Neurobiological studies of risk assessment: A comparison of expected utility and mean-variance approaches |
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Authors: | Mathieu d’Acremont Peter Bossaerts |
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Institution: | (1) Department of Economics, Orfalea College of Business, California Polytechnic State University, San Luis Obispo, CA 93407, USA |
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Abstract: | When modeling valuation under uncertainty, economists generally prefer expected utility because it has an axiomatic foundation,
meaning that the resulting choices will satisfy a number of rationality requirements. In expected utility theory, values are
computed by multiplying probabilities of each possible state of nature by the payoff in that state and summing the results. The drawback of this approach is that all state probabilities need to
be dealt with separately, which becomes extremely cumbersome when it comes to learning. Finance academics and professionals,
however, prefer to value risky prospects in terms of a trade-off between expected reward and risk, where the latter is usually
measured in terms of reward variance. This mean-variance approach is fast and simple and greatly facilitates learning, but
it impedes assigning values to new gambles on the basis of those of known ones. To date, it is unclear whether the human brain
computes values in accordance with expected utility theory or with mean-variance analysis. In this article, we discuss the
theoretical and empirical arguments that favor one or the other theory. We also propose a new experimental paradigm that could
determine whether the human brain follows the expected utility or the mean-variance approach. Behavioral results of implementation
of the paradigm are discussed. |
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