Advances in prospect theory: Cumulative representation of uncertainty
 Amos Tversky,
 Daniel Kahneman
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Abstract
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 SooHong, 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 890064 and 880206 from the Air Force Office of Scientific Research, by Grant SES9109535 from the National Science Foundation, and by the Sloan Foundation.
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 Title
 Advances in prospect theory: Cumulative representation of uncertainty
 Journal

Journal of Risk and Uncertainty
Volume 5, Issue 4 , pp 297323
 Cover Date
 19921001
 DOI
 10.1007/BF00122574
 Print ISSN
 08955646
 Online ISSN
 15730476
 Publisher
 Kluwer Academic Publishers
 Additional Links
 Topics
 Keywords

 cumulative prospect theory
 Industry Sectors
 Authors

 Amos Tversky ^{(1)}
 Daniel Kahneman ^{(2)}
 Author Affiliations

 1. Department of Psychology, Stanford University, 943052130, Stanford, CA
 2. Department of Psychology, University of California at Berkeley, 94720, Berkeley, CA