Name
Abstract | ||
|---|---|---|
We study opinion dynamics in multi-agent networks when a bias toward one of two possible opinions exists, for example reflecting a status quo versus a superior alternative. Our aim is to investigate the combined effect of bias, network structure, and opinion dynamics on the convergence of the system of agents as a whole. Models of such evolving processes can easily become analytically intractable. In this paper, we consider a simple yet mathematically rich setting, in which all agents initially share an initial opinion representing the status quo. The system evolves in steps. In each step, one agent selected uniformly at random follows an underlying update rule to revise its opinion on the basis of those held by its neighbors, but with a probabilistic bias towards the superior alternative. We analyze convergence of the resulting process under well-known update rules. The framework we propose is simple and modular, but at the same time complex enough to highlight a nonobvious interplay between topology and underlying update rule. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1016/j.ins.2022.01.072 | Information Sciences |
Keywords | DocType | Volume |
Opinion dynamics,Majority dynamics,Voter model,Social networks,Consensus,Markov chains | Journal | 593 |
ISSN | Citations | PageRank |
0020-0255 | 1 | 0.38 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Aris Anagnostopoulos | 1 | 1054 | 67.08 |
| Luca Becchetti | 2 | 945 | 55.75 |
| Emilio Cruciani | 3 | 14 | 4.30 |
| Francesco Pasquale | 4 | 421 | 28.22 |
| Sara Rizzo | 5 | 1 | 0.38 |