Robust scoring rules
| Autor: | Tsakas, Elias |
|---|---|
| Přispěvatelé: | Microeconomics & Public Economics, RS: GSBE ETBC, RS: GSBE Theme Conflict & Cooperation, RS: GSBE Theme Human Decisions and Policy Design |
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
belief elicitation
rational inattention c91 - Design of Experiments: Laboratory Individual d87 - Neuroeconomics prior beliefs hidden information costs d81 - Criteria for Decision-Making under Risk and Uncertainty posterior-separability Learning Information and Knowledge Communication Shannon entropy testing decision-theoretic models population beliefs |
| Popis: | We study elicitation of latent (prior) beliefs when the agent can acquire information via a costly attention strategy. We introduce a mechanism that simultaneously makes it strictly dominant to (a) not acquire any information, and (b) report truthfully. We call such a mechanism a robust scoring rule. Robust scoring rules are important for different reasons. Theoretically, they are 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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