IDEAS home Printed from https://ideas.repec.org/a/inm/ordeca/v14y2017i1p65-73.html
   My bibliography  Save this article

Clear Preferences Under Partial Distribution Information

Author

Listed:
  • Chin Hon Tan

    (Industrial and Systems Engineering, National University of Singapore, Singapore 117576)

  • Chunling Luo

    (Industrial and Systems Engineering, National University of Singapore, Singapore 117576)

Abstract

Stochastic dominance is often used to study preference between different distributions of outcomes. In the stochastic dominance literature, distributions of outcomes are often assumed to be known. However, complete distribution information is rarely available in practice. In this paper, we study weighted almost first-degree stochastic dominance (WAFSD) under limited distribution information. In particular, we show that it is possible to determine WAFSD with linear canonical utility based on expected rewards when outcomes are bounded from below. Furthermore, we illustrate how WAFSD based on more general forms of canonical utility functions can be ensured when additional moment information is available. The key insight is that finite distribution moments can be sufficient for revealing clear preferences in practice, despite the fact that finite distribution moments are generally insufficient for ensuring preferences across all utility functions.

Suggested Citation

  • Chin Hon Tan & Chunling Luo, 2017. "Clear Preferences Under Partial Distribution Information," Decision Analysis, INFORMS, vol. 14(1), pages 65-73, March.
  • Handle: RePEc:inm:ordeca:v:14:y:2017:i:1:p:65-73
    DOI: 10.1287/deca.2016.0344
    as

    Download full text from publisher

    File URL: https://doi.org/10.1287/deca.2017.0344
    Download Restriction: no

    File URL: https://libkey.io/10.1287/deca.2016.0344?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Steffen Brenner, 2015. "The Risk Preferences of U.S. Executives," Management Science, INFORMS, vol. 61(6), pages 1344-1361, June.
    2. Stein W. Wallace, 2000. "Decision Making Under Uncertainty: Is Sensitivity Analysis of Any Use?," Operations Research, INFORMS, vol. 48(1), pages 20-25, February.
    3. Larry Y. Tzeng & Rachel J. Huang & Pai-Ta Shih, 2013. "Revisiting Almost Second-Degree Stochastic Dominance," Management Science, INFORMS, vol. 59(5), pages 1250-1254, May.
    4. Sebastian Ebert, 2013. "Moment characterization of higher-order risk preferences," Theory and Decision, Springer, vol. 74(2), pages 267-284, February.
    5. Turan G. Bali & Stephen J. Brown & K. Ozgur Demirtas, 2013. "Do Hedge Funds Outperform Stocks and Bonds?," Management Science, INFORMS, vol. 59(8), pages 1887-1903, August.
    6. Hanoch, Giora & Levy, Haim, 1970. "Efficient Portfolio Selection with Quadratic and Cubic Utility," The Journal of Business, University of Chicago Press, vol. 43(2), pages 181-189, April.
    7. Haim Levy, 2016. "Aging Population, Retirement, and Risk Taking," Management Science, INFORMS, vol. 62(5), pages 1415-1430, May.
    8. Haim Levy, 1992. "Stochastic Dominance and Expected Utility: Survey and Analysis," Management Science, INFORMS, vol. 38(4), pages 555-593, April.
    9. Ilia Tsetlin & Robert L. Winkler & Rachel J. Huang & Larry Y. Tzeng, 2015. "Generalized Almost Stochastic Dominance," Operations Research, INFORMS, vol. 63(2), pages 363-377, April.
    10. Markowitz, Harry, 2014. "Mean–variance approximations to expected utility," European Journal of Operational Research, Elsevier, vol. 234(2), pages 346-355.
    11. Chin Hon Tan, 2015. "Weighted Almost Stochastic Dominance: Revealing the Preferences of Most Decision Makers in the St. Petersburg Paradox," Decision Analysis, INFORMS, vol. 12(2), pages 74-80, June.
    12. Moshe Leshno & Haim Levy, 2002. "Preferred by "All" and Preferred by "Most" Decision Makers: Almost Stochastic Dominance," Management Science, INFORMS, vol. 48(8), pages 1074-1085, August.
    13. Aissi, Hassene & Bazgan, Cristina & Vanderpooten, Daniel, 2009. "Min-max and min-max regret versions of combinatorial optimization problems: A survey," European Journal of Operational Research, Elsevier, vol. 197(2), pages 427-438, September.
    14. Levy, H & Markowtiz, H M, 1979. "Approximating Expected Utility by a Function of Mean and Variance," American Economic Review, American Economic Association, vol. 69(3), pages 308-317, June.
    15. Haim Levy & Moshe Leshno & Boaz Leibovitch, 2010. "Economically relevant preferences for all observed epsilon," Annals of Operations Research, Springer, vol. 176(1), pages 153-178, April.
    16. Patrick L. Brockett & James R. Garven, 1998. "A Reexamination of the Relationship Between Preferences and Moment Orderings by Rational Risk-Averse Investors," The Geneva Risk and Insurance Review, Palgrave Macmillan;International Association for the Study of Insurance Economics (The Geneva Association), vol. 23(2), pages 127-137, December.
    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. Luo, Chunling & Tan, Chin Hon & Liu, Xiao, 2020. "Maximum excess dominance: Identifying impractical solutions in linear problems with interval coefficients," European Journal of Operational Research, Elsevier, vol. 282(2), pages 660-676.
    2. Chunling Luo & Chin Hon Tan, 2020. "Almost Stochastic Dominance for Most Risk-Averse Decision Makers," Decision Analysis, INFORMS, vol. 17(2), pages 169-184, June.

