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Discriminating between Preference Functionals: A Monte Carlo Study

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  • Carbone, Enrica

Abstract

This paper reports on the results of a Monte Carlo investigation into the power of commonly employed procedures for identifying the 'correct' preference functional of individuals, and hence for discriminating between the large number of preference functionals now advocated in the theoretical literature. The paper also asks which of two commonly employed experimental procedures might be the most efficient in this respect. The results show that several of the 'newer' preference functionals are difficult to distinguish empirically--at least on the basis of conventional experimental tests--and that the Complete ranking experimental design might be better than the Pairwise Choice design. The conclusion of the paper is that more thought should therefore be given to the question of the experimental design. Copyright 1997 by Kluwer Academic Publishers

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  • Carbone, Enrica, 1997. "Discriminating between Preference Functionals: A Monte Carlo Study," Journal of Risk and Uncertainty, Springer, vol. 15(1), pages 29-54, October.
    • Handle: RePEc:kap:jrisku:v:15:y:1997:i:1:p:29-54
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    Cited by:

    1. Healy, Paul J. & Park, Hyoeun, 2023. "Model selection accuracy in behavioral game theory: A simulation," European Economic Review, Elsevier, vol. 152(C).
    2. John Hey, 2018. "Comparing Theories: What Are We Looking For?," World Scientific Book Chapters, in: Experiments in Economics Decision Making and Markets, chapter 14, pages 331-352, World Scientific Publishing Co. Pte. Ltd..
    3. Andrea Morone, 2008. "Comparison of Mean-Variance Theory and Expected-Utility Theory through a Laboratory Experiment," Economics Bulletin, AccessEcon, vol. 3(40), pages 1-7.
    4. Cheng-Min Feng & Chao-Chung Kang & Haider Ali Khan, 2002. "On Modelling Negotiations within a Dynamic Multi-objective Programming Framework: Analysis of Risk Measurement with an Application to Large BOT Projects," CIRJE F-Series CIRJE-F-161, CIRJE, Faculty of Economics, University of Tokyo.

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