Monotone Persuasion
Monotone Persuasion
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单调的说服
DOI：
10.2139/ssrn.3265980
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
Jeffrey Mensch
中科院分区：
文献类型：
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作者：
Jeffrey Mensch
我们探索贝叶斯说服环境，其中状态和动作空间是有序的，允许动作和类型之间的互补性。基于单调比较静态的文献，我们确定了保证这些在所有（可能非单调）信号结构中最优的条件。当动作空间是二元时，发送者和接收者偏好的超模块化足以使最佳信号具有单调结构。随着一系列的行动，情况变得更加有趣。我们使用发送者收益的虚拟效用表示来确定一种新颖的单交叉条件。我们还提供了一种计算最佳单调信号结构的技术，即使这种单调性是由于外生约束造成的。应用程序涉及带有偏差、容量限制和信用评级的二次损失函数。
We explore Bayesian persuasion environments in which the state and action spaces are ordered, allowing for complementarity between actions and types. Building on the literature on monotone comparative statics, we identify conditions that guarantee that these are optimal among all (possibly non-monotone) signal structures. When the action space is binary, supermodularity of the sender's and receiver's preferences suffices for the optimal signal to have a monotone structure. With a continuum of actions, the conditions are more intriguing. We identify a novel single-crossing condition using a virtual utility representation of the sender's payoff. We also provide a technique to compute the optimal monotone signal structure, even when this monotonicity is due to an exogenous constraint. Applications are given to quadratic loss functions with biases, capacity constraints, and credit ratings.