IDEAS home Printed from https://ideas.repec.org/a/ebl/ecbull/eb-09-00013.html
   My bibliography  Save this article

On the empirical relevance of st. petersburg lotteries

Author

Listed:
  • James C. Cox

    (Georgia State University)

  • Vjollca Sadiraj

    (Georgia State University)

  • Bodo Vogt

    (University of Magdeburg)

Abstract

Expected value theory has been known for centuries to be subject to critique by St. Petersburg paradox arguments. And there is a traditional rebuttal of the critique that denies the empirical relevance of the paradox because of its apparent dependence on existence of credible offers to pay unbounded sums of money. Neither critique nor rebuttal focus on the question with empirical relevance: Do people make choices in bounded St. Petersburg games that are consistent with expected value theory? This paper reports an experiment that addresses that question.

Suggested Citation

  • James C. Cox & Vjollca Sadiraj & Bodo Vogt, 2009. "On the empirical relevance of st. petersburg lotteries," Economics Bulletin, AccessEcon, vol. 29(1), pages 214-220.
  • Handle: RePEc:ebl:ecbull:eb-09-00013
    as

    Download full text from publisher

    File URL: http://www.accessecon.com/Pubs/EB/2009/Volume29/EB-09-V29-I1-P22.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Marc Rieger & Mei Wang, 2006. "Cumulative prospect theory and the St. Petersburg paradox," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 28(3), pages 665-679, August.
    2. Yaari, Menahem E, 1987. "The Dual Theory of Choice under Risk," Econometrica, Econometric Society, vol. 55(1), pages 95-115, January.
    3. Tversky, Amos & Kahneman, Daniel, 1992. "Advances in Prospect Theory: Cumulative Representation of Uncertainty," Journal of Risk and Uncertainty, Springer, vol. 5(4), pages 297-323, October.
    4. James C. Cox & Vjollca Sadiraj, 2008. "Risky Decisions in the Large and in the Small: Theory and Experiment," Experimental Economics Center Working Paper Series 2008-01, Experimental Economics Center, Andrew Young School of Policy Studies, Georgia State University.
    5. Harless, David W & Camerer, Colin F, 1994. "The Predictive Utility of Generalized Expected Utility Theories," Econometrica, Econometric Society, vol. 62(6), pages 1251-1289, 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. Eike B. Kroll & Bodo Vogt, 2009. "The St. Petersburg Paradox despite risk-seeking preferences: An experimental study," FEMM Working Papers 09004, Otto-von-Guericke University Magdeburg, Faculty of Economics and Management.
    2. James C. Cox & Eike B. Kroll & Marcel Lichters & Vjollca Sadiraj & Bodo Vogt, 2019. "The St. Petersburg paradox despite risk-seeking preferences: an experimental study," Business Research, Springer;German Academic Association for Business Research, vol. 12(1), pages 27-44, April.
    3. Yukalov, V.I., 2021. "A resolution of St. Petersburg paradox," Journal of Mathematical Economics, Elsevier, vol. 97(C).
    4. Tibor Neugebauer, 2010. "Moral Impossibility in the Petersburg Paradox : A Literature Survey and Experimental Evidence," LSF Research Working Paper Series 10-14, Luxembourg School of Finance, University of Luxembourg.
    5. Robert William, Vivian, 2013. "Ending the myth of the St Petersburg paradox," MPRA Paper 50515, University Library of Munich, Germany.
    6. Bronshtein, E. & Fatkhiev, O., 2018. "A Note on St. Petersburg Paradox," Journal of the New Economic Association, New Economic Association, vol. 38(2), pages 48-53.
    7. Kim Kaivanto & Eike Kroll, 2014. "Alternation bias and reduction in St. Petersburg gambles," Working Papers 65600286, Lancaster University Management School, Economics Department.

