We study the design of effort-maximizing grading schemes between agents with private abilities. Assuming agents derive value from the information their grade reveals about their ability, we find that more informative grading schemes induce more competitive contests. In the contest framework, we investigate the effect of manipulating individual prizes and increasing competition on expected effort, identifying conditions on ability distributions and cost functions under which these transformations may encourage or discourage effort. Our results suggest that more informative grading schemes encourage effort when agents of moderate ability are highly likely, and discourage effort when such agents are unlikely.

翻译：我们研究了具有私有能力的智能体之间努力最大化的分级方案设计。假设智能体从其成绩所揭示的能力信息中获取价值，我们发现更具信息量的分级方案会引发更激烈的竞争。在竞赛框架下，我们探究了操纵个体奖励与增强竞争对期望努力的影响，识别了能力分布与成本函数在何种条件下这些转变可能激励或抑制努力。我们的结果表明，当中等能力智能体出现概率较高时，更具信息量的分级方案会促进努力；而当此类智能体出现概率较低时，则会抑制努力。