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Comparison of Mean-Variance theory and Expected-Utility theory through a Laboratory Experiment

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  • Andrea Morone

Abstract

In the 40’s and early 50’ two decision theories were proposed and have since dominated the scene of the fascinating field of decision-making. In 1944 – when von Neumann and Morgenstern showed that if preferences are consistent with a set of axioms then it is possible to represent these preference by the expectation of some utility function – Expected Utility theory provide a natural way to establish “measurable utility”. In the early 50’s Markowitz introduced the Mean-Variance theory that is the basis of modern portfolio selection theory. Even if both models were analyzed from virtually all possible point of view; although they were tested against several generalizations; even though they seams to be the most attractive theories of decision making, they were never testes gains each other. This paper will try to fill this gap. It investigates, using experimental data, which of these two models represent a better approximation of subjects’ preferences.

Suggested Citation

  • Andrea Morone, 2004. "Comparison of Mean-Variance theory and Expected-Utility theory through a Laboratory Experiment," Experimental 0402001, University Library of Munich, Germany.
    • Handle: RePEc:wpa:wuwpex:0402001
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    References listed on IDEAS

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    8. John D. Hey, 2018. "Does Repetition Improve Consistency?," World Scientific Book Chapters, in: Experiments in Economics Decision Making and Markets, chapter 2, pages 13-62, World Scientific Publishing Co. Pte. Ltd..
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    Cited by:

    1. Morone, Andrea, 2010. "On price data elicitation: A laboratory investigation," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 39(5), pages 540-545, October.
    2. Biggar, Darryl R. & Hesamzadeh, Mohammad Reza, 2022. "An integrated theory of dispatch and hedging in wholesale electric power markets," Energy Economics, Elsevier, vol. 112(C).
    3. Andrea Morone & Ulrich Schmidt, 2008. "An Experimental Investigation of Alternatives to Expected Utility Using Pricing Data," Economics Bulletin, AccessEcon, vol. 4(20), pages 1-12.
    4. repec:ebl:ecbull:v:4:y:2008:i:20:p:1-12 is not listed on IDEAS
    5. Morone, Andrea & Temerario, Tiziana, 2015. "Eliciting Preferences Over Risk: An Experiment," MPRA Paper 68519, University Library of Munich, Germany.
    6. Zonna, Davide, 2016. "Sprechi di cibo e tentativi di riduzione. Un caso sperimentale [Avoiding food waste. A field experiment]," MPRA Paper 76097, University Library of Munich, Germany.
    7. A. Morone & P. Morone, 2014. "Estimating individual and group preference functionals using experimental data," Theory and Decision, Springer, vol. 77(3), pages 403-422, October.
    8. Dolors Berga & Jose I. Silva, 2021. "Risk-Free Versus Risky Assets: Teaching a Portfolio Model with Application to the Stock Market," Journal of Economics Teaching, Journal of Economics Teaching, vol. 6(2), pages 76-94, October.
    9. Morone, Andrea & Ozdemir, Ozlem, 2012. "Black swan protection: an experimental investigation," MPRA Paper 38842, University Library of Munich, Germany.
    10. Fatma Lajeri-Chaherli, 2016. "On The Concavity And Quasiconcavity Properties Of ( Σ , Μ ) Utility Functions," Bulletin of Economic Research, Wiley Blackwell, vol. 68(3), pages 287-296, April.
    11. Morone, Andrea & Morone, Piergiuseppe, 2012. "Are small groups expected utility?," MPRA Paper 38198, University Library of Munich, Germany.
    12. Yumi Oum & Shmuel S. Oren, 2010. "Optimal Static Hedging of Volumetric Risk in a Competitive Wholesale Electricity Market," Decision Analysis, INFORMS, vol. 7(1), pages 107-122, March.
    13. Temerario, Tiziana, 2014. "Individual and Group Behaviour Toward Risk: A Short Survey," MPRA Paper 58079, University Library of Munich, Germany.

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    More about this item

    Keywords

    Expected utility; Mean variance; preference functional; pair wise choice; experiments;
    All these keywords.

    JEL classification:

    • C9 - Mathematical and Quantitative Methods - - Design of Experiments

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