Assessing multiple prior models of behaviour under ambiguity Authors
First Online: 28 March 2013 DOI:
Cite this article as: Conte, A. & Hey, J.D. J Risk Uncertain (2013) 46: 113. doi:10.1007/s11166-013-9164-x Abstract
The recent spate of theoretical models of behaviour under ambiguity can be partitioned into two sets: those involving multiple priors and those not involving multiple priors. This paper provides an experimental investigation into the first set. Using an appropriate experimental interface we examine the fitted and predictive power of the various theories. We first estimate subject-by-subject, and then estimate and predict using a mixture model over the contending theories. The individual estimates suggest that 24% of our 149 subjects have behaviour consistent with Expected Utility, 56% with the Smooth Model, 11% with Rank Dependent Expected Utility and 9% with the Alpha Model; these figures are close to the mixing proportions obtained from the mixture estimates where the respective posterior probabilities of each of them being of the various types are 25%, 50%, 20% and 5%; and using the predictions 22%, 53%, 22% and 3%. The Smooth model appears the best.
Keywords Alpha model Ambiguity Expected utility Mixture models Rank dependent expected utility Smooth model
The authors would like to thank an anonymous referee for very helpful and sympathetic comments which led to significant improvements in the paper.
Abdellaoui, M., Baillon, A., Placido, L., & Wakker, P. (2011). The rich domain of uncertainty: source functions and their experimental implementation.
American Economic Review, 101
Ahn, D.S., Choi, S., Gale, D., Kariv, S. (2010). Estimating ambiguity aversion in a portfolio choice experiment.
Andersen, S., Harrison, G. W., Lau, M. I., & Rutström, E. E. (2006). Elicitation using multiple price list formats.
Experimental Economics, 9
Andersen, S., Fountain, J., Harrison, G.W., Rutström, E.E. (2009). Estimating aversion to uncertainty.
Baillon, A. (2008). Eliciting subjective probabilities through exchangeable events: an advantage and a limitation.
Decision Analysis, 5
Camerer, C. (1995). Individual decision making. In J. Kagel, A. Roth (Eds.),
Handbook of experimental economics (pp. 587–703). Princeton University Press.
Camerer, C., & Weber, M. (1992). Recent development in modelling preferences: uncertainty and ambiguity.
Journal of Risk and Uncertainty, 5
Conte, A., Hey, J. D., & Moffatt, P. G. (2011). Mixture models of choice under risk.
Journal of Econometrics, 162
Gajdos, T., Hayashi, T., Tallon, J. M., & Vergnaud, J. C. (2008). Attitude toward imprecise information.
Journal of Economic Theory, 140, 27–65.
Ghirardato, P., Maccheroni, F., & Marinacci, M. (2004). Differentiating ambiguity and ambiguity attitude.
Journal of Economic Theory, 118
Gilboa, I., & Schmeidler, D. (1989). Maxmin expected utility with non-unique prior.
Journal of Mathematical Economics, 18
Greiner, B. (2004). The online recruitment system ORSEE 2.0—A guide for the organization of experiments in economics.
University of Cologne Discussion Paper
Halevy, Y. (2007). Ellsberg revisited: an experimental study.
Hey, J.D., & Pace, M. (2011). The explanatory and predictive power of non two-stage-probability theories of decision making under ambiguity.
University of York Department of Economics and Related Studies Discussion Paper 11/22.
Hey, J. D., Lotito, G., & Maffioletti, A. (2010). The descriptive and predictive adequacy of theories of decision making under uncertainty/ambiguity.
Journal of Risk and Uncertainty, 41
Kahneman, D., & Tversky, A. (1979). Prospect theory: an analysis of decision under risk.
Klibanoff, P., Marinaci, M., & Mukerji, S. (2005). A smooth model of decision making under ambiguity.
Moffat, P. G., & Peters, S. A. (2001). Testing for the presence of a tremble in economic experiments.
Experimental Economics, 4, 221–228.
Preminger, A., & Wettstein, D. (2005). Using the penalized likelihood method for model selection with nuisance parameters present only under the alternative: an application to switching regression models.
Journal of Time Series Analysis, 26
Quiggin, J. (1982). A theory of anticipated utility.
Journal of Economic Behavior and Organization, 3
Schmeidler, D. (1989). Subjective probability and expected utility without additivity.
Segal, U. (1987). The Ellsberg Paradox and risk aversion: an anticipated utility approach.
International Economic Review, 28
Tversky, A., & Kahneman, D. (1992). Advances in prospect theory: cumulative representation of uncertainty.
Journal of Risk and Uncertainty, 5
Wilcox, N.T. (2007). Predicting risky choices out of context: A Monte Carlo study. University of Houston Working Paper.
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