Andreas Flache

The weakness of strong ties : Collective action failure in a highly cohesive group

Following Homans, exchange theorists have modeled informal social control as an exchange of peer approval for compliance with group obligations. The exchange model predicts higher compliance in cohesive networks with strong social ties. However, previous specifications failed to incorporate bilateral exchange of approval. Computer simulations using a Bush‐Mosteller stochastic learning model show that bilateral exchanges evolve more readily than multilateral, causing social control to flow into the maintenance of interpersonal relationships at the expense of compliance with group obligations, a structural form of the “second‐order free‐rider problem.”

Individualization as Driving Force of Clustering Phenomena in Humans

One of the most intriguing dynamics in biological systems is the emergence of clustering, in the sense that individuals self-organize into separate agglomerations in physical or behavioral space. Several theories have been developed to explain clustering in, for instance, multi-cellular organisms, ant colonies, bee hives, flocks of birds, schools of fish, and animal herds. A persistent puzzle, however, is the clustering of opinions in human populations, particularly when opinions vary continuously, such as the degree to which citizens are in favor of or against a vaccination program. Existing continuous opinion formation models predict “monoculture” in the long run, unless subsets of the population are perfectly separated from each other. Yet, social diversity is a robust empirical phenomenon, although perfect separation is hardly possible in an increasingly connected world. Considering randomness has not overcome the theoretical shortcomings so far. Small perturbations of individual opinions trigger social influence cascades that inevitably lead to monoculture, while larger noise disrupts opinion clusters and results in rampant individualism without any social structure. Our solution to the puzzle builds on recent empirical research, combining the integrative tendencies of social influence with the disintegrative effects of individualization. A key element of the new computational model is an adaptive kind of noise. We conduct computer simulation experiments demonstrating that with this kind of noise a third phase besides individualism and monoculture becomes possible, characterized by the formation of metastable clusters with diversity between and consensus within clusters. When clusters are small, individualization tendencies are too weak to prohibit a fusion of clusters. When clusters grow too large, however, individualization increases in strength, which promotes their splitting. In summary, the new model can explain cultural clustering in human societies. Strikingly, model predictions are not only robust to “noise”—randomness is actually the central mechanism that sustains pluralism and clustering.

Complexity theory and financial regulation

Economic policy needs interdisciplinary network analysis and behavioral modeling Traditional economic theory could not explain, much less predict, the near collapse of the financial system and its long-lasting effects on the global economy. Since the 2008 crisis, there has been increasing interest in using ideas from complexity theory to make sense of economic and financial markets. Concepts, such as tipping points, networks, contagion, feedback, and resilience have entered the financial and regulatory lexicon, but actual use of complexity models and results remains at an early stage. Recent insights and techniques offer potential for better monitoring and management of highly interconnected economic and financial systems and, thus, may help anticipate and manage future crises.

How to get the timing right. A computational model of the effects of the timing of contacts on team cohesion in demographically diverse teams

Lau and Murnighan’s faultline theory explains negative effects of demographic diversity on team performance as consequence of strong demographic faultlines. If demographic differences between group members are correlated across various dimensions, the team is likely to show a “subgroup split” that inhibits communication and effective collaboration between team members. Our paper proposes a rigorous formal and computational reconstruction of the theory. Our model integrates four elementary mechanisms of social interaction, homophily, heterophobia, social influence and rejection into a computational representation of the dynamics of both opinions and social relations in the team. Computational experiments demonstrate that the central claims of faultline theory are consistent with the model. We show furthermore that the model highlights a new structural condition that may give managers a handle to temper the negative effects of strong demographic faultlines. We call this condition the timing of contacts. Computational analyses reveal that negative effects of strong faultlines critically depend on who is when brought in contact with whom in the process of social interactions in the team. More specifically, we demonstrate that faultlines have hardly negative effects when teams are initially split into demographically homogeneous subteams that are merged only when a local consensus has developed.

