This paper presents a critique of expected utility theory as a descriptive model of decision making under risk, and develops an alternative model, called prospect theory. Choices among risky prospects exhibit several pervasive effects that are inconsistent with the basic tenets of utility theory. In particular, people underweight outcomes that are merely probable in comparison with outcomes that are obtained with certainty. This tendency, called the certainty effect, contributes to risk aversion in choices involving sure gains and to risk seeking in choices involving sure losses. In addition, people generally discard components that are shared by all prospects under consideration. This tendency, called the isolation effect, leads to inconsistent preferences when the same choice is presented in different forms. An alternative theory of choice is developed, in which value is assigned to gains and losses rather than to final assets and in which probabilities are replaced by decision weights. The value function is normally concave for gains, commonly convex for losses, and is generally steeper for losses than for gains. Decision weights are generally lower than the corresponding probabilities, except in the range of low prob- abilities. Overweighting of low probabilities may contribute to the attractiveness of both insurance and gambling. EXPECTED UTILITY THEORY has dominated the analysis of decision making under risk. It has been generally accepted as a normative model of rational choice (24), and widely applied as a descriptive model of economic behavior, e.g. (15, 4). Thus, it is assumed that all reasonable people would wish to obey the axioms of the theory (47, 36), and that most people actually do, most of the time. The present paper describes several classes of choice problems in which preferences systematically violate the axioms of expected utility theory. In the light of these observations we argue that utility theory, as it is commonly interpreted and applied, is not an adequate descriptive model and we propose an alternative account of choice under risk. 2. CRITIQUE

/pdf/prospect-theory-an-analysis-of-decision-under-risk-4z6oysdahj.pdf

Prospect theory: an analysis of decision under risk

From the Publisher:
Probabilistic Reasoning in Intelligent Systems is a complete andaccessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty. The author provides a coherent explication of probability as a language for reasoning with partial belief and offers a unifying perspective on other AI approaches to uncertainty, such as the Dempster-Shafer formalism, truth maintenance systems, and nonmonotonic logic. The author distinguishes syntactic and semantic approaches to uncertaintyand offers techniques, based on belief networks, that provide a mechanism for making semantics-based systems operational. Specifically, network-propagation techniques serve as a mechanism for combining the theoretical coherence of probability theory with modern demands of reasoning-systems technology: modular declarative inputs, conceptually meaningful inferences, and parallel distributed computation. Application areas include diagnosis, forecasting, image interpretation, multi-sensor fusion, decision support systems, plan recognition, planning, speech recognitionin short, almost every task requiring that conclusions be drawn from uncertain clues and incomplete information.
Probabilistic Reasoning in Intelligent Systems will be of special interest to scholars and researchers in AI, decision theory, statistics, logic, philosophy, cognitive psychology, and the management sciences. Professionals in the areas of knowledge-based systems, operations research, engineering, and statistics will find theoretical and computational tools of immediate practical use. The book can also be used as an excellent text for graduate-level courses in AI, operations research, or applied probability.

Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference

List of Figures. List of Tables. Preface. Foreword. 1. Basic Concepts. 2. Evolutionary Algorithm MOP Approaches. 3. MOEA Test Suites. 4. MOEA Testing and Analysis. 5. MOEA Theory and Issues. 3. MOEA Theoretical Issues. 6. Applications. 7. MOEA Parallelization. 8. Multi-Criteria Decision Making. 9. Special Topics. 10. Epilog. Appendix A: MOEA Classification and Technique Analysis. Appendix B: MOPs in the Literature. Appendix C: Ptrue & PFtrue for Selected Numeric MOPs. Appendix D: Ptrue & PFtrue for Side-Constrained MOPs. Appendix E: MOEA Software Availability. Appendix F: MOEA-Related Information. Index. References.

/pdf/evolutionary-algorithms-for-solving-multi-objective-problems-2txxk4mqci.pdf

Evolutionary algorithms for solving multi-objective problems

Since 1971 conjoint analysis has been applied to a wide variety of problems in consumer research. This paper discusses various issues involved in implementing conjoint analysis and describes some new technical developments and application areas for the methodology.

/pdf/conjoint-analysis-in-consumer-research-issues-and-outlook-3q5v6wkzks.pdf

Conjoint Analysis in Consumer Research: Issues and Outlook

By reducing the costs of coordination, information technology will lead to an overall shift toward proportionately more use of markets—rather than hierarchies—to coordinate economic activity.

/pdf/electronic-markets-and-electronic-hierarchies-c5wfbpjs9o.pdf

Electronic markets and electronic hierarchies

Many of the complex problems faced by decision makers involve multiple conflicting objectives. This book describes how a confused decision maker, who wishes to make a reasonable and responsible choice among alternatives, can systematically probe his true feelings in order to make those critically important, vexing trade-offs between incommensurable objectives. The theory is illustrated by many real concrete examples taken from a host of disciplinary settings. The standard approach in decision theory or decision analysis specifies a simplified single objective like monetary return to maximise. By generalising from the single objective case to the multiple objective case, this book considerably widens the range of applicability of decision analysis.

/pdf/decisions-with-multiple-objectives-preferences-and-value-18632c08oi.pdf

Decisions with Multiple Objectives: Preferences and Value Trade-Offs

Quantitative measures that can be useful in guiding the process of structuring and analysing a decision tree model are identified. These are classified according to whether they relate to the structure of the primary decision problem or the structure of the secondary or information gathering decision problem. An important question that arises during the process of developing a structural model of a decision situation is whether to encode uncertainty on an important variable directly or to model the situation further. These represent two different but complementary approaches to quantifying judgment whose use must be balanced in any decision problem. One is related to the value of modelling and one related to the value of information. The measures associated with the value of modelling are specifically useful in developing a model of the decision situation. The measures associated with the value of information are specifically useful in the analysis of the decision model. Four measures related to ...

On measures for decision model structuring

One important activity in analyzing a choice-making problem is structuring prior information into a model that reflects the reality of the decisionmaker's decision situation. The process of identifying and organizing the factors relevant to a decision situation into a framework that facilitates determination of the optimal course of action is called structuring. The structure of a decision model is limited by the extent of the decisionmaker's abilities to anticipate changes in the decision situation resulting from new information. Perspectives on structuring for decisionmaking follow directly from the systems engineering viewpoint and the steps of systems engineering. Choice making or decisionmaking is a vital part of the systems engineering process and depends critically upon the other steps for success. The primary intent and purpose is to continue the development of structuring aids for decision analysis and the integration of decision analysis with other steps in the systems engineering process. A case study of an investment decision problem in the beef production system is considered in order to illustrate the role of structuring in the decisiounaking steps of systems engineering.

David W. Rajala

Papers

Decisions with Multiple Objectives: Preferences and Value Trade-Offs

On measures for decision model structuring

On Information Structuring in Choice Making: A Case Study of Systems Engineering Decisionmaking in Beef Cattle Production