A major challenge in obtaining evaluations of products or services on e-commerce platforms is eliciting informative responses in the absence of verifiability. This paper proposes the Square Root Agreement Rule (SRA): a simple reward mechanism that incentivizes truthful responses to objective evaluations on such platforms. In this mechanism, an agent gets a reward for an evaluation only if her answer matches that of her peer, where this reward is inversely proportional to a popularity index of the answer. This index is defined to be the square root of the empirical frequency at which any two agents performing the same evaluation agree on the particular answer across evaluations of similar entities operating on the platform. Rarely agreed-upon answers thus earn a higher reward than answers for which agreements are relatively more common.
We show that in the many tasks regime, the truthful equilibrium under SRA is strictly payoff-dominant across large classes of natural equilibria that could arise in these settings, thus increasing the likelihood of its adoption. While there exist other mechanisms achieving such guarantees, they either impose additional assumptions on the response distribution that are not generally satisfied for objective evaluations or they incentivize truthful behavior only if each agent performs a prohibitively large number of evaluations and commits to using the same strategy for each evaluation. SRA is the first known incentive mechanism satisfying such guarantees without imposing any such requirements. Moreover, our empirical findings demonstrate the robustness of the incentive properties of SRA in the presence of mild subjectivity or observational biases in the responses. These properties make SRA uniquely attractive for administering reward-based incentive schemes (e.g., rebates, discounts, reputation scores, etc.) on online platforms.
Online Truthful Mechanisms for Multi-sided Markets
多边市场机制的研究越来越受到研究界的关注，并受到网络和电子商务中此类市场的众多例子的推动。这些例子中的许多代表了动态和不确定的环境，因此，实际上需要在线机制。不幸的是，据我们所知，以前没有任何一个多边市场（甚至是双边市场）的在线机制能够（近似）最大化贸易收益，同时保证理想的经济属性，如激励诚实，自愿参与和避免预算赤字。在这项工作中，我们提出了第一个在线机制的多边市场具有上述属性。我们的机制是为[Feldman and Gonen（2016）]建议的市场环境而设计的;这是由可预见的未来在线广告形式所推动的。我们设定的在线性质促使我们定义了一个更强的个体理性概念，称为“持续的个体理性”，捕捉了玩家永远不会因为参与机制或过早离开而失败的自然要求。满足连续的个人理性的要求，以及我们的机制保证的其他经济属性，需要该机制使用一种新的定价方案，在该机制的执行过程中，用户可以获得持续的增量支付到一个预先知道的最大值。由于用户在现实中很少得到报酬，这种定价方案对于机制设计来说是新的。然而，它所基于的原则可以在许多常见的现实生活场景中观察到，例如高管薪酬支付和公司收购交易。我们相信，我们新的动态定价方案概念和我们强化的个人理性概念都具有独立的利益。
Non-Monetary Mechanism Design without Priors: Achieving Efficiency via Adaptive Costly Audits
Massachusetts Institute of Technology, Operations Research Center（麻省理工学院，运筹学中心）；Georgia Tech, School of Industrial and Systems Engineering（佐治亚理工学院，工业与系统工程学院）；Massachusetts Institute of Technology, Department of EECS（麻省理工学院，电气与计算机工程系）
