Resilient Information Aggregation

TL;DR

This paper studies a multi-sender information aggregation game with a mediator, proposing algorithms to design optimal, resilient platforms that maximize user welfare while ensuring incentive compatibility and resistance to group deviations.

Contribution

It extends the cheap talk model to multiple senders and a mediator, providing algorithms for designing resilient, welfare-maximizing aggregation platforms.

Findings

Efficient algorithms for optimal mediator design.

Resilience against group deviations demonstrated.

Enhanced welfare in multi-sender aggregation settings.

Abstract

In an information aggregation game, a set of senders interact with a receiver through a mediator. Each sender observes the state of the world and communicates a message to the mediator, who recommends an action to the receiver based on the messages received. The payoff of the senders and of the receiver depend on both the state of the world and the action selected by the receiver. This setting extends the celebrated cheap talk model in two aspects: there are many senders (as opposed to just one) and there is a mediator. From a practical perspective, this setting captures platforms in which strategic experts advice is aggregated in service of action recommendations to the user. We aim at finding an optimal mediator/platform that maximizes the users' welfare given highly resilient incentive compatibility requirements on the equilibrium selected: we want the platform to be incentive compatible for the receiver/user when selecting the recommended action, and we want it to be resilient against group deviations by the senders/experts. We provide highly positive answers to this challenge, manifested through efficient algorithms.