AI 中文总结
该研究针对全支付竞赛的最优分级提出统一方法，涵盖两种信息机制，通过熨烫相关系数或虚拟能力确定等级，实现最大化期望总努力的目标。
AI 中文摘要
我们针对全支付竞赛中的最优分级开发了一种统一方法，其中设计者分配固定的异质性奖品向量以最大化期望总努力。该方法涵盖两种信息机制，并确定了共同原则：局部错序的激励收益，以及在所得等级间按分类分配奖品。在仅排名分级下，分配仅取决于序数排名；通过最小凹上确界或相邻违规者池算法对累积秩系数进行熨烫，确定哪些相邻秩被合并，以及哪些奖品在每个等级内随机化。在绩效关联分级下，分配可取决于数值努力；最优方案熨烫虚拟能力，形成内生类型等级，并在等级间按分类分配奖品块；最低及格努力以下的不及格等级，以及一系列努力区间，实现直接最优，同时保留全部奖品分配。
英文摘要
We develop a unified approach to optimal grading in an all-pay contest in which a designer assigns a fixed vector of heterogeneous prizes to maximize expected total effort. The approach covers two information regimes and identifies a common principle: iron locally misordered incentive returns and assign prizes assortatively across the resulting grades. Under rank-only grading, assignments depend only on ordinal ranks. Ironing cumulative rank coefficients---via the least concave majorant or the pool-adjacent-violators algorithm---determines which adjacent ranks are pooled and which prizes are randomized within each grade. Under performance-contingent grading, assignments may depend on numerical effort. The optimum irons virtual ability, forms endogenous type grades, and assigns prize blocks assortatively across grades. A failing grade below a minimum passing effort and a collection of effort brackets implement the direct optimum while preserving full prize assignment.
