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Simulating Social Network Formation: A Case-Based Decision Theoretic Model

Simulating Social Network Formation: A Case-Based Decision Theoretic Model
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Author(s): Robert Gilles (Virginia Tech, USA), Tabitha James (Virginia Tech, USA), Reza Barkhi (Virginia Tech, USA) and Dimitrios Diamantaras (Temple University, USA)
Copyright: 2009
Volume: 1
Issue: 4
Pages: 20
Source title: International Journal of Virtual Communities and Social Networking (IJVCSN)
Editor(s)-in-Chief: Subhasish Dasgupta (George Washington University, USA) and Rohit Rampal (State University of New York at Plattsburgh, USA)
DOI: 10.4018/jvcsn.2009092201

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Abstract

Social networks depict complex systems as graph theoretic models. The study of the formation of such systems (or networks) and the subsequent analysis of the network structures are of great interest. For information systems research and its impact on business practice, the ability to model and simulate a system of individuals interacting to achieve a certain socio-economic goal holds much promise for proper design and use of cyber networks. We use case-based decision theory to formulate a customizable model of information gathering in a social network. In this model, the agents in the network have limited awareness of the social network in which they operate and of the fixed, underlying payoff structure. Agents collect payoff information from neighbors within the prevailing social network, and they base their networking decisions on this information. Along with the introduction of the decision theoretic model, we developed software to simulate the formation of such networks in a customizable context to examine how the network structure can be influenced by the parameters that define social relationships. We present computational experiments that illustrate the growth and stability of the simulated social networks ensuing from the proposed model. The model and simulation illustrates how network structure influences agent behavior in a social network and how network structures, agent behavior, and agent decisions influence each other.

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