Making Better Decisions: Decision Theory in Practice / Edition 1

Making Better Decisions: Decision Theory in Practice / Edition 1

by Itzhak Gilboa
     
 

Making Better Decisions: Decision Theory in Practice introduces readers to some of the principal ideas from decision theory and examines how they might help us make better decisions.

The presentation is designed to appeal to students and the general reader; based on problems, readers are encouraged to imagine a situation, and then make a decision or

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Overview

Making Better Decisions: Decision Theory in Practice introduces readers to some of the principal ideas from decision theory and examines how they might help us make better decisions.

The presentation is designed to appeal to students and the general reader; based on problems, readers are encouraged to imagine a situation, and then make a decision or a judgment. The problems are chosen to exemplify some principles developed in decision theory, as well as violations of these principles derived from the psychological literature. 

Making Better Decisions offers explanations of both the theories we would like to adopt in order to make better decisions, and the theories that explain how those around us behave. In doing so, the book presents crucial insights into the decision-making process that can influence and change our behavior and our ability to interact with those around us.

Product Details

ISBN-13:
9781444336511
Publisher:
Wiley
Publication date:
10/19/2010
Pages:
232
Product dimensions:
6.10(w) x 9.10(h) x 0.90(d)

Table of Contents

Preface.

Acknowledgments.

1. Background.

Suggested Reading.

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.

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.

4. Decisions under Risk.

Introduction.

Problems.

The Independence Axiom.

Von Neumann and Morgenstern’s Result.

Measurement of Utility.

Risk Aversion.

Prospect Theory.

Exercises.

5. Decisions under Uncertainty.

Introduction.

Problems.

Subjective Probability.

Learning From the Fact We Know.

Causality.

The Sure Thing Principle.

Alternative Models.

Objective Probabilities.

Exercises.

6. Well-Being and Happiness.

Introduction.

Problems – Group A.

Problems – Group B.

Well-Being.

Measurement Issues.

What’s Happiness?

Exercises.

Appendix A: Optimal Choice.

Appendix B: Probability and Statistics.

Solutions.

Index.

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