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Local Network Effects and Complex Network Structure

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  • Sundararajan Arun

    (New York University)

Abstract

This paper presents a model of local network effects in which agents connected in a social network each value the adoption of a product by a heterogeneous subset of other agents in their neighborhood, and have incomplete information about the structure and strength of adoption complementarities between all other agents. I show that the symmetric Bayes-Nash equilibria of this network game are in monotone strategies, can be strictly Pareto-ranked based on a scalar neighbor-adoption probability value, and that the greatest such equilibrium is uniquely coalition-proof. Each Bayes-Nash equilibrium has a corresponding fulfilled-expectations equilibrium under which agents form local adoption expectations. Examples illustrate cases in which the social network is an instance of a Poisson random graph, when it is a complete graph, a standard model of network effects, and when it is a generalized random graph. A generating function describing the structure of networks of adopting agents is characterized as a function of the Bayes-Nash equilibrium they play, and empirical implications of this characterization are discussed.

Suggested Citation

  • Sundararajan Arun, 2008. "Local Network Effects and Complex Network Structure," The B.E. Journal of Theoretical Economics, De Gruyter, vol. 7(1), pages 1-37, January.
    • Handle: RePEc:bpj:bejtec:v:7:y:2008:i:1:n:46
    DOI: 10.2202/1935-1704.1319
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    Cited by:

    1. Wang, Shuliang & Sun, Jingya & Zhang, Jianhua & Dong, Qiqi & Gu, Xifeng & Chen, Chen, 2023. "Attack-Defense game analysis of critical infrastructure network based on Cournot model with fixed operating nodes," International Journal of Critical Infrastructure Protection, Elsevier, vol. 40(C).
    2. Bryan S. Graham & Andrin Pelican, 2023. "Scenario Sampling for Large Supermodular Games," Papers 2307.11857, arXiv.org.
    3. Harmsen - van Hout, Marjolein J.W. & Dellaert, Benedict G.C. & Herings, P. Jean-Jacques, 2016. "Heuristic decision making in network linking," European Journal of Operational Research, Elsevier, vol. 251(1), pages 158-170.
    4. Iván Arribas & Amparo Urbano Salvador, 2014. "Local coordination and global congestion in random networks," Discussion Papers in Economic Behaviour 0814, University of Valencia, ERI-CES.
    5. Pekka S��skilahti, 2015. "Monopoly Pricing of Social Goods," International Journal of the Economics of Business, Taylor & Francis Journals, vol. 22(3), pages 429-448, November.
    6. Gal Oestreicher-Singer & Arun Sundararajan, 2012. "The Visible Hand? Demand Effects of Recommendation Networks in Electronic Markets," Management Science, INFORMS, vol. 58(11), pages 1963-1981, November.
    7. Daniel Birke, 2009. "The Economics Of Networks: A Survey Of The Empirical Literature," Journal of Economic Surveys, Wiley Blackwell, vol. 23(4), pages 762-793, September.
    8. Ruiz Palazuelos, Sofía, 2021. "Network Perception in Network Games," MPRA Paper 115212, University Library of Munich, Germany, revised 21 Jun 0022.
    9. Jadbabaie, Ali & Kakhbod, Ali, 2019. "Optimal contracting in networks," Journal of Economic Theory, Elsevier, vol. 183(C), pages 1094-1153.
    10. Mohamed Belhaj & Frédéric Deroïan, 2016. "The Value of Network Information: Assortative Mixing Makes the Difference," AMSE Working Papers 1618, Aix-Marseille School of Economics, France, revised 11 May 2016.
    11. Belhaj, Mohamed & Deroïan, Frédéric, 2021. "The value of network information: Assortative mixing makes the difference," Games and Economic Behavior, Elsevier, vol. 126(C), pages 428-442.
    12. Zhang, Yang & He, Longfei, 2021. "Theory and experiments on network games of public goods: inequality aversion and welfare preference," Journal of Economic Behavior & Organization, Elsevier, vol. 190(C), pages 326-347.
    13. Zhiyi Wang & Lusi Yang & Jungpil Hahn, 2023. "Winner Takes All? The Blockbuster Effect on Crowdfunding Platforms," Information Systems Research, INFORMS, vol. 34(3), pages 935-960, September.
    14. Bryan S. Graham & Andrin Pelican, 2023. "Scenario sampling for large supermodular games," CeMMAP working papers 15/23, Institute for Fiscal Studies.
    15. Kexin Zhao & Bin Zhang & Xue Bai, 2018. "Estimating Contextual Motivating Factors in Virtual Interorganizational Communities of Practice: Peer Effects and Organizational Influences," Information Systems Research, INFORMS, vol. 29(4), pages 910-927, December.

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