论文标题
多维努力的最佳得分规则
Optimal Scoring Rules for Multi-dimensional Effort
论文作者
论文摘要
本文为设计规则的设计开发了一个框架，以最佳地激励代理商发挥多维努力。该框架是对古典背包问题的战略代理的概括（参见Briest，Krysta和Vöcking，2005年，Singer，2010年），将算法机制设计应用于教室是基本的。该论文确定了两个简单的评分规则家庭，以保证与最佳评分规则的持续近似。截断的单独评分规则是单维评分规则的总和，该规则被截断为可行分数的有限范围。如果报告超过阈值，则阈值评分规则将提供最大分数，否则为零。其中一个规则中的一个或另一个规则的大致最优性类似于Babaioff，Immorlica，Lucier和Weinberg（2014）的捆绑或销售。最后，我们表明，当代理商选择的努力选择时，这两个简单的评分规则中最好的最佳性能是可靠的。
This paper develops a framework for the design of scoring rules to optimally incentivize an agent to exert a multi-dimensional effort. This framework is a generalization to strategic agents of the classical knapsack problem (cf. Briest, Krysta, and Vöcking, 2005, Singer, 2010) and it is foundational to applying algorithmic mechanism design to the classroom. The paper identifies two simple families of scoring rules that guarantee constant approximations to the optimal scoring rule. The truncated separate scoring rule is the sum of single dimensional scoring rules that is truncated to the bounded range of feasible scores. The threshold scoring rule gives the maximum score if reports exceed a threshold and zero otherwise. Approximate optimality of one or the other of these rules is similar to the bundling or selling separately result of Babaioff, Immorlica, Lucier, and Weinberg (2014). Finally, we show that the approximate optimality of the best of those two simple scoring rules is robust when the agent's choice of effort is made sequentially.
