Accessibility statement

The descriptive and predictive adequacy of theories of decision making under uncertainty/ambiguity

John D Hey, Gianna Lotito and Anna Maffioletti

Journal of Risk and Uncertainty 2010


In this paper we examine the performance of theories of decision making under uncertainty/ambiguity from the perspective of their descriptive and predictive power. To this end, we employ an innovative experimental design which enables us to reproduce ambiguity in the laboratory in a transparent and non-probabilistic way. We find that judging theories on the basis of their theoretical appeal, or on their ability to do well in terms of estimation, is not the same as judging them on the basis of their predictive power. We find that the models that perform better in an aggregate sense are Gilboa and Schmeidler’s MaxMin and MaxMax Expected Utility Models, and Ghiradarto et al.’s Alpha Model, implying that more elegant theoretical models do not perform as well as relatively simple models. This suggests that decision makers, when confronted with a difficult problem, try to simplify it, rather than apply a sophisticated decision rule.

JEL Classifications

D81, C91


Ambiguity, Bingo Blower, Choquet Expected Utility, Decision Field Theory, Decision Making, Subjective Expected Utility, Hurwicz Criterion, (Gilboa and Schmeidler) MaxMin EU, (Gilboa and Schmeidler) MaxMax EU, (Ghirardato) Alpha-Model, MaxMin, MaxMax, Minimum Regret, Prospect Theory, Uncertainty.


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The data ( 362kb download), kindly organised by Peter Wakker is here.