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Small World Networks with Segregation Patterns and Brokers

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  • Gallo Edoardo

    (University of Cambridge and Queens' College)

Abstract

Individuals' cognitive knowledge of their social networks is affected by systematic biases. This paper investigates the role of the mean degree bias, i.e. the tendency to underestimate the number of connections of others, in determining the structure of stable networks. It develops a strategic network formation model where agents have heterogeneous knowledge of the network: cognizant agents know the whole network, while ignorant ones are less knowledgeable and biased. For a broad range of parameters, all cognitively stable (CS) networks are small world networks with segregation patterns and brokers. There are also some CS networks with one hub.

Suggested Citation

  • Gallo Edoardo, 2012. "Small World Networks with Segregation Patterns and Brokers," Review of Network Economics, De Gruyter, vol. 11(3), pages 1-46, September.
    • Handle: RePEc:bpj:rneart:v:11:y:2012:i:3:n:7
    DOI: 10.1515/1446-9022.1366
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    References listed on IDEAS

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    Cited by:

    1. Somayeh Koohborfardhaghighi & Jorn Altmann, 2016. "How Strategic Networking Impacts the Networking Outcome: A Complex Adaptive System Approach," TEMEP Discussion Papers 2016131, Seoul National University; Technology Management, Economics, and Policy Program (TEMEP), revised Aug 2016.
    2. Dev, Pritha, 2014. "Identity and fragmentation in networks," Mathematical Social Sciences, Elsevier, vol. 71(C), pages 86-100.
    3. Ping Sun & Elena Parilina, 2022. "Impact of Utilities on the Structures of Stable Networks with Ordered Group Partitioning," Dynamic Games and Applications, Springer, vol. 12(4), pages 1131-1162, December.
    4. Hellmann, Tim & Staudigl, Mathias, 2014. "Evolution of social networks," European Journal of Operational Research, Elsevier, vol. 234(3), pages 583-596.
    5. Somayeh Koohborfardhaghighi & Jorn Altmann, 2016. "How Network Visibility and Strategic Networking Leads to the Emergence of Certain Network Characteristics: A Complex Adaptive System Approach," TEMEP Discussion Papers 2016130, Seoul National University; Technology Management, Economics, and Policy Program (TEMEP), revised Aug 2016.

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