Risk, uncertainty and discrete choice models
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This paper examines the cross-fertilizations of random utility models with the study of decision making under risk and uncertainty. We start with a description of the expected utility (EU) theory and then consider deviations from the standard EU frameworks, involving the Allais paradox and the Ellsberg paradox, inter alia. We then discuss how the resulting non-EU framework can be modeled and estimated within the framework of discrete choices in static and dynamic contexts. Our objectives in addressing risk and ambiguity in individual choice contexts are to understand the decision choice process and to use behavioral information for prediction, prescription, and policy analysis.
KeywordsDiscrete choice Decision making Risk Uncertainty (Cumulative) prospect theory Ambiguity
We benefited from the advice and comments of Mohammed Abdellaoui and Robin Lindsey, and from the editorial assistance of Leanne Russell. Eric Bradlow’s and Robert Meyer’s suggestions were very helpful for improving the first draft of the paper. Finally, André de Palma and Nathalie Picard would like to thank RiskAttitude, ANR (FR) program, for supporting financially the June 2007 meeting.
- Ackerberg, D., Benkard, L., Berry, S., & Pakes, A. (2007). Econometric tools for analyzing market outcomes. In J. J. Heckman, & E. Leamer (Eds.), Handbook of econometrics(vol. 6). Amsterdam: Elsevier.Google Scholar
- Brownstone, D., & Small, K. A. (2005). Valuing time and reliability: Assessing the evidence from road pricing demonstrations. Transportation Research A, 39, 279–293.Google Scholar
- Chamberlain, G. (1984). Panel data. In Z. Griliches, & M. Intriligator (Eds.), Handbook of econometrics (vol. 2, (pp. 1247–1318)). Amsterdam: Elsevier.Google Scholar
- Conte, A., Hey, J. D., & Moffatt, P. G. (2008). Mixture models of choice under risk. Journal of Econometrics, in press.Google Scholar
- de Palma, A., & Picard, N. (2005). Route choice decision under travel time uncertainty. Transportation Research Part A, 39(4), 295–324.Google Scholar
- Gajdos, T, Tallon, J.-M., & Vergnaud, J. C. (2008). Representation and aggregation of preferences under uncertainty. Journal of Economic Theory, doi: 10.1016/j.jet.2007.10.001.
- Grether, D. M., & Plott, C. R. (1979). Economic theory of choice and the preference reversal phenomenon. American Economic Review, 69, 623–638.Google Scholar
- Holt, C. A. (2006). Markets, games, and strategic behavior. Boston: Addison-Wesley.Google Scholar
- Machina, M. J. (1989). Dynamic consistency and non-expected utility models of choice under uncertainty. Journal of Economic Literature, 27, 1622-1688.Google Scholar
- McFadden, D. (1981). Chapter 5: Econometric models of probabilistic choices. In C. F. Manski, & D. McFadden (Eds.), Structural analysis of discrete data with economic applications (pp. 198–272). Chicago: University of Chicago Press.Google Scholar
- McFadden, D. (2001). Economic choices. Nobel lecture, December 2000. American Economic Review, 91(3), 351–378.Google Scholar
- Rust, J. (1994). Structural estimation of Markov decision processes. In R. Engle, & D. McFadden (Eds.), Handbook of econometrics (vol. 4, (pp. 3081–3143)). Amsterdam: Elsevier.Google Scholar
- Train, K. (2003). Discrete choice methods with simulation. New York: Cambridge University Press.Google Scholar
- Van Soest, A., Kapteyn, A., & Zissimopoulos, J. (2007). Using stated preferences data to analyze preferences for full and partial retirement. IZA working paper 2785, Bonn.Google Scholar