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 of Rational Choice (2010).

Chapter 1 Background. Suggested Reading. Chapter 2 Judgment and Choice Biases. Introduction. Problems - Group A. Problems - Group B. Framing Effects. Brainstorming and Formal Models. Endowment Effect. Sunk Costs. Decision Trees. Representativeness Heuristic. Availability Heuristic. Anchoring. Mental Accounting. Dynamic Inconsistency. Exercises. Chapter 3 Consuming Statistical Data. Introduction. Problems. Conditional Probabilities. Gambler''s Fallacy. Biased Samples. Regression to the Mean. Correlation and Causation. Statistical Significance. Bayesian and Classical Statistics. Exercises. Chapter 4 Decisions under Risk. Introduction. Problems. The Independence Axiom. von-Neumann and Morgenstern''s Result. Measurement of Utility. Risk Aversion. Prospect Theory. Exercises. Chapter 5 Decisions under Uncertainty. Introduction. Problems. Subjective Probability. Learning from the fact we know. Causality. The Sure Thing Principle. Alternative Models. Objective Probabilities. Exercises. Chapter 6 Well-Being and Happiness. Introduction. Problems - Group A. Problems - Group B. Well Being. Measurement Issues. What''s Happiness? Exercises. Chapter 7 Appendix A: Optimal Choice. Chapter 8 Appendix B: Probability and Statistics. Solutions.