Local Convergence and Global Diversity: From Interpersonal to Social Influence

How can minority cultures resist assimilation into a global monolith in an increasingly “small world”? Paradoxically, Axelrod found that local convergence can actually preserve global diversity if cultural influence is combined with homophily, the principle that “likes attract.” However, follow-up studies showed that this diversity collapses under random cultural perturbation. The authors discovered a new source of this fragility—the assumption in Axelrod’s model that cultural influence is interpersonal (dyadic). The authors replicated previous models but with the more empirically plausible assumption that influence is social—people can be simultaneously influenced by several network neighbors. Computational experiments show that cultural diversity then becomes much more robust than in Axelrod’s original model or in published variations that included either social influence or homophily but not both. The authors conclude that global diversity may be sustained not by cultural experimentation and innovation but by the ability of cultural groups to discourage those activities.

The Double Edge of Common Interest: Ethnic Segregation as an Unintended Byproduct of Opinion Homophily.

How can we reduce ethnic friendship segregation in ethnically heterogeneous schools? The Common Ingroup Identity Model suggests that interethnic friendships are promoted by those intervention programs that focus on the interests students have in common. The authors argue that the outcome of these common interest interventions may crucially depend on sufficient consensus in participants’ opinions regarding the shared interest. Such an intervention may backfire and increase ethnic segregation if participants from different ethnic groups have different opinions about the common interest. The authors test their argument analyzing the dynamics of friendship networks and opinions in 48 school classes with an actor-based stochastic model. Their findings suggest that salient common interests in ethnically mixed school classes can indeed reduce ethnic segregation. However, they also found that friendship selection on the basis of similar opinions can foster ethnic segregation. This occurred when ethnicity was correlated with the opinions that students held regarding the salient interest, even when these students did not prefer intra-ethnic friendship per se.

Stochastic Collusion and the Power Law of Learning

Concerns about models of cultural adaptation as analogs of genetic selection have led cognitive game theorists to explore learning-theoretic specifications. Two prominent examples, the Bush-Mosteller stochastic learning model and the Roth-Erev payoff-matching model, are aligned and integrated as special cases of a general reinforcement learning model. Both models predict stochastic collusion as a backward-looking solution to the problem of cooperation in social dilemmas based on a random walk into a self-reinforcing cooperative equilibrium. The integration uncovers hidden assumptions that constrain the generality of the theoretical derivations. Specifically, Roth and Erev assume a “power law of learning”—the curious but plausible tendency for learning to diminish with success and intensify with failure. Computer simulation is used to explore the effects on stochastic collusion in three social dilemma games. The analysis shows how the integration of alternative models can uncover underlying principles and lead to a more general theory.

Generalization of Positive and Negative Attitudes Toward Individuals to Outgroup Attitudes

The generalization of attitudes toward individual outgroup members into attitudes toward the outgroup as a whole can affect intergroup relations. However, little is known about the relative strengths of the generalization of negative and positive interpersonal attitudes into attitudes about the outgroup. The unique contribution of negative (disliking) interpersonal attitudes to intergroup attitudes was examined and its strength was compared with the effect of positive (liking) interpersonal attitudes, using cross-sectional (Study 1; N = 733, age 10-12) and longitudinal data (Study 2; N = 960, age 12-13). Disliking uniquely contributed to respondents’ outgroup attitudes. The generalization of interpersonal liking and disliking was about equally strong in both studies. This underpins the importance of examining the effects of both positive and negative intergroup contact experiences on the formation of outgroup attitudes.

Differentiation without Distancing. Explaining Bi-Polarization of Opinions without Negative Influence

Explanations of opinion bi-polarization hinge on the assumption of negative influence, individuals’ striving to amplify differences to disliked others. However, empirical evidence for negative influence is inconclusive, which motivated us to search for an alternative explanation. Here, we demonstrate that bi-polarization can be explained without negative influence, drawing on theories that emphasize the communication of arguments as central mechanism of influence. Due to homophily, actors interact mainly with others whose arguments will intensify existing tendencies for or against the issue at stake. We develop an agent-based model of this theory and compare its implications to those of existing social-influence models, deriving testable hypotheses about the conditions of bi-polarization. Hypotheses were tested with a group-discussion experiment (N = 96). Results demonstrate that argument exchange can entail bi-polarization even when there is no negative influence.

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 on compliance with prosocial norms. Moderate doses of imitation reduce the coordination complexity of self-organized collective action and help the network achieve satisfactory levels of cooperation. High doses, however, undermine the agent-based 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.