同行评议中的激励设计：评级与重复内生匹配
Incentive Design in Peer Review: Rating and Repeated Endogenous Matching
摘要：
同行审查（例如，大规模开放在线课程（MOOC）中的评分作业，学术论文评论）是评估产品的有效且可扩展的方法（例如，任务，论文）的大量代理人时，专门审查专家（例如，教师、编辑人员有限。同行评审提出了两个关键挑战：1）确定评审员的内在能力（即，逆向选择）和2）激励评审者付出很大努力（即，道德风险）。机制设计中的一些工作采用一次性匹配规则解决纯逆向选择问题，而纯道德风险问题则是采用外部给定的固定匹配规则解决重复博弈问题。然而，在同时表现出逆向选择和道德风险的同行评审系统中，一次性或外源性匹配规则不会将代理人的当前行为与未来匹配和未来收益联系起来，并且正如我们所证明的那样，会导致近视行为（即，付出最少的努力）导致最低的审查质量。在本文中，我们首次提出了一个解决方案，同时解决逆向选择和道德风险。我们的解决方案利用代理人的重复交互，利用评级总结代理人过去的审查质量，并设计匹配规则，内生地依赖于代理人的评级。我们提出的匹配规则很容易实现，不需要知道代理的私人信息（例如，的效益和成本函数）。然而，它们有效地引导系统达到一种平衡，在这种平衡中，代理人被激励去付出更大的努力，并获得准确反映其评论质量的评级。使用几个说明性的例子，我们量化了显着的性能增益，我们提出的机制相比，现有的一杆或外源匹配规则。
英文摘要：
Peer review (e.g., grading assignments in Massive Open Online Courses (MOOCs), academic paper review) is an effective and scalable method to evaluate the products (e.g., assignments, papers) of a large number of agents when the number of dedicated reviewing experts (e.g., teaching assistants, editors) is limited. Peer review poses two key challenges: 1) identifying the reviewers' intrinsic capabilities (i.e., adverse selection) and 2) incentivizing the reviewers to exert high effort (i.e., moral hazard). Some works in mechanism design address pure adverse selection using one-shot matching rules, and pure moral hazard was addressed in repeated games with exogenously given and fixed matching rules. However, in peer review systems exhibiting both adverse selection and moral hazard, one-shot or exogenous matching rules do not link agents' current behavior with future matches and future payoffs, and as we prove, will induce myopic behavior (i.e., exerting the lowest effort) resulting in the lowest review quality. In this paper, we propose for the first time a solution that simultaneously solves adverse selection and moral hazard. Our solution exploits the repeated interactions of agents, utilizes ratings to summarize agents' past review quality, and designs matching rules that endogenously depend on agents' ratings. Our proposed matching rules are easy to implement and require no knowledge about agents' private information (e.g., their benefit and cost functions). Yet, they are effective in guiding the system to an equilibrium where the agents are incentivized to exert high effort and receive ratings that precisely reflect their review quality. Using several illustrative examples, we quantify the significant performance gains obtained by our proposed mechanism as compared to existing one-shot or exogenous matching rules.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
