Work Package 4

Network experiments (PI Jaspers)

WP4 employs an experimental approach inspired by socalled intergroup chicken games, to test how inclusivity norms can spread in polarized networks. The modelling strategy is threefold: 1. simulations; 2. testing of various within group structures; 3. testing of various betweengroup structures. We will start with a series of AgentBased Models (ABMs) in which we vary centralities (e.g., high eigenvector vs high degree centrality) as well as connections (clustering) between two opposing groups, to determine the theoretical reach of our norm interventions. These models will help us develop hypotheses and determine the structures for the following experiments. Network simulations do not always capture the complex interdependencies of reallife networks, but they inform us about the most promising avenues for lab experiments. Next, we will impose various network structures in a first series of laboratory experiments to spread the new norm. Two groups will be competing in a specifically designed game over a collective good. The new inclusivity norm will be introduced as an incentive to collaborate with the other team, rather than compete, but only if the innovator(s) manage(s) to get the majority of both groups to collaborate. We test for instance whether a starlike structured network would quickly spread the new norm, arguing that the most central actors can carry innovation the furthest (social referent approach). Centola posits that whereas information or disease may spread via highly connected ‘influencers’, individuals only adopt new behaviors (in this case: norms) after having been exposed to them through multiple social contacts. The mechanism underlying this regularity is social reinforcement. Adoption of new complex behaviors imposes strain on actors, both because they have to replace psychologically embedded behaviors, and because they risk nonconformity to the group. Nonconformity implies social sanctions, such as ridicule or social
exclusion. When multiple socially linked others signal their conformity of the behavior to the individual, they will adjust their cost/benefit consideration for adoption of the behavior and will often ultimately do so. Should inclusivity norms diffuse via this mechanism, one would expect a clustered network with many relationships to facilitate the most effective spread. We will thus evaluate both the social referent and complex contagion strategies for the spread of inclusivity norms. Polarized networks might additionally spread norms differently. Barriers to the spread of norms can be found when the network contains small cliques or network positions that block the dissemination. Social referents with high standings across both groups potentially overcome these barriers, or lowlevel individuals with some ties at least to the other group are better able to successfully introduce inclusivity norms from the fringes. In a second series of
experiments, we therefore intend to limit and vary with whom in both their own and the other group individuals can communicate. We will introduce inclusivity norms when there is no contact between the groups; when few people can communicate with one other person from the opposing group; and when one individual can communicate with many others of the other group. By varying the structure of intergroup communication, we can determine which structure spreads inclusivity norms best and furthest. The exact number of participants needed for these experiments is determined after the simulations, as some network structures require more network members than others. Participants will be recruited online, and via the sociology lab at Utrecht University. Combined, these simulations and experiments will identify which target individuals are most successful in spreading inclusivity norms and under which within- and between-group structures.