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.

翻译：在信息聚合博弈中，一组发送者通过中介与接收者进行交互。每位发送者观察世界状态并向中介传达一条消息，中介根据收到的消息向接收者推荐一个行动。发送者与接收者的收益既取决于世界状态，也取决于接收者所选择的行动。该设定在两个方面扩展了著名的廉价谈话模型：存在多位发送者（而非仅一位）以及一个中介。从实践角度来看，该设定捕捉了平台场景——其中战略专家建议被聚合，以服务于向用户推荐行动。我们旨在寻找一个最优中介/平台，在所选均衡上满足高度弹性的激励相容要求时最大化用户福利：我们希望该平台对接收者/用户在选择推荐行动时具有激励相容性，并且希望其能够抵御发送者/专家的群体偏离。关于这一挑战，我们通过高效算法给出了高度正向的解答。