Simulation Dataset: Breaking Echo Chambers in Collective Opinion Dynamics with Homophily

Abstract

Collective estimation is a variant of collective decision-making, where agents need to achieve consensus on an environmental quantity in a self-organized fashion via social interactions. A particularly challenging scenario is a fully distributed collective estimation with strongly constrained, dynamical interaction networks, for example, encountered in real physical space, where agents need first to explore a spatially distributed feature through movement, then reach consensus, while being able only to communicate with nearby neighbors. Here, collectives face several challenges in achieving precise, consensus estimation, particularly due to complex behaviors emerging from the simultaneous evolution of the interaction network and the agents' opinions. While homophilic networks may facilitate collective estimation in well-connected networks, we show that disproportionate interactions with like-minded neighbors lead to the emergence of echo chambers, preventing collective consensus. Our simulation results confirm that, besides a lack of exposure to attitude-challenging opinions, seeking reaffirming information entraps agents in echo chambers. We propose a generic novel approach based on a Dichotomous Markov Process (DMP) where stubborn mobile agents (called Messengers) connect the disconnected clusters by physically transporting their opinions to other clusters to inform and direct the other agents. We show that diverse collective behaviors arise from the DMP as a switching mechanism. We study a continuum between task specialization with no switching (full-time Messengers), generalization with slow task switching (part-time Messengers), and rapid task switching (short-time Messengers) and its impact on system performance. Our results show that stubborn agents can, in various ways, break the echo chambers and promote consensus in collective opinion.