Selling Cookies

Bergemann, Dirk; Bonatti, Alessandro

doi:10.1257/mic.20140155

Cited by 97 publications

(22 citation statements)

References 30 publications

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“…Various recent models on selling information in [6,7,11] are quite similar to Bayesian persuasion, with the main difference being that the sender's utility function is replaced with revenue. Whereas Babaioff et al [6] consider the algorithmic question of selling information when states of nature are explicitly given as input, the analogous algorithmic questions to ours have not been considered in their model.…”

Section: Additional Discussion Of Related Workmentioning

confidence: 99%

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Algorithmic Bayesian Persuasion

Dughmi¹,

Xu²

2019

SIAM J. Comput.

127

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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.A somewhat less artificial example of persuasion is in the context of providing financial advice. Here, the receiver is an investor, actions correspond to stocks, and the sender is a stockbroker or financial adviser with access to stock return projections which are a-priori unknown to the investor. When the adviser's commission or return is not aligned with the investor's returns, this is a nontrivial Bayesian persuasion problem. In fact, interesting examples exist when stock returns are independent from each other, or even i.i.d. Consider the following simple example which fits into the i.i.d. model considered in Section 3: the...

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Section: Additional Discussion Of Related Workmentioning

confidence: 99%

“…In particular, denote the bit complexity of x by ℓ. Since a i , b i are computed by a two-variable two-equation linear system involving x i (Equations (7) and (8)), they each have O(ℓ) bit complexity. Consequently, all the explicitly described facets of P 2 have O(ℓ) bit complexity.…”

Section: Reductionmentioning

confidence: 99%

Algorithmic Bayesian Persuasion

Dughmi¹,

Xu²

2019

SIAM J. Comput.

127

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“…Moreover, third-degree price discrimination can itself temper competition enough so that firms find it profitable to discriminate all consumer types, making them worse off (Armstrong & Zhou, 2010). An interesting analysis has been recently carried out by Bergemann et al (2015), who focus on third-degree price discrimination in a single-product monopoly setting. In particular, they analyse the welfare effects of market segmentation and demonstrate that market segmentation can achieve any combination of consumer surplus and producer surplus as long as (i) consumer surplus is nonnegative, (ii) producer surplus is greater or equal than his surplus under no segmentation and (iii) total surplus does not exceed the total value that consumers receive from the good.…”

Section: Price Discriminationmentioning

confidence: 99%

“…A more recent strand of literature has instead focused on consumers' information disclosure when data improves matching and allows the firm to operate price discrimination. This literature adopts a setup similar to Bergemann et al (2015) but allows consumers to disclose data instead of exogenously giving it to the firm. On the one hand, consumers are incentivised to disclose information to allow the firm to offer them a more valuable good.…”

Section: Behaviour-based Price Discriminationmentioning

confidence: 99%

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The microeconomics of data – a survey

Pino

2022

J. Ind. Bus. Econ.

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In recent years, academia, institutions, and policymakers have been focusing their attention on the impact of data in digital markets. The economic literature that explicitly models data and their collection as strategic variables is growing, but most studies focus on distinct settings with specific data uses. This survey aims to organise this literature to extract general insights that hold across different models and assumptions. To do so, I identify three classes of models according to the way they model data collection. I find that each class is characterised by a specific impact of data on the market outcomes, regardless of the specific data use. First, when firms obtain data without strategic interactions, their use has a pro-competitive effect on the market. However, firms fail to fully internalise the data externalities, leading to data overuse and, in turn, privacy concerns. Second, when firms collect data from their interaction with consumers, data can facilitate market tipping, especially if firms are asymmetric in their starting positions. Third, when firms acquire data from data intermediaries, data are strategically sold to temper competition in the downstream market, allowing intermediaries to extract most of the surplus at the expense of firms and consumers. These general insights can facilitate future research and help policymakers to have a more general understanding of the competitive effects of data, depending on the situation at hand.

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Optimal targeting with entry

Liu

2017

Manage Decis Econ

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We study an advertising agency's optimal choice of targeting technology with endogenous market structure, namely, when targeting changes firms' entry strategies into the advertising and product market. We show that the advertising agency faces a trade-off between demand-expansion and profit-dissipation: The former arises as targeting induces more entry and increases the demand for advertising; the latter refers to that targeting relaxes competition by inducing more differentiation. We show that perfect targeting is not optimal for the advertising agency. Compared to social optimum, the advertising agency underinvests in targeting when investment cost is low and overinvests when targeting is costly. JEL Codes: L1; L8; M3286 LIU These two observations determine the incentive to invest in targeting for the advertising agency. Specifically, a better targeting technology affects the profit of the advertising agency in three ways. First, it induces more entry (the entrant is more likely to enter) and thus increases the profit, which is a demand-expansion effect. Second, it induces more entry with niche product, when the entrant could have entered with a general product. This is a competition-relaxing effect: LIU David Zvilichovsky, and participants at seminars and conferences in Toulouse, Liège, Mannheim, Würzburg, Paris, and Paderborn for useful comments. All remaining errors are mine. ORCID Xingyi Liu

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Selling Cookies

Cited by 97 publications

References 30 publications

Algorithmic Bayesian Persuasion

Algorithmic Bayesian Persuasion

The microeconomics of data – a survey

Optimal targeting with entry

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