
arXiv: 1711.04474
We consider a Bayesian persuasion problem where the persuader and the decision maker communicate through an imperfect channel that has a fixed and limited number of messages and is subject to exogenous noise. We provide an upper bound on the payoffs the persuader can secure by communicating through the channel. We also show that the bound is tight, i.e., if the persuasion problem consists of a large number of independent copies of the same base problem, then the persuader can achieve this bound arbitrarily closely by using strategies that tie all the problems together. We characterize this optimal payoff as a function of the information-theoretic capacity of the communication channel.
Comment: Journal of Economic Theory
Computer Science - Information Theory, communication channel, Bayesian persuasion, Decision theory, [SHS.INFO] Humanities and Social Sciences/Library and information sciences, [INFO.INFO-GT] Computer Science [cs]/Computer Science and Game Theory [cs.GT], [INFO.INFO-IT] Computer Science [cs]/Information Theory [cs.IT], mutual information, Mathematics - Optimization and Control, Mathematics - Probability
Computer Science - Information Theory, communication channel, Bayesian persuasion, Decision theory, [SHS.INFO] Humanities and Social Sciences/Library and information sciences, [INFO.INFO-GT] Computer Science [cs]/Computer Science and Game Theory [cs.GT], [INFO.INFO-IT] Computer Science [cs]/Information Theory [cs.IT], mutual information, Mathematics - Optimization and Control, Mathematics - Probability
| 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). | 62 | |
| 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. | Top 1% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
