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

作者: John D. Hey , Gianna Lotito , Anna Maffioletti

DOI: 10.1007/S11166-010-9102-0

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

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