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Long Bayesian Persuasion

Authors: Daehong Min; Wenhao Wu;

Long Bayesian Persuasion

Abstract

This paper extends the sequential Bayesian persuasion model (Li and Norman, 2021; Wu, 2023) to infinite-period games, where a group of senders take unlimited opportunities one-by-one to reveal information to one decision maker. We first solve for the Perfect Bayesian Equilibrium outcomes through a geometric approach that features a simple better-than-full-revelation principle. Next, we seek for a refinement of PBE outcomes by proposing a so-called common-knowledge-of-common-ground&nbsp;assumption, which relies on senders’ recursive induction through higher-order belief hierarchy.&nbsp;<span>The justification of this notion is provided by showing the monotonicity of a&nbsp;</span><span>sequence of sets of beliefs which are used to characterize the reasonable outcomes&nbsp;</span><span>of the persuasion process. Under this refinement, we find a counter-intuitive result&nbsp;</span><span>that when the conflicts between two senders become more intense, the equilibrium&nbsp;</span><span>outcome can be less informative in such an infinite-period game. Finally, we make&nbsp;</span><span>comparison with finite-period persuasion games, and find that it is possible that, once&nbsp;</span><span>removing the ending period, senders achieve Pareto improvement.</span>

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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