AI Bibliography
AI Bibliography |
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Tversky, A., & Kahneman, D. (1992). Advances in prospect theory: Cumulative representation of uncertainty. Journal of Risk and uncertainty, 5(4), 297–323. |
| Resource type: Journal Article BibTeX citation key: Tversky1992 View all bibliographic details  |
Categories: Cognitive Science, Complexity Science, Data Sciences, Decision Theory, General, Mathematics, Military Science Subcategories: Cross-domain deterrence, Decision making, Forecasting, Game theory, Human decisionmaking, Military research, Strategy, Systems theory Creators: Kahneman, Tversky Publisher: Collection: Journal of Risk and uncertainty |
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| Abstract |
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We develop a new version of prospect theory that employs cumulative rather than separable decision weights and extends the theory in several respects. This version, called cumulative prospect theory, applies to uncertain as well as to risky prospects with any number of outcomes, and it allows different weighting functions for gains and for losses. Two principles, diminishing sensitivity and loss aversion, are invoked to explain the characteristic curvature of the value function and the weighting functions. A review of the experimental evidence and the results of a new experiment confirm a distinctive fourfold pattern of risk attitudes: risk aversion for gains and risk seeking for losses of high probability; risk seeking for gains and risk aversion for losses of low probability.
This article has benefited from discussions with Colin Camerer, Chew Soo-Hong, David Freedman, and David H. Krantz. We are especially grateful to Peter P. Wakker for his invaluable input and contribution to the axiomatic analysis. We are indebted to Richard Gonzalez and Amy Hayes for running the experiment and analyzing the data. This work was supported by Grants 89-0064 and 88-0206 from the Air Force Office of Scientific Research, by Grant SES-9109535 from the National Science Foundation, and by the Sloan Foundation. |