Dynamic Social Network Modeling and Analysis Workshop Summary and Papers (2003) / Chapter Skim
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Polarization in Dynamic Networks: A Hopfield Model of Emergent Structure
Polarization in Dynamic Networks: A Hopfield Model of Emergent Structure
Pages 162-173
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From page 162... ...
Although we find that polarization into two antagonistic groups is a unique global attractor, we investigate the conditions under which uniform and pluralistic alignments may also be equilibria. From a random start, agents can self-organ~ze into pluralistic arrangements if the population size is large relative to the size of the state space.
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From page 163... ...
Formal models generally assume that each agent chooses interaction partners that are similar to itself, while social interaction also leads agents to adopt each other's traits and thus grow more similar. In this positive feedback loop, a minimal initial similarity increases the probability of interaction which then increases similarity.
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From page 164... ...
to social networks. Like Heider's Balance Theory, an important property of attractor networks is that individual nodes seek to minimize "energy,' (or dissonance)
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In the Hopfield model, the path weight wit changes as a function of similarity In the states of node i and j. Weights begin with uniformly distributed random values, subject to the constraints that weights are symmetric (wifwji)
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It also assumes that agents are willing and able to change ad positions in response to social influence (v=~) , which would not be the case, for example, if race or ethnicity were a salient dimension of social differentiation.
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From page 168... ...
DISCUSSION With binary agent states, the dynamics of homophilous influence and xenophobic differentiation create an energy landscape in which there is only one basin of attraction, polarization. Any configuration with more than two cohesive subgroups represents global tension for a reason that is readily apparent: A binary state precludes the ability to hold a position that 168 DYNAMIC SOCIAL NF7TWORI |
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Accordingly, polarization initially decImes with increasing multiplexity. However, if population density In the state space fats below a critical level (either due to low population or high multiplexity)
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, which posits an ~n-group bias toward those who share a salient trait, prejudice against the out-group, and a tendency to ignore or change discrepant traits. The Hopfield model produces dynamic networks that self-organize into a similar pattern, but without a higher-order cognitive Damework of social categories.
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From page 171... ...
As a consequence, the computational model identifies polarization as the global attractor in a multiplex opinion space, an important substantive result that is overlooked by static equilibrium analysis. Previous theoretical work has emphasized the global stability of social homogeneity, where convergence to unanimity is an almost irresistible force In closely interacting populations.
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~ 982. "Neural networks arid physical systems with emergent collective computational abilities." Proc NatAcacI Sci 79:2554-~.
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1994. Social Network Analysis: Methods and Applications.
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