A truth serum for non-bayesians: Correcting proper scoring rules for risk attitudes

Proper scoring rules provide convenient and highly efficient tools for incentive-compatible elicitations of subjective beliefs. As traditionally used, however, they are valid only under expected value maximization. This paper shows how they can be generalized to modern ("non-expected utility") theories of risk and ambiguity, yielding mutual benefits: users of scoring rules can benefit from the empirical realism of non-expected utility, and analysts of ambiguity attitudes can benefit from efficient measurements using proper scoring rules. An experiment demonstrates the feasibility of our generalization.

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