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How Can Social Networks Ever Become Complex? Modelling the Emergence of Complex Networks from Local Social Exchanges

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Abstract

Small-world and power-law network structures have been prominently proposed as models of large networks. However, the assumptions of these models usually lack sociological grounding. We present a computational model grounded in social exchange theory. Agents search attractive exchange partners in a diverse population. Agent use simple decision heuristics, based on imperfect, local information. Computer simulations show that the topological structure of the emergent social network depends heavily upon two sets of conditions, harshness of the exchange game and learning capacities of the agents. Further analysis show that a combination of these conditions affects whether star-like, small-world or power-law structures emerge.

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  • Josep M. Pujol & Andreas Flache & Jordi Delgado & Ramon Sangüesa, 2005. "How Can Social Networks Ever Become Complex? Modelling the Emergence of Complex Networks from Local Social Exchanges," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 8(4), pages 1-12.
    • Handle: RePEc:jas:jasssj:2005-40-2
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    1. Andreas Flache, 2001. "Individual Risk Preferences And Collective Outcomes In The Evolution Of Exchange Networks," Rationality and Society, , vol. 13(3), pages 304-348, August.
    2. Michael D. Cohen & Rick L. Riolo & Robert Axelrod, 2001. "The Role Of Social Structure In The Maintenance Of Cooperative Regimes," Rationality and Society, , vol. 13(1), pages 5-32, February.
    3. Todd, Peter M. & Gigerenzer, Gerd, 2003. "Bounding rationality to the world," Journal of Economic Psychology, Elsevier, vol. 24(2), pages 143-165, April.
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    Cited by:

    1. David O'Sullivan, 2009. "Changing Neighborhoods—Neighborhoods Changing," Sociological Methods & Research, , vol. 37(4), pages 498-530, May.
    2. Lynne Hamill & Nigel Gilbert, 2009. "Social Circles: A Simple Structure for Agent-Based Social Network Models," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 12(2), pages 1-3.
    3. Marcel Ausloos & Herbert Dawid & Ugo Merlone, 2015. "Spatial Interactions in Agent-Based Modeling," Dynamic Modeling and Econometrics in Economics and Finance, in: Pasquale Commendatore & Saime Kayam & Ingrid Kubin (ed.), Complexity and Geographical Economics, edition 127, pages 353-377, Springer.
    4. Telcs, András & Csernai, Márton & Gulyás, András, 2013. "Load balanced diffusive capture process on homophilic scale-free networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(3), pages 510-519.
    5. Mooweon Rhee & Tohyun Kim, 2014. "Identity-based learning and segregation in social networks under different institutional environments," Computational and Mathematical Organization Theory, Springer, vol. 20(4), pages 339-368, December.
    6. Enayat, Taha & Mehrani Ardebili, Mohsen & Reyhani Kivi, Ramtin & Amjadi, Bahador & Jamali, Yousef, 2022. "A computational approach to Homans Social Exchange Theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 597(C).

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