Bayesian persuasion with heterogeneous priors

Alonso, Ricardo; Câmara, Odilon

doi:10.1016/j.jet.2016.07.006

Cited by 119 publications

(87 citation statements)

References 46 publications

(66 reference statements)

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“…Gentzkow and Kamenica (2012) allow many senders to disclose information. Alonso andCâmara (2014a, 2014b) allow heterogeneous priors and heterogeneous receivers, respectively. Gentzkow and Kamenica (2014a) allow mechanisms to have different costs to the sender.…”

Section: Related Literaturementioning

confidence: 99%

Experimental design to persuade

Kolotilin

2015

Games and Economic Behavior

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A sender chooses ex ante how information will be disclosed ex post. A receiver obtains public information and information disclosed by the sender. Then he takes one of two actions. The sender wishes to maximize the probability that the receiver takes the desired action. The sender optimally discloses only whether the receiver's utility is above a cutoff. I derive necessary and sufficient conditions for the sender's and receiver's welfare to be monotonic in information. In particular, the sender's welfare increases with the precision of the sender's information and decreases with the precision of public information.

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Section: Related Literaturementioning

confidence: 99%

Experimental design to persuade

Kolotilin

2015

Games and Economic Behavior

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“…This strong coupling can be seen by taking a closer look at the expectations appearing in the obedience constraints (7)- (8). According to (2), the detector's expected continuation cost from time t onward when she has received messages m 1:t and decides to declare the jump at time t is…”

Section: Features Of the Problemmentioning

confidence: 99%

Dynamic Information Design: A Simple Problem on Optimal Sequential Information Disclosure

Farhadi

Teneketzis

2020

SSRN Journal

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We study a dynamic information design problem in a finite-horizon setting consisting of two strategic and long-term optimizing agents, namely a principal (he) and a detector (she). The principal observes the evolution of a Markov chain that has two states, one "good" and one "bad" absorbing state, and has to decide how to sequentially disclose information to the detector. The detector's only information consists of the messages she receives from the principal. The detector's objective is to detect as accurately as possible the time of the jump from the good to the bad state. The principal's objective is to delay the detector as much as possible from detective the jump to the bad state. For this setting, we determine the optimal strategies of the principal and the detector. The detector's optimal strategy is described by time-varying thresholds on her posterior belief of the good state. We prove that it is optimal for the principal to give no information to the detector before a time threshold, run a mixed strategy to confuse the detector at the threshold time, and reveal the true state afterwards. We present an algorithm that determines both the optimal time threshold and the optimal mixed strategy that could be employed by the principal. We show, through numerical experiments, that this optimal sequential mechanism significantly outperforms any other information disclosure strategy presented in literature.or subsidies) so as to align their strategic objectives (e.g. maximization of a strategic agent's utility) with his own objective (e.g. maximization of social welfare). In this situation, the system's information structure (who knows what and when) is fixed, and the coordinator's goal is to design a mechanism that incentivizes the strategic agents to reveal their information truthfully [48].2. Information Design. In this approach, the coordinator (e.g. a central control center in a metropolitan area) knows perfectly the evolution of the system's state (e.g. the evolution of traffic in the transportation network), but the decision making (e.g. route selection) is done by the strategic agents (e.g. drivers) who have incomplete/imperfect knowledge of the state (e.g. drivers only know the traffic in their immediate vicinity). To incentivize strategic agents to take actions that are desirable for the coordinator, he can provide, sequentially over time, information about the system's state to them (e.g. provide traffic updates to selfish drivers). The goal of information provision/disclosure is the alignment of each agent's objective (e.g. minimization of the average time to reach the destination) with the coordinator's objective (e.g. minimization of the traffic in the roads). In this situation, the game-form/mechanism is fixed but the system's information structure is not fixed. It has to be designed by the coordinator through the sequential disclosure/provision of information to the strategic agents so as to serve his goal [17,59].This paper addresses an information design problem. Information design problems are ...

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“…Bayesian persuasion with a privately informed sender is considered in Gill andSgroi (2008, 2012), Perez-Richet (2014), Alonso andCâmara (2016b), andHedlund (2017). Bayesian persuasion with multiple senders is analyzed in Ostrovsky and Schwarz (2010), Board and Lu (2016), Kamenica (2017a, 2017b), and Li and Norman (2017).…”

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confidence: 99%

“…Dynamic Bayesian persuasion is examined in Au (2015), Ely et al (2015), and Ely (2017). Information acquisition and the value of information in Bayesian persuasion are explored in Kamenica (2014, 2017c), Kolotilin (2015), Alonso and Câmara (2016a), and .…”

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Persuasion of a Privately Informed Receiver

et al. 2016

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Abstract. We study persuasion mechanisms in linear environments. A receiver has a private type and chooses between two actions. A sender designs a persuasion mechanism or an experiment to disclose information about a payoff-relevant state. A persuasion mechanism conditions information disclosure on the receiver's report about his type, whereas an experiment discloses information independent of the receiver's type. We establish the equivalence of implementation by persuasion mechanisms and by experiments, and characterize optimal persuasion mechanisms.JEL Classification: D82, D83, L82

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Bayesian persuasion with heterogeneous priors

Cited by 119 publications

References 46 publications

Experimental design to persuade

Experimental design to persuade

Dynamic Information Design: A Simple Problem on Optimal Sequential Information Disclosure

Persuasion of a Privately Informed Receiver

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