The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Prospect theory: An analysis of decision under risk
Abstract
Introduction Expected utility theory has dominated the analysis of decision making under risk. It has been generally accepted as a normative model of rational choice (Keeney and Raiffa, 1976), and widely applied as a descriptive model of economic behavior (e.g., Friedman and Savage, 1948, and Arrow, 1971). Thus, it is assumed that all reasonable people would wish to obey the axioms of the theory (von Neumann & Morgenstern, 1944, and Savage, 1954), 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. Critique Decision making under risk can be viewed as a choice between prospects or gambles. A prospect ( x 1 , p 1 ; …; x n , p n ) is a contract that yields outcome x i with probability p i , where p 1 + p 2 + … + p n = 1. To simplify notation, we omit null outcomes and use ( x, p ) to denote the prospect ( x, p ; 0, 1 – p ) that yields x with probability p and 0 with probability 1 – p . The (riskless) prospect that yields x with certainty is denoted by ( x ). The present discussion is restricted to prospects with so-called objective or standard probabilities.
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Citations
Cites
Judgment under Uncertainty: Heuristics and Biases
Tversky, A., & Kahneman, D. (1975) · Utility, Probability, and Human Decision Making · doi:10.1007/978-94-010-1834-0_8 · Cited by 22,650
Self-citation“Risk, Ambiguity, and the Savage Axioms.”
Ellsberg, D. (1961) · The Quarterly Journal of Economics · doi:10.2307/1884324 · Cited by 7,891
Cited by
Items 401–437 of 437
Valuation of uncertain and delayed rewards in primate prefrontal cortex
Kim, S., Hwang, J., Seo, H., & Lee, D. (2009) · Neural Networks · doi:10.1016/j.neunet.2009.03.010 · Cited by 37
Choi, P., & Coulter, K. S. (2012) · Journal of Retailing · doi:10.1016/j.jretai.2012.04.001 · Cited by 36
Association of socioeconomic status with medical assistance in dying: a case-control analysis
Redelmeier, D. A., Ng, K., Thiruchelvam, D., & Shafir, E. (2021) · BMJ Open · doi:10.1136/bmjopen-2020-043547 · Cited by 37
Conditioned associations and economic decision biases.
Guitart-Masip, M., Talmi, D., & Dolan, R. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.06.021 · Cited by 36
Satisfaction measures with monetary and non-monetary components: hotel’s overall scores
Nicolau, J. L., Mellinas, J. P., & Martín-Fuentes, E. (2020) · International Journal of Hospitality Management · doi:10.1016/j.ijhm.2020.102497 · Cited by 33
Do you remember? Rater memory systems and leadership measurement
Hansbrough, T. K., Lord, R. G., Schyns, B., Foti, R. J., Liden, R. C., & Acton, B. P. (2021) · The Leadership Quarterly · doi:10.1016/j.leaqua.2020.101455 · Cited by 33
Increased Loss Aversion in Unmedicated Patients with Obsessive–Compulsive Disorder
Sip, K. E., Gonzalez, R., Taylor, S. F., & Stern, E. R. (2018) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2017.00309 · Cited by 33
A Negativity Bias in Reframing Shapes Political Preferences Even in Partisan Contexts
Boydstun, A. E., Ledgerwood, A., & Sparks, J. (2019) · Social Psychological and Personality Science · doi:10.1177/1948550617733520 · Cited by 31
Krieger, M., & Felder, S. (2013) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph10062560 · Cited by 31
Pushkarskaya, H., Sobowale, K., Henick, D., Tolin, D. F., Anticevic, A., Pearlson, G. D., Levy, I., Harpaz-Rotem, I., & Pittenger, C. (2019) · Journal of Psychiatric Research · doi:10.1016/j.jpsychires.2018.11.029 · Cited by 30
Abnormal stress responsiveness and suicidal behavior: a risk phenotype
Steinberg, L. J., & Mann, J. J. (2020) · Biomarkers in Neuropsychiatry · doi:10.1016/j.bionps.2020.100011 · Cited by 29
Return Migration to India: Decision‐Making among Academic Engineers and Scientists
Sabharwal, M., & Varma, R. (2016) · International Migration · doi:10.1111/imig.12265 · Cited by 27
Linnerud, K., Toney, P., Simonsen, M., & Holden, E. (2019) · Energy Policy · doi:10.1016/j.enpol.2019.04.039 · Cited by 27
Jang, S. M., & Oh, Y. W. (2016) · New Media & Society · doi:10.1177/1461444815583491 · Cited by 27
Scagnolari, S., Walker, J., & Maggi, R. (2015) · Accident Analysis & Prevention · doi:10.1016/j.aap.2015.07.002 · Cited by 27
Tubadji, A., Nijkamp, P., & Angelis, V. (2016) · Cambridge Journal of Regions, Economy and Society · doi:10.1093/cjres/rsv035 · Cited by 26
Kautish, P., Khare, A., & Sharma, R. (2022) · Journal of Marketing Analytics · doi:10.1057/s41270-021-00134-7 · Cited by 25
Variables influencing the neural correlates of perceived risk of physical harm
Coaster, M., Rogers, B. P., Jones, O. D., Viscusi, W. K., Merkle, K. L., Zald, D. H., & Gore, J. C. (2011) · Cognitive, Affective, & Behavioral Neuroscience · doi:10.3758/s13415-011-0047-9 · Cited by 24
“A substantial literature has documented the difficulty people have in making risky decisions in a consistent manner and in a way that conforms with rational norms (Kahneman & Tversky, 1979).”
