Opinion fluctuations and persistent disagreement in social networks
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Acemoglu_Opinion Fluctuations and Disagreement in Social Ne.pdf
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Author(s)
Acemoglu, Daron
Como, Giacomo
Fagnani, Fabio
Ozdaglar, Asuman E.
Date Issued
December 2011
Journal
Proceedings of the 2011 50th IEEE Conference on Decision and Control and European Control Conference (CDC-ECC)
Publisher
Institute of Electrical and Electronics Engineers
Citation
Acemoglu, Daron, Giacomo Como, Fabio Fagnani, and Asuman Ozdaglar. “Opinion fluctuations and persistent disagreement in social networks.” In IEEE Conference on Decision and Control and European Control Conference, 12-15 Dec. 2011, Orlando, FLa. 2347-2352. Institute of Electrical and Electronics Engineers, 2011.
Version
Author's final manuscript
Abstract
We study a tractable opinion dynamics model that generates long-run disagreements and persistent opinion fluctuations. Our model involves an inhomogeneous stochastic gossip process of continuous opinion dynamics in a society consisting of two types of agents: regular agents, who update their beliefs according to information that they receive from their social neighbors; and stubborn agents, who never update their opinions and might represent leaders, political parties or media sources attempting to influence the beliefs in the rest of the society. When the society contains stubborn agents with different opinions, the belief dynamics never lead to a consensus (among the regular agents). Instead, beliefs in the society almost surely fail to converge, the belief profile keeps on oscillating in an ergodic fashion, and it converges in law to a non-degenerate random vector. The structure of the graph describing the social network and the location of the stubborn agents within it shape the opinion dynamics. The expected belief vector is proved to evolve according to an ordinary differential equation coinciding with the Kolmogorov backward equation of a continuous-time Markov chain on the graph with absorbing states corresponding to the stubborn agents, and hence to converge to a harmonic vector, with every regular agent’s value being the weighted average of its neighbors’ values, and boundary conditions corresponding to the stubborn agents’ beliefs. Expected cross-products of the agents’ beliefs allow for a similar characterization in terms of coupled Markov chains on the graph describing the social network. We prove that, in large-scale societies which are highly fluid, meaning that the product of the mixing time of the Markov chain on the graph describing the social network and the relative size of the linkages to stubborn agents vanishes as the population size grows large, a condition of homogeneous influence emerges, whereby the stationary beliefs’ marginal distributions of most of the regular agents have approximately equal first and second moment.
MIT Department
Massachusetts Institute of Technology. Department of Economics
Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
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Creative Commons Attribution-Noncommercial-Share Alike 3.0
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DOI of Published Version
https://doi.org/10.1109/CDC.2011.6161319
