This model extends Schelling’s segregation model with mobility sharing. Agents of two groups occupy cells of a 32 × 32 grid and repeatedly choose activity destinations subject to a preference threshold, the minimum share of ingroup neighbours they accept. Trips are matched into shared rides between members of different groups, using one of four matching algorithms on a shareability network (maximal cross-group matching, maximum weight matching on contact time, maximum cardinality matching, or random greedy matching) with a maximum detour constraint. After each trip, an agent’s preference threshold changes with the time spent in contact with outgroup members.

Starting from complete segregation, the model examines under which conditions contact during shared trips leads to integration. Besides the main scenario, in which contact always lowers the threshold, it includes two extensions. In the first, a share of adversarial agents becomes less tolerant with contact, and this share grows or shrinks with the current level of segregation, governed by a branching dissimilarity index and a conversion rate. In the second, each contact raises or lowers the threshold with a probability that depends on the agent’s current attitude relative to a compatibility threshold.

Segregation is measured with the index of dissimilarity and the Gini coefficient. The model file includes BehaviorSpace experiments for each analysis in the associated article. The matching step uses the NetworkX library through the Python extension of NetLogo (requires Python 3 with numpy and networkx 3.x).

This model represents individuals from two different groups interacting and sharing attitudes toward both groups. Through these interactions, individuals adjust their attitudes, which can lead to different outcomes in opinion dynamics. The model can study both intra-group and inter-group opinion dynamics. Under certain conditions, agents with initially moderate attitudes might enter a negative spiral, leading to increasingly extreme intergroup attitudes. Alternatively, these moderate attitudes can foster consensus between groups, resulting in agreement on intergroup attitudes. This model serves as a tool for exploring opinion polarization, particularly in contexts where attitudes evolve through interaction rather than remaining static.

By adjusting different parameters, one can examine the effects of variations in attitude distributions; group size and relative group size; contributions of xenophobia, opinion distance, and individual attitudes towards the outgroup; as well as ratio between xenophobia and attitudinal bias to the overall intergroup bias.

The model accompanies the paper When contact backfires, and when it does not: A computational model of the opinion dynamics of intergroup attitudes.

We present a network agent-based model of ethnocentrism and intergroup cooperation in which agents from two groups (majority and minority) change their communality (feeling of group solidarity), cooperation strategy and social ties, depending on a barrier of “likeness” (affinity). Our purpose was to study the model’s capability for describing how the mechanisms of preexisting markers (or “tags”) that can work as cues for inducing in-group bias, imitation, and reaction to non-cooperating agents, lead to ethnocentrism or intergroup cooperation and influence the formation of the network of mixed ties between agents of different groups. We explored the model’s behavior via four experiments in which we studied the combined effects of “likeness,” relative size of the minority group, degree of connectivity of the social network, game difficulty (strength) and relative frequencies of strategy revision and structural adaptation. The parameters that have a stronger influence on the emerging dominant strategies and the formation of mixed ties in the social network are the group-tag barrier, the frequency with which agents react to adverse partners, and the game difficulty. The relative size of the minority group also plays a role in increasing the percentage of mixed ties in the social network. This is consistent with the intergroup ties being dependent on the “arena” of contact (with progressively stronger barriers from e.g. workmates to close relatives), and with measures that hinder intergroup contact also hindering mutual cooperation.