BackgroundAssessing citizens’ non-adoption of public e-services in Germany
Distel, B. (2020) · Information Polity · doi:10.3233/ip-190214 · Cited by 23
Exploring differences in dogs’ and wolves’ preference for risk in a foraging task
Marshall-Pescini, S., Besserdich, I., Kratz, C., & Range, F. (2016) · Frontiers in Psychology · doi:10.3389/fpsyg.2016.01241 · Cited by 22
“Change the Analyst and Not the System: A Different Approach to Intelligence Reform.”
Bar-Joseph, U., & McDermott, R. (2008) · Foreign Policy Analysis · doi:10.1111/j.1743-8594.2007.00061.x · Cited by 22
Impaired generalization of reward but not loss in obsessive-compulsive disorder
Rouhani, N., Wimmer, G. E., Schneier, F. R., Fyer, A. J., Shohamy, D., & Simpson, H. B. (2019) · Depression and Anxiety · doi:10.1002/da.22857 · Cited by 21
Harnessing aspect-based sentiment analysis: How are tweets associated with forecast accuracy?
The Australian National University, A., Ho, S. Y., Choi, K. W. (., The Australian National University, A., Yang, F. (., & The Australian National University, A. (2019) · Journal of the Association for Information Systems · doi:10.17705/1jais.00564 · Cited by 21
Warring from the virtual to the real: Assessing the public’s threshold for war over cyber security
Kreps, S., & Das, D. (2017) · Research & Politics · doi:10.1177/2053168017715930 · Cited by 18
Are NFL Coaches Risk and Loss Averse? Evidence from Their Use of Kickoff Strategies
Urschel, J. D., & Zhuang, J. (2011) · Journal of Quantitative Analysis in Sports · doi:10.2202/1559-0410.1311 · Cited by 17
Freiburg, M., & Grichnik, D. (2013) · Journal of Behavioral Finance · doi:10.1080/15427560.2013.791295 · Cited by 16
Motivating change with mobile: seven guidelines
Morris, M. E. (2012) · Interactions · doi:10.1145/2168931.2168939 · Cited by 16
Wei, J., Qi, S., Wang, W., Jiang, L., Gao, H., Zhao, F., Al-Bukhaiti, K., & Wan, A. (2025) · Contemporary Mathematics · doi:10.37256/cm.6220256459 · Cited by 15
“According to prospect theory, individuals interpret outcomes in terms of gains and losses, with these perceptions shaped by how the options are framed and the decision-maker’s expectations [21].”
InfluentialAre Sports Bettors Biased toward Longshots, Favorites, or Both? A Literature Review
Newall, P. W. S., & Cortis, D. (2021) · Risks · doi:10.3390/risks9010022 · Cited by 14
Principles of moral accounting: How our intuitive moral sense balances rights and wrongs
Johnson, S. G. B., & Ahn, J. (2021) · Cognition · doi:10.1016/j.cognition.2020.104467 · Cited by 14
Tudoran, A. A., & Olsen, S. O. (2017) · Journal of Consumer Behaviour · doi:10.1002/cb.1633 · Cited by 13
“Mittal et al. (1998) and Streukens and de Ruyter (2004) adopt a prospect theory approach (Kahneman and Tversky, 1979) to theoretically explain the relationship between CS and repurchase intention.”
BackgroundEffects of perceived benefits about when and how to use a learning strategy
Yamaguchi, T. (2017) · The Japanese journal of psychology · doi:10.4992/jjpsy.88.16007 · Cited by 6
Choice Set Misspecification in Reward Inference
Freedman, R., Shah, R., & Dragan, A. (2021) · arXiv · doi:10.48550/arxiv.2101.07691 · Cited by 6
Simulating the deep decarbonisation of residential heating for limiting global warming to 1.5 °C
Knobloch, F., Pollitt, H., Chewpreecha, U., Daioglou, V., & Mercure, J.-F. (2019) · Energy Efficiency · doi:10.1007/s12053-018-9710-0 · Cited by 6
State-dependent value representation: evidence from the striatum
Burke, C. J., Dreher, J.-C., Seymour, B., & Tobler, P. N. (2014) · Frontiers in Neuroscience · doi:10.3389/fnins.2014.00193 · Cited by 4
Revealing User Confidence in Machine Learning-Based Decision Making
Zhou, J., Yu, K., & Chen, F. (2018) · Human–Computer Interaction Series · doi:10.1007/978-3-319-90403-0_11 · Cited by 1
Mathematical Definition, Mapping, and Detection of (Anti)Fragility
Taleb, N. N., & Douady, R. (2012) · arXiv · doi:10.48550/arxiv.1208.1189
“…is a quite unhappy result—the utility curve cannot be possibly monotone concave, but rather, like everything in nature necessarily bounded on both sides, the left and the right, convex–concave and, as Kahneman and Tversky (1979) have debunked, both path dependent and mixed in its nonlinearity.”
Background
Metadata
- Authors
- Kahneman, D., & Tversky, A.
- Venue
- Decision, Probability and Utility
- Publisher
- Cambridge University Press
- Published
- 1988-03-25
- Pages
- 183-214
- OpenAlex
- W2133469585
- Cited by
- 32,474
- References
- 2
- FWCI
- 76.53
- Type
- book chapter
- Language
- en
- Open access
- closed
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