We consider schemes for obtaining truthful reports on a common but hidden
signal from large groups of rational, self-interested agents. One example are
online feedback mechanisms, where users provide observations about the quality
of a product or service so that other users can have an accurate idea of what
quality they can expect. However, (i) providing such feedback is costly, and
(ii) there are many motivations for providing incorrect feedback.
Both problems can be addressed by reward schemes which (i) cover the cost of
obtaining and reporting feedback, and (ii) maximize the expected reward of a
rational agent who reports truthfully. We address the design of such
incentive-compatible rewards for feedback generated in environments with pure
adverse selection. Here, the correlation between the true knowledge of an agent
and her beliefs regarding the likelihoods of reports of other agents can be
exploited to make honest reporting a Nash equilibrium.
In this paper we extend existing methods for designing incentive-compatible
rewards by also considering collusion. We analyze different scenarios, where,
for example, some or all of the agents collude. For each scenario we
investigate whether a collusion-resistant, incentive-compatible reward scheme
exists, and use automated mechanism design to specify an algorithm for deriving
an efficient reward mechanism.

本论文考虑如何设计一种激励机制，以在固有的逆向选择环境下，利用纯粹的协作或对抗以及报告代价获得真实从众意见汇报。