Template-type: ReDif-Paper 1.0
Author-Name: Tsakas, Elias
Author-workplace-name: Microeconomics & Public Economics, RS: GSBE ETBC, RS: GSBE Theme Conflict & Cooperation, RS: GSBE Theme Human Decisions and Policy Design
Title: Robust scoring rules
Abstract: 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<br/>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<br/>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.
Classification-JEL: c91,d81,d82,d83,d87
Series: GSBE Research Memoranda
Creation-Date: 20181008
Number: 023
File-URL: https://cris.maastrichtuniversity.nl/ws/files/29754027/RM18023.pdf
File-Format: application/pdf
File-Size: 747913
Handle: Repec:unm:umagsb:2018023
DOI: 10.26481/umagsb.2018023