Experimental design to persuade

Kolotilin, Anton

doi:10.1016/j.geb.2015.02.006

Cited by 50 publications

(12 citation statements)

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“…This is also the sense in which we use the term. Such scenarios are increasingly common in the information economy, and it is therefore unsurprising that persuasion has been the subject of a large body of work in recent years, motivated by domains as varied as auctions [9,25,24,10], advertising [3,33,17], voting [2], security [46,42], multiarmed bandits [37,38], medical research [35], and financial regulation [28,29]. (For an empirical survey of persuasion, we refer the reader to [21]).…”

Section: "One Quarter Of the Gdp Is Persuasion"mentioning

confidence: 99%

Algorithmic Bayesian persuasion

Dughmi

2016

Proceedings of the Forty-Eighth Annual ACM Symposium on Theory of Computing

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Persuasion, defined as the act of exploiting an informational advantage in order to effect the decisions of others, is ubiquitous. Indeed, persuasive communication has been estimated to account for almost a third of all economic activity in the US. This paper examines persuasion through a computational lens, focusing on what is perhaps the most basic and fundamental model in this space: the celebrated Bayesian persuasion model of Kamenica and Gentzkow [34]. Here there are two players, a sender and a receiver. The receiver must take one of a number of actions with a-priori unknown payoff, and the sender has access to additional information regarding the payoffs of the various actions for both players. The sender can commit to revealing a noisy signal regarding the realization of the payoffs of various actions, and would like to do so as to maximize her own payoff in expectation assuming that the receiver rationally acts to maximize his own payoff. When the payoffs of various actions follow a joint distribution (the common prior), the sender's problem is nontrivial, and its computational complexity depends on the representation of this prior.We examine the sender's optimization task in three of the most natural input models for this problem, and essentially pin down its computational complexity in each. When the payoff distributions of the different actions are i.i.d. and given explicitly, we exhibit a polynomial-time (exact) algorithmic solution, and a "simple" (1 − 1/e)-approximation algorithm. Our optimal scheme for the i.i.d. setting involves an analogy to auction theory, and makes use of Border's characterization of the space of reduced-forms for single-item auctions. When action payoffs are independent but non-identical with marginal distributions given explicitly, we show that it is #P-hard to compute the optimal expected sender utility. In doing so, we rule out a generalized Border's theorem, as defined by Gopalan et al [30], for this setting. Finally, we consider a general (possibly correlated) joint distribution of action payoffs presented by a black box sampling oracle, and exhibit a fully polynomial-time approximation scheme (FPTAS) with a bi-criteria guarantee. Our FPTAS is based on Monte-Carlo sampling, and its analysis relies on the principle of deferred decisions. Moreover, we show that this result is the best possible in the black-box model for information-theoretic reasons.

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Section: "One Quarter Of the Gdp Is Persuasion"mentioning

confidence: 99%

Algorithmic Bayesian persuasion

Dughmi

2016

Proceedings of the Forty-Eighth Annual ACM Symposium on Theory of Computing

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“…Kolotilin (2014Kolotilin ( , 2015 studies optimal persuasion mechanisms to a privately informed receiver. In a somewhat different setting, Ivanov (2010) studies the benefit to a principal of limiting the information available to a privately informed agent when they both engage in strategic communication (i.e., cheap talk).…”

Section: Introductionmentioning

confidence: 99%

Bayesian persuasion with heterogeneous priors

Alonso

Câmara

2016

Journal of Economic Theory

119

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In a world in which rational individuals may hold different prior beliefs, a sender can influence the behavior of a receiver by controlling the informativeness of an experiment (public signal). We characterize the set of distributions of posterior beliefs that can be induced by an experiment, and provide necessary and sufficient conditions for a sender to benefit from persuasion. We then provide sufficient conditions for the sender to benefit from persuasion for almost every pair of prior beliefs, even when there is no value of persuasion under a common prior. Our main condition is that the receiver's action depends on his beliefs only through his expectation of some random variable.JEL classification: D72, D83, M31.

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“…In our setting, their assumptions about the receiver would imply that either full or no disclosure is optimal. Kolotilin (2016) allows for nonlinear sender's and receiver's utility functions and an arbitrary joint distribution of the state and the receiver's type. The linearprogramming approach in Kolotilin (2016) permits to verify whether a candidate experiment is optimal.…”

mentioning

confidence: 99%

“…Kolotilin (2016) allows for nonlinear sender's and receiver's utility functions and an arbitrary joint distribution of the state and the receiver's type. The linearprogramming approach in Kolotilin (2016) permits to verify whether a candidate experiment is optimal. But this approach has limited applicability because it does not allow to directly characterize the optimal experiments.…”

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confidence: 99%

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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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Experimental design to persuade

Cited by 50 publications

References 54 publications

Algorithmic Bayesian persuasion

Algorithmic Bayesian persuasion

Bayesian persuasion with heterogeneous priors

Persuasion of a Privately Informed Receiver

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