Decision Theory and Decision Analysis: Trends and Challenges, Softcover reprint of the original 1st ed. 1994

Decision Theory and Decision Analysis: Trends and Challenges is divided into three parts. The first part, overviews, provides state-of-the-art surveys of various aspects of decision analysis and utility theory. The second part, theory and foundations, includes theoretical contributions on decision-making under uncertainty, partial beliefs and preferences. The third section, applications, reflects the real possibilities of recent theoretical developments such as non-expected utility theories, multicriteria decision techniques, and how these improve our understanding of other areas including artificial intelligence, economics, and environmental studies.

I Overviews.- 1. The Prescriptive Orientation of Decision Making: A Synthesis of Decision Analysis, Behavioral Decision Making, and Game Theory.- 2. A Perspective on Recent Developments in Utility Theory.- 3. Decision Influence Diagrams and Their Uses.- 4. A Pyramid of Decision Approaches.- II Theory and Foundations.- 5. Direct Decision Making.- 6. On Some Conditions for the Ellsberg Phenomenon.- 7. On the Foundations of Robust Decision Making.- 8. Rational Comparisons and Numerical Representations.- 9. Robust Decision Making as a Decision Making Aid under Uncertainty.- 10. Topological Characterizations of Posets.- 11. Inference in Multidimensional Gaussian Processes.- III Applications.- 12. An Explanation and Characterization for the Buying of Lotteries.- 13. Stochastic Dominance for Elliptical Distributions: Applications in Bayesian Inference.- 14. The Nearly Perfect Auctioneer: Cryptographic Protocols for Auctions and Bidding.- 15. Optimal Hypothesis Testing with a Vague Prior.- 16. Multiple Criteria Decision Making: Some Connections with Economic Analysis.- 17. Experiments in Robust Decision Making.- 18. Heuristic Solving of NP-Complete Job-Shop Scheduling Problems by Multicriteria Optimisation.- 19. Multiple Choices in an Oligopolistic Market: Explicative Models and Neural Networks.- 20. Expert-Based Value Functions for Soil Pollutants: Assessment and Aggregation of Responses.