Social consensus through the influence of committed minorities

By Jierui Xie, S. Sreenivasan, G. Korniss, Weituo Zhang, C. Lim, Boleslaw K. Szymanski. Physical review. E, Statistical, nonlinear, and soft matter physics, 2011

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We show how the prevailing majority opinion in a population can be rapidly reversed by a small fraction p of randomly distributed committed agents who consistently proselytize the opposing opinion and are immune to influence. Specifically, we show that when the committed fraction grows beyond a critical value p(c) ≈ 10%, there is a dramatic decrease in the time T(c) taken for the entire population to adopt the committed opinion. In particular, for complete graphs we show that when p < pc, T(c) ~ exp [α(p)N], whereas for p>p(c), T(c) ~ ln N. We conclude with simulation results for Erdős-Rényi random graphs and scale-free networks which show qualitatively similar behavior.

Subfields: Sociology and Political Science, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/8ead8db8717e80d0d3d37c68a68b1959da79f8bf

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Citations in the corpus

Cognitive perspectives on opinion dynamics: the role of knowledge in consensus formation, opinion divergence, and group polarization (2017, background): “minorities [63], individual inflexibility [64], close-minded agents [65], narrowminded agents [61, 62], extremists [14, 16, 18, 66, 67], and stubborn agents or actors [68–70].”
Modeling Radicalization Phenomena in Heterogeneous Populations (2015, result): “ork datasets [39{41]. Opinion dynamics allows to analyze and to model the spreading of ideas, opinions, and feelings by reducing the study of complex social scenarios to the analysis of few variables [42]. Even terrorism and criminal activities may be studied by the same approach, i.e., reducing the”
Network resilience (2020, background): “[497] for the given size N and the given average degree 〈k〉.”
Control of multistability (2014, background): “A simple example of bistability in social competition is the naming game [130], where two words compete across an intermediate state and agents accept both words [131].”
The structure and dynamics of multilayer networks (2014, result): “Nevertheless, small perturbations could break the existing equilibrium, and a small minority of committed agents can alter the election outcome, which is consistent with recent claims [325].”
Modelling dynamical processes in complex socio-technical systems (2011, methodology): “This computational approach is also helping to guide researchers in identifying typical nonlinear behaviour and tipping points 100 not accessible by analytical means, using the numericalsimulationsasanovelexperimentalworkbench 101,102 .”
Opinion Dynamics and the Evolution of Social Power in Influence Networks (2015, background): “Significant attention [2] [8] [35] [59] [76] has focused on opinion dynamic processes with exogenous inputs as models for Bayesian and non-Bayesian learning in networks.”
On truth discovery in social sensing: A maximum likelihood estimation approach (2012, background): “Recent research attempts to understand the fundamental factors that affect the behavior of these emerging social sensing applications, such as analysis of characteristics of social networks [10], information propagation [17] and tipping points [32].”