Making Better Decisions Decision Theory in

Making Better Decisions introduces readers to some of the principal aspects of decision theory, and examines how these might lead us to make better decisions.- Introduces readers to key aspects of decision theory and examines how they might help us make better decisions - Presentation of material encourages readers to imagine a situation and make a decision or a judgment - Offers a broad coverage of the subject including major insights from several sub-disciplines: microeconomic theory, decision theory, game theory, social choice, statistics, psychology, and philosophy - Explains these insights informally in a language that has minimal mathematical notation or jargon, even when describing and interpreting mathematical theorems - Critically assesses the theory presented within the text, as well as some of its critiques - Includes a web resource for teachers and students

Itzhak Gilboa is a chaired Professor in the Department of Economics and Decision Sciences at HEC, Paris, and in the Eitan Berglas School of Economics at Tel Aviv University, and Fellow of the Cowles Foundation at Yale University. He previously held the position of chaired Professor at the Kellogg School of Management at Northwestern University. Gilboa's research focuses on decision under uncertainty. He has worked with David Schmeidler on axiomatic foundation of non-Bayesian decision theory and contributed to research in complexity in game theory, evolutionary game theory, and social choice. He is co-author of A Theory of Case-Based Decisions (with David Schmeidler, 2001), and author of Theory of Decision under Uncertainty (2009) and Rational Choice (2010).

Preface	p. viii
Acknowledgments	p. x
Background	p. 1
Suggested Reading	p. 5
Judgment and Choice Biases	p. 6
Introduction	p. 6
Problems - Group A	p. 9
Problems - Group B	p. 12
Framing Effects	p. 15
Brainstorming and Formal Models	p. 20
Endowment Effect	p. 22
Sunk Costs	p. 27
Decision Trees	p. 30
Representativeness Heuristic	p. 34
Availability Heuristic	p. 39
Anchoring	p. 44
Mental Accounting	p. 46
Dynamic Inconsistency	p. 51
Exercises	p. 53
Consuming Statistical Data	p. 57
Introduction	p. 57
Problems	p. 58
Conditional Probabilities	p. 63
Gambler's Fallacy	p. 72
Biased Samples	p. 77
Regression to the Mean	p. 80
Correlation and Causation	p. 81
Statistical Significance	p. 84
Bayesian and Classical Statistics	p. 85
Exercises	p. 93
Decisions under Risk	p. 98
Introduction	p. 98
Problems	p. 99
The Independence Axiom	p. 102
Von Neumann and Morgenstern's Result	p. 110
Measurement of Utility	p. 113
Risk Aversion	p. 116
Prospect Theory	p. 123
Exercises	p. 130
Decisions under Uncertainty	p. 133
Introduction	p. 133
Problems	p. 134
Subjective Probability	p. 141
Learning From the Fact We Know	p. 151
Causality	p. 163
The Sure Thing Principle	p. 166
Alternative Models	p. 171
Objective Probabilities	p. 172
Exercises	p. 174
Well-Being and Happiness	p. 178
Introduction	p. 178
Problems - Group A	p. 179
Problems - Group B	p. 180
Well-Being	p. 181
Measurement Issues	p. 184
What's Happiness?	p. 186
Exercises	p. 188
Optimal Choice	p. 191
Probability and Statistics	p. 195
Solutions	p. 204
Index	p. 210
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Making Better Decisions Decision Theory in Practice

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