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

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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

This paper discusses the discounted utility (DU) model: its historical development, underlying assumptions, and "anomalies" - the empirical regularities that are inconsistent with its theoretical predictions. We then summarize the alternate theoretical formulations that have been advanced to address these anomalies. We also review three decades of empirical research on intertemporal choice, and discuss reasons for the spectacular variation in implicit discount rates across studies. Throughout the paper, we stress the importance of distinguishing time preference, per se, from many other considerations that also influence intertemporal choices.

/pdf/time-discounting-and-time-preference-a-critical-review-47don96ek5.pdf

Time Discounting and Time Preference: A Critical Review

Objective. To standardize outcome measures in systemic lupus erythematosus (SLE). Three indices were identified which could adequately describe outcome (disease activity, damage from disease, and health status); we describe here the development of the Disease Activity Index.

Methods. Twenty-four variables were identified as important factors in a disease activity index. These were used to generate 574 patient profiles, which were rated on a disease activity scale of 0–10 by 14 rheumatologists. A second rating of 10 of the profiles yielded scores that were not significantly different from the first, indicating that experienced clinicians can reliably make global estimates of disease activity. Multiple regression models were used to estimate the relative importance of the 24 clinical variables in the physicians' global rating of disease activity. These were estimated on a ‘training set’ of 75% of physicians' ratings, and then validated on a ‘testing set,’ consisting of the remaining 25% of physicians' ratings.

Results. The explanatory power of the models in the training set was high (R2 = 0.93). The models' regression coefficients for the organ systems were simplified for easier use in clinical practice. This generated a ‘weighted’ index of 9 organ systems for disease activity in SLE, the SLEDAI, as follows: 8 for central nervous system and vascular, 4 for renal and musculoskeletal, 2 for serosal, dermal, immunologic, and 1 for constitutional and hematologic. The maximum theoretical score is 105, but in practice, few patients have scores greater than 45. The SLEDAI predicted well the physicians' ratings in the testing set (Pearson's correlation coefficients = 0.64–0.79).

Conclusion. The SLEDAI is a validated model of experienced clinicians' global assessments of disease activity in lupus. It represents the consensus of a group of experts in the field of lupus research.

https://onlinelibrary.wiley.com/doi/pdf/10.1002/art.1780350606

Derivation of the SLEDAI. A disease activity index for lupus patients. The Committee on Prognosis Studies in SLE.

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.

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Decisions with Multiple Objectives: Preferences and Value Trade-Offs

Decisions with Multiple Objectives

PART 1: Concepts 1. Thinking about Values 1.1 Value-Focused Thinking 1.2 Creating Alternatives 1.3 Identifying Decision Opportunities 1.4 Thinking about Values 1.5 The Uses of Value-Focused Thinking 2. The Framework of Value-Focused Thinking 2.1 Framing a Decision Situation 2.2 Fundamental Objectives 2.3 The Decision Context 2.4 Guiding Strategic Thinking and Action 2.5 The Framework 2.6 Comparing Alternative-Focused and Value-Focused Thinking 2.7 Ethics and Value Neutrality Part 2: Foundations 3. Identifying and Structuring Objectives 3.1 Identifying Objectives 3.2 Identifying Fundamental Objectives 3.3 Structures of Objectives 3.4 How to Structure Objectives 3.5 Desirable Properties of Fundamental Objectives 3.6 Relating Objectives Hierarchies and Objectives Networks 3.7 Incomplete Objectives Hierarchies and Networks 3.8 Objectives Hierarchies for Groups 4. Measuring the Achievement of Objectives 4.1 The Concept of an Attribute 4.2 The Types of Attributes 4.3 Developing Constructed Attributes 4.4 Use of Proxy Attributes 4.5 Desirable Properties of Attributes 4.6 The Decision of Selecting Attributes 4.7 Connecting Decision Situations with Attributes 5. Quantifying Objectives with a Value Model 5.1 Building a Value Model 5.2 Multiple-Objective Value Models 5.3 Single-Objective Value Models 5.4 Prioritizing Objectives 5.5 The Art of Assessing Value Models 5.6 Issues to Consider in Value Assessments Part 3: Uses 6. Uncovering Hidden Objectives 6.1 Insights from Attributes 6.2 Insights from Violations of Independence Assumptions 6.3 Insights from Value Tradeoffs 6.4 Insights from Single-Attribute Objective Functions 6.5 Insights from Multiple Value Assessments 7. Creating Alternatives for a Single Decisionmaker 7.1 Counteracting Cognitive Biases 7.2 Use of Objectives 7.3 Use of Strategic Objectives 7.4 Focus on High-Value Alternatives 7.5 Use of Evaluated Alternatives 7.6 Generic Alternatives 7.7 Coordinated Alternatives 7.8 Process Alternatives 7.9 Removing Constraints 7.10 Better Utilization of Resources 7.11 Screening to Identify Good Alternatives 7.12 Alternatives for a Series of Similar Decisions 8. Creating Alternatives for Multiple Decisionmakers 8.1 Pleasing Other Stakeholders 8.2 Stakeholder Influence on Your Consequences 8.3 Clarifying Stakeholder Values for Group Decisions 8.4 Creating Alternatives for Negotiations 9. Identifying Decision Opportunities 9.1 Use of Strategic Objectives 9.2 Use of Resources Available 9.3 A Broader Decision Context 9.4 Monitoring Achievement 9.5 Establishing a Process 9.6 Negotiating for Your Side and for the Other Side 9.7 Being in the Right Place at the Right Time 9.8 When You Have No Idea about What to Do 10. Insights for the Decisionmaking Process 10.1 Guiding Information Collection 10.2 Evaluating Alternatives 10.3 Interconnecting Decisions 10.4 Improving Communication 10.5 Facilitating Involvement in Multiple-Stakeholder Decisions 10.6 Guiding Strategic Thinking Part 4: Applications 11. Selected Applications 11.1 NASA Leadership in Space 11.2 Transporting Nuclear Waste 11.3 Research on Climate Change 11.4 Air Pollution in Los Angeles 11.5 Design of Integrated Circuit Testers 11.6 Collaborating on a Book 12. Value-Focused Thinking at British Columbia Hydra 12.1 Identification and Structuring of the Strategic Objectives 12.2 First Revision of the Strategic Objectives and the Preliminary Attributes 12.3 Current Version of the Strategic Objectives and Attributes 12.4 The Quantitative Value Assessment 12.5 Insights from the Value Assessment 12.6 Decision Opportunities 13. Value-Focused Thinking for My Decisions 13.1 Strategic Objectives for Life 13.2 Guiding Involvement in Professional Activities 13.3 Decisions about Health and Safety 13.4 Professional Decisions 13.5 Personal Decisions 13.6 Value-Focused Thinking and You References Index of Applications and Examples General Index

Value-Focused Thinking: A Path to Creative Decisionmaking

Decisions with multiple objectives: preferences and value tradeoffs

This paper explores reasons for difficulties in communicating risks among analysts, the laypublic, media, and regulators. Formulating risk communication problems as decisions involving objectives and alternatives helps to identify strategies for overcoming these difficulties. Several strategies are suggested to achieve risk communication objectives like improving public knowledge about risks and risk management, encouraging risk reduction behavior, understanding public values and concerns, and increasing trust and credibility.

Ralph L. Keeney

Papers

Decisions with Multiple Objectives: Preferences and Value Trade-Offs

Decisions with Multiple Objectives

Value-Focused Thinking: A Path to Creative Decisionmaking

Decisions with multiple objectives: preferences and value tradeoffs

Improving Risk Communication