    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. Jakusch, Sven Thorsten, 2017. "On the applicability of maximum likelihood methods: From experimental to financial data," SAFE Working Paper Series 148, Leibniz Institute for Financial Research SAFE, revised 2017.
    2. Epper, Thomas & Fehr-Duda, Helga, 2017. "A Tale of Two Tails: On the Coexistence of Overweighting and Underweighting of Rare Extreme Events," Economics Working Paper Series 1705, University of St. Gallen, School of Economics and Political Science.
    3. Aluma Dembo & Shachar Kariv & Matthew Polisson & John Quah, 2021. "Ever since Allais," IFS Working Papers W21/15, Institute for Fiscal Studies.
    4. Abdellaoui, Mohammed & Bleichrodt, Han, 2007. "Eliciting Gul's theory of disappointment aversion by the tradeoff method," Journal of Economic Psychology, Elsevier, vol. 28(6), pages 631-645, December.
    5. Stephen G Dimmock & Roy Kouwenberg & Olivia S Mitchell & Kim Peijnenburg, 2021. "Household Portfolio Underdiversification and Probability Weighting: Evidence from the Field," The Review of Financial Studies, Society for Financial Studies, vol. 34(9), pages 4524-4563.
    6. Andersen, Steffen & Harrison, Glenn W. & Lau, Morten Igel & Rutström, Elisabet E., 2010. "Behavioral econometrics for psychologists," Journal of Economic Psychology, Elsevier, vol. 31(4), pages 553-576, August.
    7. Wakker, Peter P. & Zank, Horst, 2002. "A simple preference foundation of cumulative prospect theory with power utility," European Economic Review, Elsevier, vol. 46(7), pages 1253-1271, July.
    8. Boonen, Tim J. & Tan, Ken Seng & Zhuang, Sheng Chao, 2016. "The role of a representative reinsurer in optimal reinsurance," Insurance: Mathematics and Economics, Elsevier, vol. 70(C), pages 196-204.
    9. John Hey, "undated". "Experiments and the Economics of Individual Decision Making Under Risk and Uncertainty," Discussion Papers 95/49, Department of Economics, University of York.
    10. Eyal Baharad & Doron Kliger, 2013. "Market failure in light of non-expected utility," Theory and Decision, Springer, vol. 75(4), pages 599-619, October.
    11. 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.
    12. James C. Cox & Eike B. Kroll & Marcel Lichters & Vjollca Sadiraj & Bodo Vogt, 2019. "The St. Petersburg paradox despite risk-seeking preferences: an experimental study," Business Research, Springer;German Academic Association for Business Research, vol. 12(1), pages 27-44, April.
    13. Henry Stott, 2006. "Cumulative prospect theory's functional menagerie," Journal of Risk and Uncertainty, Springer, vol. 32(2), pages 101-130, March.
    14. Levy, Haim & Levy, Moshe, 2002. "Experimental test of the prospect theory value function: A stochastic dominance approach," Organizational Behavior and Human Decision Processes, Elsevier, vol. 89(2), pages 1058-1081, November.
    15. Diecidue, Enrico & Schmidt, Ulrich & Zank, Horst, 2009. "Parametric weighting functions," Journal of Economic Theory, Elsevier, vol. 144(3), pages 1102-1118, May.
    16. Xiaoxian Ma & Qingzhen Zhao & Jilin Qu, 2008. "Robust portfolio optimization with a generalized expected utility model under ambiguity," Annals of Finance, Springer, vol. 4(4), pages 431-444, October.
    17. Han Bleichrodt & Jose Luis Pinto, 2000. "A Parameter-Free Elicitation of the Probability Weighting Function in Medical Decision Analysis," Management Science, INFORMS, vol. 46(11), pages 1485-1496, November.
    18. Martina Nardon & Paolo Pianca, 2019. "Behavioral premium principles," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 42(1), pages 229-257, June.
    19. Pavlo Blavatskyy, 2018. "A second-generation disappointment aversion theory of decision making under risk," Theory and Decision, Springer, vol. 84(1), pages 29-60, January.
    20. Krzysztof Kontek & Michal Lewandowski, 2018. "Range-Dependent Utility," Management Science, INFORMS, vol. 64(6), pages 2812-2832, June.

    More about this item

    Keywords

    Decision Theory; St. Petersburg Lotteries; Experiment;
    All these keywords.

    JEL classification:

    • C9 - Mathematical and Quantitative Methods - - Design of Experiments
    • D8 - Microeconomics - - Information, Knowledge, and Uncertainty

    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:ebl:ecbull:eb-09-00013. 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: John P. Conley (email available below). General contact details of provider: .

    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.