Motivational Ratings
研究了在标准职业关注框架下，最优（努力最大化）评级的设计，证明该评级是过去观测的线性函数，但不是马尔可夫过程，而是两个马尔可夫过程之和，并展示了如何组合不同类型和年份的信息。
Abstract Performance evaluation (“rating”) systems not only provide information to users but also motivate the rated worker. This article solves for the optimal (effort-maximizing) rating within the standard career concerns framework. We prove that this rating is a linear function of past observations. The rating, however, is not a Markov process, but rather the sum of two Markov processes. We show how it combines information of different types and vintages. An increase in effort may adversely affect some (but not all) future ratings.