Tag Archives: Computational Social Science

Cultural Integration and Differentiation in Groups and Organizations

Experimental and field research has demonstrated a pervasive tendency toward pairwise conformity among individuals connected by positive social ties, and work using formal models has shown that opinions on connected influence networks should thus converge toward uniformity. Observing that diversity persists even in small scale groups and organizations, we investigate two empirically grounded mechanisms of social differentiation that may account for this persistence: First, actors may dislike or disrespect peers who diverge too much from their own views, and may change their opinions or behaviors to distance themselves further from those negative referents. Second, when surrounded by similar others, actors may try to maintain a sufficient sense of uniqueness by exploring new opinions or behaviors. Using computational experiments, we demonstrate that these two mechanisms lead to different patterns of polarization, radicalization, and factionalism and also investigate the conditions under which integration occurs. In closing, we discuss the implications for cultural dynamics in organizations.

Mäs, Michael, Flache, Andreas, and James A. Kitts. “Cultural Integration and Differentiation in Groups and Organizations.” Perspectives on Culture and Agent-Based Simulations: Integrating Cultures, Springer, 2014.

Social Influence and the Emergence of Norms Amid Ties of Amity and Enmity

This paper explores the coevolution of social networks and behavioral norms. Previous research has investigated the long-term behavior of feedback systems of attraction and influence, particularly the tendency toward homogenization in arbitrary cultural fields. This paper extends those models by allowing that norms diffuse not only by simple contagion but through intentional sanctioning behavior among peers. Further, the model allows for negative relations, where actors differentiate themselves from enemies while seeking to align themselves with friends. Sociometric maps reveal nontrivial system dynamics – structural bifurcation, discrimination between factions, and cycles of deviance and solidarity – emerging from a few elementary agent-level assumptions.

View online appendix here.

Kitts, James A. “Social Influence and the Emergence of Norms Amid Ties of Amity and Enmity.” Simulation Modelling Practice and Theory, 14(3): 407-422, March 2006.

Polarization in Dynamic Networks: A Hopfield Model of Emergent Structure

Why do populations often self-organize into antagonistic groups even in the absence of competition over scarce resources? We look for answers by exploring the dynamics of influence and attraction between computational agents. Our model is an extension of Hopfield’s attractor network. Agents are attracted to others with similar states (the principle of homophily) and are also influenced by others, as conditioned by the strength and valence of the social tie. Negative valence implies xenophobia (instead of homophily) and differentiation (instead of imitation). Consistent with earlier work on structural balance, we find that networks can self-organize into two antagonistic factions, without the knowledge or intent of the agents. We model this tendency as a function of network size, the number of potentially contentious issues, and agents’ openness and flexibility toward alternative positions. Although we find that polarization into two antagonistic groups is a unique global attractor, we investigate the conditions under which global uniformity or pluralistic alignments may also be equilibria.

Macy, Michael W., Kitts, James A., Flache, Andreas, and Steve Benard. “Polarization in Dynamic Networks: A Hopfield Model of Emergent Structure. Dynamic Social Network Modeling and Analysis, National Academies Press, 162-173, 2003.

Structural Learning: Attraction and Conformity in Task-Oriented Groups

This study extends previous research that showed how informal social sanctions can backfire when members prefer friendship over enforcement of group norms. We use a type of neural network to model the coordination of informal social control in a small group of adaptive agents confronted with a social dilemma. This model incorporates two mechanisms of social influence, informal sanctions and imitation. Both mechanisms vary with the strength of the social tie between source and target. Previous research focused on the effects of social sanctions. Here, we demonstrate a curvilinear effect of imitation – or “mimetic influence” – on compliance with prosocial norms. Moderate doses of mimetic influence reduce the coordination complexity of self-organized collective action and help the network achieve satisfactory levels of cooperation. High doses, however, undermine the learning required to find cooperative solutions. Increasing group size also diminishes compliance due to increased complexity, with larger groups requiring more imitation to overcome the coordination problem.

Kitts, James A., Macy, Michael W., and Andreas Flache. “Structural Learning: Attraction and Conformity in Task-Oriented Groups.” Computational and Mathematical Organization Theory, 5:129-145, 1999.