Bayesian Persuasion with Costly Information Acquisition

ERN: Search Pub Date : 2018-03-01 DOI:10.2139/ssrn.3161174
Ludmila Matysková
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引用次数: 38

Abstract

A sender who chooses a signal to reveal to a receiver can often influence the receiver’s subsequent actions. Is persuasion more difficult when the receiver has her own sources of information? Does the receiver benefit from having additional information sources? We consider a Bayesian persuasion model extended to a receiver’s endogenous acquisition of information under an entropy-based cost commonly used in rational inattention. A sender’s optimal signal can be computed from standard Bayesian persuasion subject to an additional constraint: the receiver never gathers her own costly information. We further determine a finite set of the sender’s signals satisfying the additional constraint in which some optimal signal must be contained. The set is characterized by linear conditions using the receiver’s utility and information cost parameters. The new method is also applicable to a standard Bayesian persuasion model and can simplify, sometimes dramatically, the search for a sender’s optimal signal (as opposed to a standard concavification technique used to solve these models). We show that the ‘threat’ of additional learning weakly decreases the sender’s expected equilibrium payoff. However, the outcome can be worse not only for the sender, but also for the receiver.
代价信息获取的贝叶斯说服
发送方选择向接收方透露的信号通常会影响接收方的后续行动。当接收者有自己的信息来源时,说服是否更困难?接收方是否从额外的信息源中受益?我们考虑将贝叶斯说服模型扩展到接收者在基于熵的成本下获得信息的内生获取,这种成本通常用于理性不注意。发送者的最优信号可以从标准贝叶斯说服中计算出来,但要加上一个额外的约束:接收者从不收集自己昂贵的信息。我们进一步确定了满足附加约束的有限发送者信号集,其中必须包含一些最优信号。该集合具有使用接收者效用和信息成本参数的线性条件的特征。新方法也适用于标准贝叶斯说服模型,并且可以简化(有时显着)搜索发送者的最佳信号(与用于解决这些模型的标准凹化技术相反)。我们表明,额外学习的“威胁”会微弱地降低发送者的预期均衡收益。然而,结果可能更糟,不仅对发送者,而且对接收者。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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