IDEAS home Printed from https://ideas.repec.org/p/stz/wpaper/eth-rc-14-005.html
   My bibliography  Save this paper

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: ftp://web.sg.ethz.ch/RePEc/stz/wpaper/pdf/ETH-RC-14-005.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Helga Fehr-Duda & Thomas Epper, 2012. "Probability and Risk: Foundations and Economic Implications of Probability-Dependent Risk Preferences," Annual Review of Economics, Annual Reviews, vol. 4(1), pages 567-593, July.
    2. Camerer, Colin F & Ho, Teck-Hua, 1994. "Violations of the Betweenness Axiom and Nonlinearity in Probability," Journal of Risk and Uncertainty, Springer, vol. 8(2), pages 167-196, March.
    3. Helga Fehr-Duda & Manuele Gennaro & Renate Schubert, 2006. "Gender, Financial Risk, and Probability Weights," Theory and Decision, Springer, vol. 60(2), pages 283-313, May.
    4. Birnbaum, Michael H. & Chavez, Alfredo, 1997. "Tests of Theories of Decision Making: Violations of Branch Independence and Distribution Independence," Organizational Behavior and Human Decision Processes, Elsevier, vol. 71(2), pages 161-194, August.
    5. Mohammed Abdellaoui, 2000. "Parameter-Free Elicitation of Utility and Probability Weighting Functions," Management Science, INFORMS, vol. 46(11), pages 1497-1512, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Aurélien Baillon & Han Bleichrodt & Vitalie Spinu, 2020. "Searching for the Reference Point," Management Science, INFORMS, vol. 66(1), pages 93-112, January.
    2. Víctor González-Jiménez, 2021. "Incentive contracts when agents distort probabilities," Vienna Economics Papers vie2101, University of Vienna, Department of Economics.
    3. Victor H. Gonzalez-Jimenez, 2019. "Contracting Probability Distortions," Vienna Economics Papers 1901, University of Vienna, Department of Economics.
    4. 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).
    5. 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.
    6. Christodoulakis, George, 2020. "Estimating the term structure of commodity market preferences," European Journal of Operational Research, Elsevier, vol. 282(3), pages 1146-1163.
    7. Aurélien Baillon & Han Bleichrodt & Vitalie Spinu, 2020. "Searching for the Reference Point," Management Science, INFORMS, vol. 66(1), pages 93-112, January.
    8. 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.
    9. 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.
    10. Victor H. Gonzalez-Jimenez, 2019. "Contracting Probability Distortions," Vienna Economics Papers vie1901, University of Vienna, Department of Economics.
    11. Víctor González-Jiménez, 2021. "Incentive contracts when agents distort probabilities," Vienna Economics Papers 2101, University of Vienna, Department of Economics.
    12. Cédric Gutierrez & Emmanuel Kemel, 2021. "Measuring natural source dependence," Working Papers hal-03330409, HAL.
    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. 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.
    16. 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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ryan O. Murphy & Robert H. W. ten Brincke, 2018. "Hierarchical Maximum Likelihood Parameter Estimation for Cumulative Prospect Theory: Improving the Reliability of Individual Risk Parameter Estimates," Management Science, INFORMS, vol. 64(1), pages 308-328, January.
    2. Jan B Engelmann & C Monica Capra & Charles Noussair & Gregory S Berns, 2009. "Expert Financial Advice Neurobiologically “Offloads” Financial Decision-Making under Risk," PLOS ONE, Public Library of Science, vol. 4(3), pages 1-14, March.
    3. 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.
    4. Eyal Baharad & Doron Kliger, 2013. "Market failure in light of non-expected utility," Theory and Decision, Springer, vol. 75(4), pages 599-619, October.
    5. Vincent Laferrière & David Staubli & Christian Thöni, 2023. "Explaining Excess Entry in Winner-Take-All Markets," Management Science, INFORMS, vol. 69(2), pages 1050-1069, February.
    6. George Wu & Alex B. Markle, 2008. "An Empirical Test of Gain-Loss Separability in Prospect Theory," Management Science, INFORMS, vol. 54(7), pages 1322-1335, July.
    7. Victor H. Gonzalez-Jimenez, 2019. "Contracting Probability Distortions," Vienna Economics Papers vie1901, University of Vienna, Department of Economics.
    8. Martín Egozcue & Luis Fuentes García & Ričardas Zitikis, 2023. "The Slicing Method: Determining Insensitivity Regions of Probability Weighting Functions," Computational Economics, Springer;Society for Computational Economics, vol. 61(4), pages 1369-1402, April.
    9. Narges Hajimoladarvish, 2017. "Very Low Probabilities in the Loss Domain," The Geneva Risk and Insurance Review, Palgrave Macmillan;International Association for the Study of Insurance Economics (The Geneva Association), vol. 42(1), pages 41-58, March.
    10. Michael H. Birnbaum & Ulrich Schmidt & Miriam D. Schneider, 2017. "Testing independence conditions in the presence of errors and splitting effects," Journal of Risk and Uncertainty, Springer, vol. 54(1), pages 61-85, February.
    11. George Wu & Jiao Zhang & Mohammed Abdellaoui, 2005. "Testing Prospect Theories Using Probability Tradeoff Consistency," Journal of Risk and Uncertainty, Springer, vol. 30(2), pages 107-131, January.
    12. Anthony Newell, 2020. "Is your heart weighing down your prospects? Interoception, risk literacy and prospect theory," QuBE Working Papers 058, QUT Business School.
    13. Victor H. Gonzalez-Jimenez, 2019. "Contracting Probability Distortions," Vienna Economics Papers 1901, University of Vienna, Department of Economics.
    14. Michael H. Birnbaum, 2005. "Three New Tests of Independence That Differentiate Models of Risky Decision Making," Management Science, INFORMS, vol. 51(9), pages 1346-1358, September.
    15. Ilke Aydogan & Yu Gao, 2020. "Experience and rationality under risk: re-examining the impact of sampling experience," Experimental Economics, Springer;Economic Science Association, vol. 23(4), pages 1100-1128, December.
    16. Filiz-Ozbay, Emel & Guryan, Jonathan & Hyndman, Kyle & Kearney, Melissa & Ozbay, Erkut Y., 2015. "Do lottery payments induce savings behavior? Evidence from the lab," Journal of Public Economics, Elsevier, vol. 126(C), pages 1-24.
    17. Hamza Bahaji, 2011. "Incentives from stock option grants: a behavioral approach," Post-Print halshs-00681607, HAL.
    18. Ariane Charpin, 2018. "Tests des modèles de décision en situation de risque. Le cas des parieurs hippiques en France," Revue économique, Presses de Sciences-Po, vol. 69(5), pages 779-803.
    19. Thomas Epper & Helga Fehr-Duda & Adrian Bruhin, 2011. "Viewing the future through a warped lens: Why uncertainty generates hyperbolic discounting," Journal of Risk and Uncertainty, Springer, vol. 43(3), pages 169-203, December.
    20. Attema, Arthur E. & l’Haridon, Olivier & van de Kuilen, Gijs, 2019. "Measuring multivariate risk preferences in the health domain," Journal of Health Economics, Elsevier, vol. 64(C), pages 15-24.

    More about this item

    Keywords

    Prospect theory; Risk preference; Decision making under risk; Hierarchical parameter estimation; Maximum likelihood;
    All these keywords.

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:stz:wpaper:eth-rc-14-005. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Claudio J. Tessone (email available below). General contact details of provider: https://edirc.repec.org/data/dmethch.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.