Divide or Confer: Aggregating Information without Verification

James Best, Daniel Quigley, Maryam Saeedi and Ali Shourideh

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Abstract: We examine receiver-optimal mechanisms for aggregating information that is divided across many biased senders. Each sender privately observes an unconditionally independent signal about an unknown state, so no sender can verify another's report. A receiver has a binary accept/reject decision which determines players' payoffs via the state. When information is divided across a small population and bias is low, the receiver-optimal mechanism coincides with the sender-preferred allocation, and can be implemented by letting senders \emph{confer} privately before reporting. However, for larger populations, we can benefit from the informational divide. We introduce a novel \emph{incentive-compatibility-in-the-large (ICL)} approach to solve the high-dimensional mechanism design problem for the large-population limit. We use this to show that optimal mechanisms converge to one that depends only on the accept payoff and punishes excessive consensus in the direction of the common bias. These surplus burning punishments imply payoffs are bounded away from the first-best level.

Date: 2025-07
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