Informational externalities and emergence of consensus

Rosenberg, Dinah; Solan, Eilon; Vieille, Nicolas

doi:10.1016/j.geb.2008.09.027

Cited by 99 publications

(95 citation statements)

References 19 publications

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“…Rosenberg, Solan and Vieille [25] consider a model with fully rational agents -one per nodemaximizing the discounted sum of payoffs. In this setting strategic behavior arises, and so they study the properties of the model's Nash equilibria.…”

Section: Discussion Of Our Modelmentioning

confidence: 99%

“…To the best of our knowledge, the literature (e.g., [13,25,22]) does not contain an explicit description of an algorithm to compute the actions chosen by agents in our model. However, it seems that a dynamic programming algorithm that performs this computation is well known.…”

Section: Efficient Computationmentioning

confidence: 99%

“…They show, in the spirit of Aumann's Agreement Theorem [4], that agents on a network converge to the same action under some conditions 1 . Related work by Rosenberg, Solan and Vieille [25] and Ménager [20] sheds more light on the phenomenon of agreement on actions and the conditions in which it arises.…”

Section: Introductionmentioning

confidence: 99%

“…Second, a game theoretic analysis of strategic players is possible only if the players choose actions that are optimal by some criterion. Hence game-theoretic analyses of learning on networks (e.g., [25]) often opt for the more difficult but fully Bayesian model.…”

Section: Introductionmentioning

confidence: 99%

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Tractable Bayesian social learning on trees

Kanoria

Tamuz

2012

2012 IEEE International Symposium on Information Theory Proceedings

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We study a model of Bayesian agents in social networks who learn from the actions of their neighbors. Most results concerning social learning in networks have been achieved either in 'herd behavior' models, where each agent acts only once, or in models where agents are not Bayesian and use rules of thumb, or are boundedly rational. Models of Bayesian agents who act repeatedly have posed two related problems: (1) they have proved notoriously difficult to analyze; and (2) the calculations required of interacting Bayesian agents often seem intractable.We consider a set of Bayesian agents who are attempting to iteratively estimate an unknown 'state of the world' s from their initial private signals, and the past actions of their neighbors in a social network. When private signals are independent conditioned on s, and when the social network graph is a tree, we provide an algorithm for the agents' calculations with running time that is exponentially lower than what is currently known.We use this algorithm to perform the first numerical simulations of interacting Bayesian agents on networks with hundreds of nodes, and observe rapid learning of s in some settings.

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Section: Discussion Of Our Modelmentioning

confidence: 99%

Section: Efficient Computationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Tractable Bayesian social learning on trees

Kanoria

Tamuz

2012

2012 IEEE International Symposium on Information Theory Proceedings

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“…The first of these question is answered independently by Rosenberg et al (2009) in a setting where agents select utility maximizing actions in each period. My approach is somewhat different.…”

Section: Introductionmentioning

confidence: 99%

A General Framework for Rational Learning in Social Networks

Mueller‐Frank

2011

SSRN Journal

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. This paper provides a formal characterization of the process of rational learning in social networks. Agents receive initial private information and select an action out of a choice set under uncertainty in each of infinitely many periods, observing the history of choices of their neighbors. Choices are made based on a common behavioral rule. Conditions under which rational learning leads to global consensus, local indifference, and local disagreement are characterized. In the general setting considered, rational learning can lead to pairs of neighbors selecting different actions once learning ends while not being indifferent among their choices. Terms of use: Documents in EconStor mayThe effect of the network structure on the degree of information aggregation and speed of convergence is also considered, and an answer to the question of optimal information aggregation in networks is provided. The results highlight distinguishing features between properties of Bayesian and non-Bayesian learning in social networks.

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