9780511153303Subject(s): Decision making--Mathematical modelsGenre/Form: Electronic books.Additional physical formats: Print version:: A Theory of Case-Based DecisionsDDC classification: 003.56 LOC classification: HD30.23 .G53 2001Online resources: Click to View
Contents:
Cover -- Half-title -- Title -- Copyright -- Dedication -- CONTENTS -- ACKNOWLEDGMENTS -- CHAPTER 1 PROLOGUE -- 1 The scope of this book -- 2 Meta-theoretical vocabulary -- 2.1 Theories and conceptual frameworks -- 2.2 Descriptive and normative theories -- 2.3 Axiomatizations -- 2.4 Behaviorist, behavioral, and cognitive theories -- 2.5 Rationality -- 2.6 Deviations from rationality -- 2.7 Subjective and objective terms -- 3 Meta-theoretical prejudices -- 3.1 Preliminary remark on the philosophy of science -- 3.2 Utility and expected utility "theories" as conceptual frameworks and as theories -- 3.3 On the validity of purely behavioral economic theory -- 3.4 What does all this have to do with CBDT? -- CHAPTER 2 DECISION RULES -- 4 Elementary formula and interpretations -- 4.1 Motivating examples -- 4.2 Model -- 4.3 Aspirations and satisficing -- 4.4 Comparison with EUT -- 4.5 Comments -- 5 Variations and generalizations -- 5.1 Average similarity -- 5.2 Act similarity -- 5.3 Case similarity -- 6 CBDT as a behaviorist theory -- 6.1 W-maximization -- 6.2 Cognitive specification: EUT -- 6.3 Cognitive specification: CBDT -- 6.4 Comparing the cognitive specifications -- 7 Case-based prediction -- CHAPTER 3 AXIOMATIC DERIVATION -- 8 Highlights -- 9 Model and result -- 9.1 Axioms -- 9.2 Basic result -- 9.3 Learning new cases -- 9.4 Equivalent cases -- 9.5 U-maximization -- 10 Discussion of the axioms -- 11 Proofs -- CHAPTER 4 CONCEPTUAL FOUNDATIONS -- 12 CBDT and expected utility theory -- 12.1 Reduction of theories -- 12.2 Hypothetical reasoning -- 12.3 Observability of data -- 12.4 The primacy of similarity -- 12.5 Bounded rationality? -- 13 CBDT and rule-based systems -- 13.1 What can be known? -- 13.2 Deriving case-based decision theory -- 13.3 Implicit knowledge of rules -- 13.4 Two roles of rules -- CHAPTER 5 PLANNING.
14 Representation and evaluation of plans -- 14.1 Dissection, selection, and recombination -- 14.2 Representing uncertainty -- 14.3 Plan evaluation -- 14.4 Discussion -- 15 Axiomatic derivation -- 15.1 Set-up -- 15.2 Axioms and result -- 15.3 Proof -- CHAPTE 6 REPEATED CHOICE -- 16 Cumulative utility maximization -- 16.1 Memory-dependent preferences -- 16.2 Related literature -- 16.3 Model and results -- 16.4 Comments -- 16.5 Proofs -- 17 The potential -- 17.1 Definition -- 17.2 Normalized potential and neo-classical utility -- 17.3 Substitution and complementarity -- CHAPTER 7 LEARNING AND INDUCTION -- 18 Learning to maximize expected payoff -- 18.1 Aspiration-level adjustment -- 18.2 Realism and ambitiousness -- 18.3 Highlights -- 18.4 Model -- 18.5 Results -- 18.6 Comments -- 18.7 Proofs -- 19 Learning the similarity function -- 19.1 Examples -- 19.2 Counter-example to U-maximization -- 19.3 Learning and expertise -- 20 Two views of induction: CBDT and simplicism -- 20.1 Wittgenstein and Hume -- 20.2 Examples -- BIBLIOGRAPHY -- INDEX.
Summary: Gilboa and Schmeidler provide paradigm for modelling decision making under uncertainty.
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Cover -- Half-title -- Title -- Copyright -- Dedication -- CONTENTS -- ACKNOWLEDGMENTS -- CHAPTER 1 PROLOGUE -- 1 The scope of this book -- 2 Meta-theoretical vocabulary -- 2.1 Theories and conceptual frameworks -- 2.2 Descriptive and normative theories -- 2.3 Axiomatizations -- 2.4 Behaviorist, behavioral, and cognitive theories -- 2.5 Rationality -- 2.6 Deviations from rationality -- 2.7 Subjective and objective terms -- 3 Meta-theoretical prejudices -- 3.1 Preliminary remark on the philosophy of science -- 3.2 Utility and expected utility "theories" as conceptual frameworks and as theories -- 3.3 On the validity of purely behavioral economic theory -- 3.4 What does all this have to do with CBDT? -- CHAPTER 2 DECISION RULES -- 4 Elementary formula and interpretations -- 4.1 Motivating examples -- 4.2 Model -- 4.3 Aspirations and satisficing -- 4.4 Comparison with EUT -- 4.5 Comments -- 5 Variations and generalizations -- 5.1 Average similarity -- 5.2 Act similarity -- 5.3 Case similarity -- 6 CBDT as a behaviorist theory -- 6.1 W-maximization -- 6.2 Cognitive specification: EUT -- 6.3 Cognitive specification: CBDT -- 6.4 Comparing the cognitive specifications -- 7 Case-based prediction -- CHAPTER 3 AXIOMATIC DERIVATION -- 8 Highlights -- 9 Model and result -- 9.1 Axioms -- 9.2 Basic result -- 9.3 Learning new cases -- 9.4 Equivalent cases -- 9.5 U-maximization -- 10 Discussion of the axioms -- 11 Proofs -- CHAPTER 4 CONCEPTUAL FOUNDATIONS -- 12 CBDT and expected utility theory -- 12.1 Reduction of theories -- 12.2 Hypothetical reasoning -- 12.3 Observability of data -- 12.4 The primacy of similarity -- 12.5 Bounded rationality? -- 13 CBDT and rule-based systems -- 13.1 What can be known? -- 13.2 Deriving case-based decision theory -- 13.3 Implicit knowledge of rules -- 13.4 Two roles of rules -- CHAPTER 5 PLANNING.

14 Representation and evaluation of plans -- 14.1 Dissection, selection, and recombination -- 14.2 Representing uncertainty -- 14.3 Plan evaluation -- 14.4 Discussion -- 15 Axiomatic derivation -- 15.1 Set-up -- 15.2 Axioms and result -- 15.3 Proof -- CHAPTE 6 REPEATED CHOICE -- 16 Cumulative utility maximization -- 16.1 Memory-dependent preferences -- 16.2 Related literature -- 16.3 Model and results -- 16.4 Comments -- 16.5 Proofs -- 17 The potential -- 17.1 Definition -- 17.2 Normalized potential and neo-classical utility -- 17.3 Substitution and complementarity -- CHAPTER 7 LEARNING AND INDUCTION -- 18 Learning to maximize expected payoff -- 18.1 Aspiration-level adjustment -- 18.2 Realism and ambitiousness -- 18.3 Highlights -- 18.4 Model -- 18.5 Results -- 18.6 Comments -- 18.7 Proofs -- 19 Learning the similarity function -- 19.1 Examples -- 19.2 Counter-example to U-maximization -- 19.3 Learning and expertise -- 20 Two views of induction: CBDT and simplicism -- 20.1 Wittgenstein and Hume -- 20.2 Examples -- BIBLIOGRAPHY -- INDEX.

Gilboa and Schmeidler provide paradigm for modelling decision making under uncertainty.

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Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2018. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.

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