Robust scoring rules
Research output: Working paper › Professional
crucial both for establishing that decision-theoretic models under uncertainty are testable. From an applied point of view, they are needed for eliciting unbiased estimates of population beliefs. We prove that a robust scoring rule exists under mild axioms on the attention costs. These axioms are shown to characterize the class of posterior-separable cost functions. Our existence
proof is constructive, thus identifying an entire class of robust scoring rules. Subsequently, we show that we can arbitrarily approximate the agent's prior beliefs with a quadratic scoring rule. The same holds true for a discrete scoring rule. Finally, we show that the prior beliefs can be approximated, even when we are uncertain about the exact specification of the agent's attention costs.
- belief elicitation, prior beliefs, rational inattention, hidden information costs, posterior-separability, Shannon entropy, population beliefs, testing decision-theoretic models
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