Události

Daniel Quigley, Ph.D.

University of Oxford, United Kingdom

Authors: James Best, Daniel Quigley, Maryam Saeedi and Ali Shourideh

Abstract: We examine mechanisms for aggregating information divided across large populations of biased senders. Each sender privately observes an unconditionally independent signal about an unknown state, so no sender can verify another’s report. A receiver makes a binary accept/reject decision whose payoffs (to all players) depend on the state. Even though cross-verification is impossible, we show the receiver can benefit from informational division. We introduce a novel incentive-compatibility-in-the-large approach that studies optimal design via the large-population limit. For fixed population size, optimal mechanisms are in general complex. However, we show that they converge to a simple mechanism that depends only on the payoff from acceptance, and punishes excessive consensus in the direction of the common bias. These surplus burning punishments yield payoffs bounded away from the first best; the resulting inefficiency demonstrates how our concept of informational division is distinct from standard models of information in large populations.

Full Text: Divide and Confer: Aggregating Information without Verification