James Best, Daniel Quigley, Maryam Saeedi, Ali Shourideh
We examine receiver-optimal mechanisms for aggregating information 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 makes a binary accept/reject decision that determines the 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 confer privately before reporting. However, for larger populations, the receiver can benefit from the informational divide. We introduce a novel incentive-compatibility-in-the-large approach to solve the high-dimensional mechanism design problem for the large-population limit. Using this, we 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 lead to payoffs that are bounded away from the first-best.
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