Dynamic Social Network Modeling and Analysis Workshop Summary and Papers (2003) / Chapter Skim
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Using Multi-Theoretical Multi-Level (MTML) Models to Study Adversarial Networks
Using Multi-Theoretical Multi-Level (MTML) Models to Study Adversarial Networks
Pages 324-344
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It begins by identifying the theoretical mechanisms that influence the dynamics and coevolution of communication and knowledge networks in general. Next it describes how examining a`dversanal social networks requires an extension to the MTML framework.
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~ ^^ ~~ v, ~^ ^~ ~~ —In ~—v Allot QU1 Adversarial social networks are defined as the networks of multiple organizations within a population or the networks of multiple populations of organizations within a community (van Meter, 20011. These networks exist in the same or similar niches, competing for the same or similar resources, and seek to Dive out their competitors?
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how best to characterize knowledge networks at various levels and 326 DYNAMIC SOCIAL NETWORK MODELING AND ANALYSIS
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Selected Social Theories and their Theoretical Mechanisms Theorv Theories of Self-Interest Social Capital Structural Holes Transaction Costs Mutual Self Interest & Collective Action Public Good Theory Cntical Mass Theory Cognitive Theories Semantic/knowledge Networks Cognitive social structures Cognitive Consistency Balance theory Cognitive Dissonance Contagion Theories Social Information Processing Social Learning Theory Institutional Theory Structural Theory of Action Exchange and Dependency Social Exchange Theory Resource Dependency Network Exchange Homophily & Proximity Social Companson Theory Social Identity Physical proximity Electronic Proximity 328 Theoretical Mechanism Investments in opportunities Control of information flow Cost minimization Joint value maximization Inducements to contribute Number of people with resources & interests Cognitive mechanisms leading to: Shared interpretations Similanty in perceptual structures Drive to avoid imbalance & restore balance Dnve to reduce dissonance Exposure to contact leading to: Social influence Imitation, modeling Mimetic behavior Similar positions in structure and roles Exchange of valued resources Equality of exchange Inequality of exchange Complex calculi for balance Choices based on similarity Choose comparable others Choose based on own group identity Influence of distance Influence of accessibility DYNAMIC SOCIAL NETWORK MODELING ED ISIS
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Semantic networks are created on the basis of shared message content and similarity in interpretation and understanding (CarIey, 1986; Monge & Eisenberg, 1987~. A complementary perspective views interorganizational networks as structures of DYNAMIC SOCIAL NETWORK HODEL~G ED CYSTS 329
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Competitors often develop differing semantic networks based on ingroup-outgroup polarization. While developing their own knowledge networks, competitors seek to undermine the knowledge networks of others, often by disseminating misinformation and faulty knowledge to their opponents, including their cognitive communication structures.
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In most social contexts, more than one of the theoretical mechanisms reviewed above simultaneously influence people. In some cases different theones, some using similar theoretical mechanisms, offer similar explanations but at different levels of analysis.
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provides a framework to explain the coevolution of populations of adversarial networks within a community. Community ecology examines multiple populations of differing organizations as well as the venous niches in which they occur.
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Clearly, the community ecology theory 334 DYNAMIC SOCIAL NETWORK MODELING ED THESIS
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ANALYTIC FRAMEWORK FOR STUDYING MTML MODELS The previous section has described theoretical mechanisms that offer explanations for the co-evolution of communication and knowledge networks. The schematic in the following figure describes a comprehensive analytic methodology developed by Contractor et ~ (1999)
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refer to venous relational properties of the focal network itself that influence the probability of ties being present or absent in the same network. From a meta-theoretical perspective, these endogenous variables 336 DYNAMIC SOCKS NETWORK MODELING ED ^4YSIS
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refer to venous properties outside the specific relation within the focal network that influence the probability of ties being present or absent in the focal network. Hence exogenous variables include the attributes of the actors in the network, additional network relations among the actors, the same network relation at previous points in time, as well as other networks within the same population or other populations.
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Social dilemmas: DYNAMIC SOCIAL NETWORK MODELING ED ANALYSIS 339
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(19951. A social network perspective on human resources management.
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. Centrality in social networks: I Conceptual clarification.
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Social Networks, 9, ~ 09- ~ 34. Krebs, V
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(2001~. Terronsts/Liberators: Researching and dealing with adversary social networks.
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(2001~. Computer networks as social networks.
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