    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. Chunling Luo & Chin Hon Tan, 2020. "Almost Stochastic Dominance for Most Risk-Averse Decision Makers," Decision Analysis, INFORMS, vol. 17(2), pages 169-184, June.
    2. Hermann Jahnke & Jan Thomas Martini & Tobias Wiens, 2019. "Price limits under incomplete preference information based on almost stochastic dominance," Business Research, Springer;German Academic Association for Business Research, vol. 12(1), pages 241-269, April.
    3. Lee, Yung-Tsung & Kung, Ko-Lun & Liu, I-Chien, 2018. "Profitability and risk profile of reverse mortgages: A cross-system and cross-plan comparison," Insurance: Mathematics and Economics, Elsevier, vol. 78(C), pages 255-266.
    4. Bruni, Renato & Cesarone, Francesco & Scozzari, Andrea & Tardella, Fabio, 2017. "On exact and approximate stochastic dominance strategies for portfolio selection," European Journal of Operational Research, Elsevier, vol. 259(1), pages 322-329.
    5. Yi-Chieh Huang & Kamhon Kan & Larry Y. Tzeng & Kili C. Wang, 2021. "Estimating the Critical Parameter in Almost Stochastic Dominance from Insurance Deductibles," Management Science, INFORMS, vol. 67(8), pages 4742-4755, August.
    6. Jow-Ran Chang & Wei-Han Liu & Mao-Wei Hung, 2019. "Revisiting generalized almost stochastic dominance," Annals of Operations Research, Springer, vol. 281(1), pages 175-192, October.
    7. Chin Hon Tan, 2015. "Weighted Almost Stochastic Dominance: Revealing the Preferences of Most Decision Makers in the St. Petersburg Paradox," Decision Analysis, INFORMS, vol. 12(2), pages 74-80, June.
    8. Xu, Guo & Wing-Keung, Wong & Lixing, Zhu, 2013. "Almost Stochastic Dominance for Risk-Averse and Risk-Seeking Investors," MPRA Paper 51744, University Library of Munich, Germany.
    9. Guo, Xu & Wong, Wing-Keung & Zhu, Lixing, 2016. "Almost stochastic dominance for risk averters and risk seeker," Finance Research Letters, Elsevier, vol. 19(C), pages 15-21.
    10. Francesco Cesarone & Justo Puerto, 2024. "New approximate stochastic dominance approaches for Enhanced Indexation models," Papers 2401.12669, arXiv.org.
    11. Guo, Xu & Post, Thierry & Wong, Wing-Keung & Zhu, Lixing, 2014. "Moment conditions for Almost Stochastic Dominance," Economics Letters, Elsevier, vol. 124(2), pages 163-167.
    12. Guo, Xu & Wong, Wing-Keung & Zhu, Lixing, 2013. "Almost Stochastic Dominance and Moments," MPRA Paper 49274, University Library of Munich, Germany.
    13. Tzu-Ying Chen & Yi-Hsin Elsa Hsu & Rachel J. Huang & Larry Y. Tzeng, 2021. "Making socioeconomic health inequality comparisons when health concentration curves intersect," Social Choice and Welfare, Springer;The Society for Social Choice and Welfare, vol. 57(4), pages 875-899, November.
    14. Denuit, Michel M. & Huang, Rachel J. & Tzeng, Larry Y. & Wang, Christine W., 2014. "Almost marginal conditional stochastic dominance," Journal of Banking & Finance, Elsevier, vol. 41(C), pages 57-66.
    15. Guo, Xu & Zhu, Xuehu & Wong, Wing-Keung & Zhu, Lixing, 2013. "A note on almost stochastic dominance," Economics Letters, Elsevier, vol. 121(2), pages 252-256.
    16. Ilia Tsetlin & Robert L. Winkler & Rachel J. Huang & Larry Y. Tzeng, 2015. "Generalized Almost Stochastic Dominance," Operations Research, INFORMS, vol. 63(2), pages 363-377, April.
    17. Tommaso Lando & Lucio Bertoli-Barsotti, 2019. "Distorted stochastic dominance: a generalized family of stochastic orders," Papers 1909.04767, arXiv.org.
    18. Bi, Hongwei & Huang, Rachel J. & Tzeng, Larry Y. & Zhu, Wei, 2019. "Higher-order Omega: A performance index with a decision-theoretic foundation," Journal of Banking & Finance, Elsevier, vol. 100(C), pages 43-57.
    19. Chia-Lin Chang & Michael McAleer & Wing-Keung Wong, 2018. "Big Data, Computational Science, Economics, Finance, Marketing, Management, and Psychology: Connections," JRFM, MDPI, vol. 11(1), pages 1-29, March.
    20. Zhao, Kena & Ng, Tsan Sheng & Tan, Chin Hon & Pang, Chee Khiang, 2021. "An almost robust model for minimizing disruption exposures in supply systems," European Journal of Operational Research, Elsevier, vol. 295(2), pages 547-559.

    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:inm:ordeca:v:14:y:2017:i:1:p:65-73. 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: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.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.