Group adaptation drives opinion dynamics in higher-order networks

Abstract: In modern interconnected societies, opinions and beliefs can quickly spread across large populations, giving rise to collective behaviors such as the adoption of social norms or polarization. These phenomena have motivated many models aimed at reproducing emergent properties from simple interaction mechanisms. In particular, opinion dynamics models describe how individual opinions evolve through interactions and study the conditions for global consensus or polarization. Most models assume that these interactions occur between pairs of agents, typically on a fixed network structure. However, opinion changes can occur in groups, which may also undergo adaptive changes if disagreement arises. Here, we propose a bounded confidence model that incorporates both mechanisms: group discussions can lead to global agreement among members, while strong internal disagreement causes groups to split, with resulting subgroups merging with others. We systematically study the model outcomes as a function of agents' tolerance for agreement. Strikingly, adaptivity suppresses key effects of group interactions, restoring a phenomenology close to that of pairwise interactions. In particular, adaptivity enables the formation of large groups and prevents fragmentation at small tolerance. It also restores a phase transition from polarization to consensus, which would otherwise disappear in a non-adaptive group-based model. Overall, our work shows that both adaptivity and group interactions shape the structure of social ties and global opinion dynamics in a population.