标题： 说服与匹配：最优生产运输 显示英文标题

标题： Persuasion and Matching: Optimal Productive Transport

摘要： 我们考虑一般的贝叶斯劝导问题，其中接收者的效用在单维行动中是单峰的。 我们证明，在每次实现中最多将两个状态合并的信号总是最优的，并且在非奇异条件（扭曲条件）下，这样的成对信号是唯一的解。 我们的核心结果提供了风险更大的前景会引发更高或更低行动的条件，因此在每个嵌套前景集上，所引起的行动是单谷的或单峰的。 我们还提供了完全披露或负向异质性披露最优性的条件，其中所有前景都是嵌套的。 在方法论上，我们的结果依赖于新颖的对偶性和互补松弛定理。 我们的分析扩展到一个将一维输入分配给生产单位的一般问题，我们称之为最优生产运输。 这个问题包括额外的应用，包括俱乐部经济（将工人分配给公司，或学生分配给学校）、稳健期权定价（将未来资产价格分配给价格分布）以及党派选区划分（将选民分配给选区）。 显示英文摘要

摘要： We consider general Bayesian persuasion problems where the receiver's utility is single-peaked in a one-dimensional action. We show that a signal that pools at most two states in each realization is always optimal, and that such pairwise signals are the only solutions under a non-singularity condition (the twist condition). Our core results provide conditions under which riskier prospects induce higher or lower actions, so that the induced action is single-dipped or single-peaked on each set of nested prospects. We also provide conditions for the optimality of either full disclosure or negative assortative disclosure, where all prospects are nested. Methodologically, our results rely on novel duality and complementary slackness theorems. Our analysis extends to a general problem of assigning one-dimensional inputs to productive units, which we call optimal productive transport. This problem covers additional applications including club economies (assigning workers to firms, or students to schools), robust option pricing (assigning future asset prices to price distributions), and partisan gerrymandering (assigning voters to districts).