Biased opinion dynamics: when the devil is in the details

• DOI: 10.1016/j.ins.2022.01.072
• Publication Date: 2022-02-02T22:25:39Z
• Authors: Aris Anagnostopoulos; Luca Becchetti; Emilio Cruciani; Francesco Pasquale; Sara Rizzo
• Keywords: Social and Information Networks (cs.SI); FOS: Computer and information sciences; Consensus; Opinion dynamics; Majority dynamics; Voter model; Social networks; Consensus; Markov chains; Markov chains; Majority dynamics; Agent theories and models; agent-based simulation and emergence; agent societies; 006; Computer Science - Social and Information Networks; 0102 computer and information sciences; 02 engineering and technology; Settore INF/01 - INFORMATICA; 01 natural sciences; Social networks; [INFO.INFO-MA]Computer Science [cs]/Multiagent Systems [cs.MA]; 0202 electrical engineering, electronic engineering, information engineering; Voter model; Computer Science - Multiagent Systems; [INFO.INFO-MA] Computer Science [cs]/Multiagent Systems [cs.MA]; Opinion dynamics; Multiagent Systems (cs.MA)

We study opinion dynamics in multi-agent networks when a bias toward one of two possible opinions exists, for example reflecting status quo versus superior alternative. Our aim is to investigate the combined effect bias, network structure, and on convergence system agents as whole. Models such evolving processes can easily become analytically intractable. In this paper, we consider simple yet mathematically rich setting, which all initially share an initial representing quo. The evolves steps. each step, agent selected uniformly at random follows underlying update rule revise its basis those held by neighbors, but with probabilistic towards analyze resulting process under well-known rules. framework propose modular, same time complex enough highlight nonobvious interplay between topology rule.