# Dynamics of information exchange in endogenous social networks

## Author Info

• Bimpikis, Kostas

()

• Ozdaglar, Asuman

()
(Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology)

• Acemoglu, Daron

()
(Department of Economics, Massachusetts Institute of Technology)

## Abstract

We develop a model of information exchange through communication and investigate its implications for information aggregation in large societies. An \textit{underlying state} determines payoffs from different actions. Agents decide which others to form a costly \textit{communication link} with, incurring the associated cost. After receiving a \textit{private signal} correlated with the underlying state, they exchange information over the induced \textit{communication network} until taking an (irreversible) action. We define \textit{asymptotic learning} as the fraction of agents taking the correct action converging to one as a society grows large. Under truthful communication, we show that asymptotic learning occurs if (and under some additional conditions, also only if) in the induced communication network most agents are a short distance away from information hubs'', which receive and distribute a large amount of information. Asymptotic learning therefore requires information to be aggregated in the hands of a few agents. We also show that while truthful communication may not always be a best response, it is an equilibrium when the communication network induces asymptotic learning. Moreover, we contrast equilibrium behavior with a socially optimal strategy profile, i.e., a profile that maximizes aggregate welfare. We show that when the network induces asymptotic learning, equilibrium behavior leads to maximum aggregate welfare, but this may not be the case when asymptotic learning does not occur. We then provide a systematic investigation of what types of cost structures and associated social cliques (consisting of groups of individuals linked to each other at zero cost, such as friendship networks) ensure the emergence of communication networks that lead to asymptotic learning. Our result shows that societies with too many and sufficiently large social cliques do not induce asymptotic learning, because each social clique would have sufficient information by itself, making communication with others relatively unattractive. Asymptotic learning results either if social cliques are not too large, in which case communication across cliques is encouraged, or if there exist very large cliques that act as information hubs.

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## Bibliographic Info

Article provided by Econometric Society in its journal Theoretical Economics.

Volume (Year): 9 (2014)
Issue (Month): 1 (January)
Pages:

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Handle: RePEc:the:publsh:1204

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Web page: http://econtheory.org

## Related research

Keywords: Information aggregation; learning; search; social networks;

Other versions of this item:

Find related papers by JEL classification:
• C72 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Noncooperative Games
• D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
• D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search, Learning, and Information
• D85 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Network Formation

## References

References listed on IDEAS
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1. Andrea Galeotti & Sanjeev Goyal, 2002. "Network Formation with Heterogeneous Players," Tinbergen Institute Discussion Papers 02-069/1, Tinbergen Institute.
2. Marco Ottaviani & Giuseppe Moscarini & Lones Smith, 1998. "Social learning in a changing world," Economic Theory, Springer, vol. 11(3), pages 657-665.
3. Andrea Galeotti, 2004. "One-way Flow Networks: the Role of Heterogeneity," Tinbergen Institute Discussion Papers 04-031/1, Tinbergen Institute.
4. Oriana Bandiera & Imran Rasul, 2002. "Social networks and technology adoption in Northern Mozambique," LSE Research Online Documents on Economics 3539, London School of Economics and Political Science, LSE Library.
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[Connections: An Introduction to the Economics of Networks]
," Introductory Chapters, Princeton University Press.
6. Hojman, Daniel A. & Szeidl, Adam, 2008. "Core and periphery in networks," Journal of Economic Theory, Elsevier, vol. 139(1), pages 295-309, March.
7. Ambrus, Attila & Azevedo, Eduardo M. & Kamada, Yuichiro, 2013. "Hierarchical cheap talk," Theoretical Economics, Econometric Society, vol. 8(1), January.
8. Nair, Harikesh S. & Manchanda, Puneet & Bhatia, Tulikaa, 2006. "Asymmetric Peer Effects in Physician Prescription Behavior: The Role of Opinion Leaders," Research Papers 1970, Stanford University, Graduate School of Business.
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10. repec:fth:stanho:e-89-7 is not listed on IDEAS
11. David A. Reinstein & Christopher M. Snyder, 2005. "THE INFLUENCE OF EXPERT REVIEWS ON CONSUMER DEMAND FOR EXPERIENCE GOODS: A CASE STUDY OF MOVIE CRITICS -super-* ," Journal of Industrial Economics, Wiley Blackwell, vol. 53(1), pages 27-51, 03.
12. repec:hal:cesptp:halshs-00367692 is not listed on IDEAS
13. Andrea Galeotti & Christian Ghiglino & Francesco Squintani, 2009. "Strategic Information Transmission in Networks," Economics Discussion Papers 668, University of Essex, Department of Economics.
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## Citations

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Cited by:
1. Garcia, Daniel, 2012. "Communication and Information Acquisition in Networks," MPRA Paper 55481, University Library of Munich, Germany, revised 24 Apr 2014.
2. Bar Ifrach & Costis Maglaras & Marco Scarsini, 2012. "Monopoly Pricing in the Presence of Social Learning," Working Papers 12-01, NET Institute, revised Sep 2012.
3. Carlos Lever Guzmán, 2010. "Strategic Spending in Voting Competitions with Social Networks," Working Papers 2010-16, Banco de México.
4. Daniel Andrei & Bruce Carlin & Michael Hasler, 2014. "Model Disagreement and Economic Outlook," NBER Working Papers 20190, National Bureau of Economic Research, Inc.
5. Pietro Battiston & Luca Stanca, 2014. "Boundedly Rational Opinion Dynamics in Directed Social Networks: Theory and Experimental Evidence," Working Papers 267, University of Milano-Bicocca, Department of Economics, revised Jan 2014.

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