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Hierarchical maximum likelihood parameter estimation for cumulative prospect theory: Improving the reliability of individual risk parameter estimates

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  • Ryan O. Murphy
  • Robert H.W. ten Brincke

Abstract

Individual risk preferences can be identified by using decision models with tuned parameters that maximally fit a set of risky choices made by a decision maker. A goal of this model fitting procedure is to isolate parameters that correspond to stable risk preferences. These preferences can be modeled as an individual difference, indicating a particular decision maker's tastes and willingness to tolerate risk. Using hierarchical statistical methods we show significant improvements in the reliability of individual risk preference parameters over other common estimation methods. This hierarchal procedure uses population level information (in addition to an individual's choices) to break ties (or near-ties) in the fit quality for sets of possible risk preference parameters. By breaking these statistical ``ties'' in a sensible way, researchers can avoid overfitting choice data and thus better measure individual differences in people's risk preferences.

Suggested Citation

  • Ryan O. Murphy & Robert H.W. ten Brincke, "undated". "Hierarchical maximum likelihood parameter estimation for cumulative prospect theory: Improving the reliability of individual risk parameter estimates," Working Papers ETH-RC-14-005, ETH Zurich, Chair of Systems Design.
    • Handle: RePEc:stz:wpaper:eth-rc-14-005
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    Cited by:

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    2. Liu, Hui-hui & Song, Yao-yao & Liu, Xiao-xiao & Yang, Guo-liang, 2020. "Aggregating the DEA prospect cross-efficiency with an application to state key laboratories in China," Socio-Economic Planning Sciences, Elsevier, vol. 71(C).
    3. Emmanuel Kemel & Antoine Nebout & Bruno Ventelou, 2021. "To test or not to test? Risk attitudes and prescribing by French GPs," Working Papers hal-03330153, HAL.
    4. Xiaoxue Sherry Gao & Glenn W. Harrison & Rusty Tchernis, 2020. "Behavioral Welfare Economics and Risk Preferences: A Bayesian Approach," NBER Working Papers 27685, National Bureau of Economic Research, Inc.
    5. Victor H. Gonzalez-Jimenez, 2019. "Contracting Probability Distortions," Vienna Economics Papers vie1901, University of Vienna, Department of Economics.
    6. Cédric Gutierrez & Emmanuel Kemel, 2021. "Measuring natural source dependence," Working Papers hal-03330409, HAL.
    7. Shi, Hai-Liu & Chen, Sheng-Qun & Chen, Lei & Wang, Ying-Ming, 2021. "A neutral cross-efficiency evaluation method based on interval reference points in consideration of bounded rational behavior," European Journal of Operational Research, Elsevier, vol. 290(3), pages 1098-1110.
    8. Víctor González-Jiménez, 2021. "Incentive contracts when agents distort probabilities," Vienna Economics Papers vie2101, University of Vienna, Department of Economics.
    9. Victor H. Gonzalez-Jimenez, 2019. "Contracting Probability Distortions," Vienna Economics Papers 1901, University of Vienna, Department of Economics.
    10. Christodoulakis, George, 2020. "Estimating the term structure of commodity market preferences," European Journal of Operational Research, Elsevier, vol. 282(3), pages 1146-1163.
    11. Maroussia Favre & Amrei Wittwer & Hans Rudolf Heinimann & Vyacheslav I Yukalov & Didier Sornette, 2016. "Quantum Decision Theory in Simple Risky Choices," PLOS ONE, Public Library of Science, vol. 11(12), pages 1-29, December.
    12. Víctor González-Jiménez, 2021. "Incentive contracts when agents distort probabilities," Vienna Economics Papers 2101, University of Vienna, Department of Economics.
    13. Louis Eeckhoudt & Anna Maria Fiori & Emanuela Rosazza Gianin, 2018. "Risk Aversion, Loss Aversion, and the Demand for Insurance," Risks, MDPI, vol. 6(2), pages 1-19, May.
    14. Qian Wu & Monique Vanerum & Anouk Agten & Andrés Christiansen & Frank Vandenabeele & Jean-Michel Rigo & Rianne Janssen, 2021. "Certainty-Based Marking on Multiple-Choice Items: Psychometrics Meets Decision Theory," Psychometrika, Springer;The Psychometric Society, vol. 86(2), pages 518-543, June.
    15. Ferro, Giuseppe M. & Kovalenko, Tatyana & Sornette, Didier, 2021. "Quantum decision theory augments rank-dependent expected utility and Cumulative Prospect Theory," Journal of Economic Psychology, Elsevier, vol. 86(C).

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    Keywords

    Prospect theory; Risk preference; Decision making under risk; Hierarchical parameter estimation; Maximum likelihood;
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