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Identity-based learning and segregation in social networks under different institutional environments

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Abstract

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.

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Notes

  1. Although we also observe heterophilous tendency in some contexts (Bell 1978; Skvoretz 1983), we follow Blau (1977) to assume only homophilous tendency, which is far more robust over various types of identity-based network relations (McPherson et al. 2001) and particularly relevant for the phenomenon of social disintegration and segregation in social networks (Blau 1977; Fararo 1981; Freeman 1978; Skvoretz 1983).

  2. For the sake of simplicity, our model has a scope condition that assumes such residual attention does not affect an actor’s decision of choosing a counterpart in terms of the identity in focus. While other kinds of identities or network positions may be correlated with the identity in focus, we can conjecture that the salience of these other features increases the salience of the identity in focus as much as they are correlated (cf. Skvoretz, 1983) and attracts the actor’s initial attention that much more.

  3. If we move beyond our scope condition by considering multiple identities and multivalued subgroups, the residual attention and correlation (or consolidation) among these multiple identities become important issues (cf. Blau, 1977). We discuss this issue in the discussion section.

  4. It is assumed that each actor’s likelihood of tie initiation is not affected by other factors, such as the size and composition of the systems, which allows us to compare the net structural effects.

  5. The actors (and the systems) with low levels of attention to identity reach the maximum capacity (or density) slightly more slowly than those with high levels, to the extent that these actors are relatively more likely to randomly reject a same-subgroup tie initiation, as indicated in Eq. (4).

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Authors and Affiliations

  1. School of Business, Yonsei University, 134 Shinchon-dong, Seodaemun-gu, Seoul, 120-749, Korea

    Mooweon Rhee

  2. School of Business, Sungkyunkwan University, 25-2 Seonggyungwan-ro, Jongno-gu, Seoul, 110-745, Korea

    Tohyun Kim

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  1. Mooweon Rhee
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Correspondence to Tohyun Kim.

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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.

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Rhee, M., Kim, T. Identity-based learning and segregation in social networks under different institutional environments. Comput Math Organ Theory 20, 339–368 (2014). https://doi.org/10.1007/s10588-013-9169-7

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