To study the evolution of segregation in social networks across systems embedded in different institutional environments, we develop an identity-based learning model where segregation is stochastically conditioned by the initial distribution of the actor’s attention to identity and the updating of this distribution over time. The updating process, which we call the process of mutual learning multiplier, is based on an actor’s success and failure experiences in tying with the same-subgroup and cross-subgroup actors. Results from a Monte Carlo simulation of the model show that the mutual learning multiplier produces disproportional relationships between the initial distribution of identity attention and the level of segregation in social networks. We also find that those relationships are affected by the actors’ attention to structural holes, rate of learning from experience, system size, and the identity heterogeneity of the system. Overall, the model provides insights into various dynamics of network structuration across time and space.
|Number of pages||30|
|Journal||Computational and Mathematical Organization Theory|
|Publication status||Published - 2014 Dec 1|
Bibliographical noteFunding Information:
We are very thankful to Steve Borgatti, Jim March, Olav Sorenson, and anonymous reviewers for their insightful comments on earlier versions of this manuscript. This paper was supported by the National Research Foundation of Korea Grant funded by the Korean Government (NRF-2013S1A3A2053799) to Mooweon Rhee.
All Science Journal Classification (ASJC) codes
- Decision Sciences(all)
- Computer Science(all)
- Modelling and Simulation
- Computational Mathematics
- Applied Mathematics