Cooperation in partly observable networked markets

Fainmesser, Itay P.; Goldberg, David A.

doi:10.1016/j.geb.2017.11.008

Cited by 10 publications

(2 citation statements)

References 54 publications

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“…Moreover, it is common knowledge that each agent holds this belief. These types of beliefs are consistent with Fainmesser and Goldberg (). They show in a random network in which the number of each agent's neighbors is bounded that as the population becomes large, each agent believes asymptotically that the network is a random tree in which she is the root agent.…”

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confidence: 78%

Locally Bayesian learning in networks

Tan

2020

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Agents in a network want to learn the true state of the world from their own signals and their neighbors' reports. Agents know only their local networks, consisting of their neighbors and the links among them. Every agent is Bayesian with the (possibly misspecified) prior belief that her local network is the entire network. We present a tractable learning rule to implement such locally Bayesian learning: each agent extracts new information using the full history of observed reports in her local network. Despite their limited network knowledge, agents learn correctly when the network is a social quilt, a tree‐like union of cliques. But they fail to learn when a network contains interlinked circles (echo chambers), despite an arbitrarily large number of correct signals.

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confidence: 78%

Locally Bayesian learning in networks

Tan

2020

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“…Various results on the generation of large graphs with given degree distributions using the configuration model are available in the Mathematics and Computer Science literatures (see for example, Greenhill et al 2006). The formal statements can also be found in Fainmesser and Goldberg (2018), Lemmas 6 and 7. have also adopted X, is…”

Section: Modelmentioning

confidence: 99%

Pricing Network Effects: Competition

Fainmesser¹,

Galeotti

2020

American Economic Journal: Microeconomics

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We study the practice of influencer marketing in oligopoly markets and its effect on market efficiency. In our model, each consumer is influenced by choices of a subset of other consumers. Firms gather information on consumers’ influence and price discriminate using this information. In equilibrium, firms charge premia/subsidize below-/ above-average-influential consumers; the premia/discounts depend on the strength of network effects and on how much information firms have on consumers’ influence. Influencer marketing leads to inefficient consumer-product matches. Firms’ investments in information are strategic complements, leading to a race for information acquisition that erodes welfare and firms’ profits but increases consumer surplus. (JEL D11, D21, D43, D83, D85, L13, M31)

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Social Networks from a Designer’s Viewpoint

Vega‐Redondo

2019

Studies in Economic Design

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Cooperation in partly observable networked markets

Cited by 10 publications

References 54 publications

Locally Bayesian learning in networks

Locally Bayesian learning in networks

Pricing Network Effects: Competition

Social Networks from a Designer’s Viewpoint

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