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Aspirational Preferences and Their Representation by Risk Measures

Author

Listed:
  • David B. Brown

    () (Fuqua School of Business, Duke University, Durham, North Carolina 27708)

  • Enrico De Giorgi

    () (Department of Economics, University of St. Gallen, CH-9000 St. Gallen, Switzerland)

  • Melvyn Sim

    () (NUS Business School, National University of Singapore, Singapore 119245, Republic of Singapore)

Abstract

We consider choice over uncertain, monetary payoffs and study a general class of preferences. These preferences favor diversification, except perhaps on a subset of sufficiently disliked acts over which concentration is instead preferred. This structure encompasses a number of known models (e.g., expected utility and several variants under a concave utility function). We show that such preferences share a representation in terms of a family of measures of risk and targets. Specifically, the choice function is equivalent to selection of a maximum index level such that the risk of beating the target at that level is acceptable. This representation may help to uncover new models of choice. One that we explore in detail is the special case when the targets are bounded. This case corresponds to a type of satisficing and has descriptive relevance. Moreover, the model is amenable to large-scale optimization. This paper was accepted by Teck Ho, decision analysis.

Suggested Citation

  • David B. Brown & Enrico De Giorgi & Melvyn Sim, 2012. "Aspirational Preferences and Their Representation by Risk Measures," Management Science, INFORMS, vol. 58(11), pages 2095-2113, November.
  • Handle: RePEc:inm:ormnsc:v:58:y:2012:i:11:p:2095-2113
    DOI: 10.1287/mnsc.1120.1537
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    File URL: http://dx.doi.org/10.1287/mnsc.1120.1537
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Giuseppe Attanasi & Nikolaos Georgantzís & Valentina Rotondi & Daria Vigani, 2018. "Lottery- and survey-based risk attitudes linked through a multichoice elicitation task," Theory and Decision, Springer, vol. 84(3), pages 341-372, May.
    2. Lucy Gongtao Chen & Daniel Zhuoyu Long & Melvyn Sim, 2015. "On Dynamic Decision Making to Meet Consumption Targets," Operations Research, INFORMS, vol. 63(5), pages 1117-1130, October.
    3. Wolfram Wiesemann & Daniel Kuhn & Melvyn Sim, 2014. "Distributionally Robust Convex Optimization," Operations Research, INFORMS, vol. 62(6), pages 1358-1376, December.
    4. William B. Haskell & Wenjie Huang & Huifu Xu, 2018. "Preference Elicitation and Robust Optimization with Multi-Attribute Quasi-Concave Choice Functions," Papers 1805.06632, arXiv.org.
    5. William B. Haskell & J. George Shanthikumar & Z. Max Shen, 2017. "Aspects of optimization with stochastic dominance," Annals of Operations Research, Springer, vol. 253(1), pages 247-273, June.
    6. Eeckhoudt, Louis & Fiori, Anna Maria & Rosazza Gianin, Emanuela, 2016. "Loss-averse preferences and portfolio choices: An extension," European Journal of Operational Research, Elsevier, vol. 249(1), pages 224-230.
    7. Lucy Gongtao Chen & Daniel Zhuoyu Long & Georgia Perakis, 2015. "The Impact of a Target on Newsvendor Decisions," Manufacturing & Service Operations Management, INFORMS, vol. 17(1), pages 78-86, February.
    8. Patrick Jaillet & Jin Qi & Melvyn Sim, 2016. "Routing Optimization Under Uncertainty," Operations Research, INFORMS, vol. 64(1), pages 186-200, February.
    9. Jun-Ya Gotoh & Michael Jong Kim & Andrew E. B. Lim, 2017. "Calibration of Distributionally Robust Empirical Optimization Models," Papers 1711.06565, arXiv.org, revised May 2020.
    10. Van Vliet, Ben, 2017. "Capability satisficing in high frequency trading," Research in International Business and Finance, Elsevier, vol. 42(C), pages 509-521.
    11. Ling, Aifan & Sun, Jie & Yang, Xiaoguang, 2014. "Robust tracking error portfolio selection with worst-case downside risk measures," Journal of Economic Dynamics and Control, Elsevier, vol. 39(C), pages 178-207.
    12. Qi, Jin & Sim, Melvyn & Sun, Defeng & Yuan, Xiaoming, 2016. "Preferences for travel time under risk and ambiguity: Implications in path selection and network equilibrium," Transportation Research Part B: Methodological, Elsevier, vol. 94(C), pages 264-284.
    13. Samuel Drapeau & Michael Kupper, 2013. "Risk Preferences and Their Robust Representation," Mathematics of Operations Research, INFORMS, vol. 38(1), pages 28-62, February.
    14. Post, Thierry & Kopa, Miloš, 2013. "General linear formulations of stochastic dominance criteria," European Journal of Operational Research, Elsevier, vol. 230(2), pages 321-332.
    15. Joel Goh & Nicholas G. Hall, 2013. "Total Cost Control in Project Management via Satisficing," Management Science, INFORMS, vol. 59(6), pages 1354-1372, June.
    16. Magron, Camille, 2014. "Investors’ aspirations and portfolio performance," Finance Research Letters, Elsevier, vol. 11(2), pages 153-160.

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