Risk preferences for gains and losses in multiple objective decision making
Management Science
Generic utility theory: measurement foundations and applications in multiattribute utility theory
Journal of Mathematical Psychology
Unbounded utility for Savage's “Foundations of statistics,” and other models
Mathematics of Operations Research
A Belief-Based Account of Decision Under Uncertainty
Management Science
Nonlinear Decision Weights in Choice Under Uncertainty
Management Science
Cumulative Prospect Theory for Parametric and Multiattribute Utilities
Mathematics of Operations Research
Loss aversion and scale compatibility in two-attribute trade-offs
Journal of Mathematical Psychology
A characterization of quality-adjusted life-years under cumulative prospect theory
Mathematics of Operations Research
Reference dependence in cumulative prospect theory
Journal of Mathematical Psychology
Parameter-Free Elicitation of Utility and Probability Weighting Functions
Management Science
Preference Foundations for Nonexpected Utility: A Generalized and Simplified Technique
Mathematics of Operations Research
Anniversary Article: Decision Analysis in Management Science
Management Science
Loss Aversion Under Prospect Theory: A Parameter-Free Measurement
Management Science
Predicting Utility Under Satiation and Habit Formation
Management Science
Multiple attribute decision making considering aspiration-levels: A method based on prospect theory
Computers and Industrial Engineering
Risk decision analysis in emergency response: A method based on cumulative prospect theory
Computers and Operations Research
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Prospect theory is currently the main descriptive theory of decision under uncertainty. It generalizes expected utility by introducing nonlinear decision weighting and loss aversion. A difficulty in the study of multiattribute utility under prospect theory is to determine when an attribute yields a gain or a loss. One possibility, adopted in the theoretical literature on multiattribute utility under prospect theory, is to assume that a decision maker determines whether the complete outcome is a gain or a loss. In this holistic evaluation, decision weighting and loss aversion are general and attribute-independent. Another possibility, more common in the empirical literature, is to assume that a decision maker has a reference point for each attribute. We give preference foundations for this attribute-specific evaluation where decision weighting and loss aversion are depending on the attributes.