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Prospect Versus Utility

Author

Listed:
  • Imran S. Currim

    (Graduate School of Business Administration, New York University, New York, New York 10006)

  • Rakesh K. Sarin

    (Fuqua School of Business, Duke University, Durham, North Carolina 27706)

Abstract

We show how to calibrate a prospect model of decision making under risk for an individual. The prospect model is empirically compared to a utility model on two criteria, verification of the postulates of each model, and predictive accuracy. The empirical comparison is performed via three experiments. In Experiment 1, predictive accuracy of the models is compared in nonparadoxical situations, those which favor neither model. In contrast the predictions in Experiment 2 are for paradoxical choices, those which favor the prospect model. In Experiment 1, the prospect model is compared to a model comprising a utility function which permits separate risk attitudes for gain and losses, and hence is more flexible than a utility model as traditionally assessed. In contrast the utility model in Experiment 3 is assessed as is traditionally done assuming constant risk attitude across gains and losses. Several calibration procedures are contrasted across experiments. Our results show a high degree of consistency with the postulates of both models. On predictive accuracy the prospect model outperforms the utility model for paradoxical choices. However, for nonparadoxical situations there is little difference in the predictive ability of both models.

Suggested Citation

  • Imran S. Currim & Rakesh K. Sarin, 1989. "Prospect Versus Utility," Management Science, INFORMS, vol. 35(1), pages 22-41, January.
  • Handle: RePEc:inm:ormnsc:v:35:y:1989:i:1:p:22-41
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    File URL: http://dx.doi.org/10.1287/mnsc.35.1.22
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    Citations

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    Cited by:

    1. Schunk, Daniel & Winter, Joachim, 2009. "The relationship between risk attitudes and heuristics in search tasks: A laboratory experiment," Journal of Economic Behavior & Organization, Elsevier, vol. 71(2), pages 347-360, August.
    2. Glaser, Markus, 2001. "Behavioral Financial Engineering: eine Fallstudie zum Rationalen Entscheiden," Sonderforschungsbereich 504 Publications 01-06, Sonderforschungsbereich 504, Universität Mannheim;Sonderforschungsbereich 504, University of Mannheim.
    3. Lévesque, Moren & Schade, Christian, 2002. "Intuitive optimizing for time allocation decisions in newly formed ventures," SFB 373 Discussion Papers 2002,24, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    4. Zank H., 1998. "Cumulative Prospect Theory for Parametric and Multiattribute Utilities," Research Memorandum 008, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
    5. Chia-Lin Chang & Michael McAleer & Wing-Keung Wong, 2018. "Big Data, Computational Science, Economics, Finance, Marketing, Management, and Psychology: Connections," Journal of Risk and Financial Management, MDPI, Open Access Journal, vol. 11(1), pages 1-29, March.
    6. Lahdelma, Risto & Salminen, Pekka, 2009. "Prospect theory and stochastic multicriteria acceptability analysis (SMAA)," Omega, Elsevier, vol. 37(5), pages 961-971, October.
    7. Jakusch, Sven Thorsten & Meyer, Steffen & Hackethal, Andreas, 2016. "Taming models of prospect theory in the Wild? Estimation of Vlcek and Hens (2011)," SAFE Working Paper Series 146, Research Center SAFE - Sustainable Architecture for Finance in Europe, Goethe University Frankfurt.
    8. Diecidue, Enrico & Schmidt, Ulrich & Zank, Horst, 2009. "Parametric weighting functions," Journal of Economic Theory, Elsevier, vol. 144(3), pages 1102-1118, May.
    9. Manrai, Ajay K., 1995. "Mathematical models of brand choice behavior," European Journal of Operational Research, Elsevier, vol. 82(1), pages 1-17, April.
    10. Jakusch, Sven Thorsten, 2016. "On the applicability of maximum likelihood methods: From experimental to financial data," SAFE Working Paper Series 148, Research Center SAFE - Sustainable Architecture for Finance in Europe, Goethe University Frankfurt.
    11. Ulrich Schmidt & Horst Zank, 2012. "A genuine foundation for prospect theory," Journal of Risk and Uncertainty, Springer, vol. 45(2), pages 97-113, October.
    12. Schunk, Daniel, 2005. "Search behaviour with reference point preferences : theory and experimental evidence," Papers 05-12, Sonderforschungsbreich 504.
    13. Chia-Lin Chang & Michael McAleer & Wing-Keung Wong, 2016. "Management science, economics and finance: A connection," Documentos de Trabajo del ICAE 2016-07, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    14. Chia-Lin Chang & Michael McAleer & Wing-Keung Wong, 2018. "Decision Sciences, Economics, Finance, Business, Computing, and Big Data: Connections," Tinbergen Institute Discussion Papers 18-024/III, Tinbergen Institute.
    15. Peter Brooks & Horst Zank, 2005. "Loss Averse Behavior," Journal of Risk and Uncertainty, Springer, vol. 31(3), pages 301-325, December.
    16. Schunk, Daniel, 2009. "Behavioral heterogeneity in dynamic search situations: Theory and experimental evidence," Journal of Economic Dynamics and Control, Elsevier, vol. 33(9), pages 1719-1738, September.
    17. Glaser, Markus, 2001. "Behavioral Financial Engineering : eine Fallstudie zum Rationalen Entscheiden," Papers 01-06, Sonderforschungsbreich 504.
    18. Nielsen, Thomas D. & Jaffray, Jean-Yves, 2006. "Dynamic decision making without expected utility: An operational approach," European Journal of Operational Research, Elsevier, vol. 169(1), pages 226-246, February.
    19. Narges Hajimoladarvish, 2017. "Very Low Probabilities in the Loss Domain," The Geneva Papers on Risk and Insurance Theory, Springer;International Association for the Study of Insurance Economics (The Geneva Association), vol. 42(1), pages 41-58, March.
    20. Martina Nardon & Paolo Pianca, 2014. "European option pricing with constant relative sensitivity probability weighting function," Working Papers 2014:25, Department of Economics, University of Venice "Ca' Foscari".
    21. Coelho, Luís Alberto Godinho & Pires, Cesaltina Maria Pacheco & Dionísio, Andreia Teixeira & Serrão, Amílcar Joaquim da Conceição, 2012. "The impact of CAP policy in farmer's behavior – A modeling approach using the Cumulative Prospect Theory," Journal of Policy Modeling, Elsevier, vol. 34(1), pages 81-98.
    22. Loehman, Edna, 1998. "Testing risk aversion and nonexpected utility theories," Journal of Economic Behavior & Organization, Elsevier, vol. 33(2), pages 285-302, January.
    23. McAlvanah, Patrick, 2010. "Subadditivity, patience, and utility: The effects of dividing time intervals," Journal of Economic Behavior & Organization, Elsevier, vol. 76(2), pages 325-337, November.

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