Phase transitions in social impact models of opinion formation

Hołyst, Janusz A.; Kacperski, Krzysztof; Schweitzer, Frank

doi:10.1016/s0378-4371(00)00282-x

Cited by 133 publications

(144 citation statements)

References 24 publications

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“…On the other hand, starting with two neighboring kinks, we find η = 0.00(2), δ = 0.285(5), and z = 1.18(2). These results are consistent with the parity conservation universality class [3,4].…”

Section: Phase Transitions and Damage Spreading In The Lattice Casesupporting

confidence: 90%

“…Rule T23 is a strict majority rule, whose evolution starting from a random configuration leads to the formation of frozen patches of zeros and ones in a 3 The usual order parameter for magnetic system is the magnetization M = 2c − 1. few time steps. A small variation of the probabilities induces fluctuations in the position of the patches.…”

Section: A Simple Casementioning

confidence: 99%

“…a homogeneous democratic society. 1 It may be considered as the annealed version of a 1 The case with strong leader was studied in Ref [3]. …”

mentioning

confidence: 99%

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Opinion Formation and Phase Transitions in a Probabilistic Cellular Automaton with Two Absorbing States

Bagnoli

Franci

Rechtman

2002

Lecture Notes in Computer Science

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Abstract. We discuss the process of opinion formation in a completely homogeneous, democratic population using a class of probabilistic cellular automata models with two absorbing states. Each individual can have one of two opinions that can change according to that of his neighbors. It is dominated by an overwhelming majority and can disagree against a marginal one. We present the phase diagram in the mean field approximation and from numerical experiments for the simplest nontrivial case. For arbitrarily large neighborhoods we discuss the mean field results for a non-conformist society, where each individual adheres to the overwhelming majority of its neighbors and choses an opposite opinion in other cases. Mean field results and preliminary lattice simulations with long-range connections among individuals show the presence of coherent temporal oscillations of the population. Modeling social pressure and political transitionsWhat happens to a society when a large fraction of people switches from a conformist to a non-conformist attitude? Is the transition smooth or revolutionary? These important questions, whose answers can make the difference between two well-known political points of view, is approached using a theoretical model, in the spirit of Latané's social impact theory [1,2].We assume that one's own inclination towards political choices originates from a mixture of a certain degree of conformism and non-conformism. Conformists tend to agree with the local community majority, that is with the average opinion in a given neighborhood, while non-conformists do the opposite. However, an overwhelming majority in the neighborhood (which includes the subject itself) is always followed.We shall study here the case of a homogeneous population, i.e. a homogeneous democratic society. 1 It may be considered as the annealed version of a 1 The case with strong leader was studied in Ref [3].

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Section: Phase Transitions and Damage Spreading In The Lattice Casesupporting

confidence: 90%

Section: A Simple Casementioning

confidence: 99%

See 1 more Smart Citation

Opinion Formation and Phase Transitions in a Probabilistic Cellular Automaton with Two Absorbing States

Bagnoli

Franci

Rechtman

2002

Lecture Notes in Computer Science

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“…Identification of four distinct phases in opinion formation: this aspect is not entirely captured by existing models (Sznajd-Weron & Sznajd, 2000;Li et al, 2012;Acemoglu et al, 2013;Chen, Wang & Li, 2014;Guille et al, 2013;Fang, Zhang & Thalmann, 2013) although previous research (Hołyst, Kacperski & Schweitzer, 2000) has noticed that there are some stages in opinion evolution. We argue that the succession of opinion formation phases is critical to the social balancing phenomenon (i.e., the general opinion becomes stable despite constant local oscillations).…”

Section: Introductionmentioning

confidence: 98%

Tolerance-based interaction: a new model targeting opinion formation and diffusion in social networks

Topîrceanu

Udrescu

Vlăduţiu

et al. 2016

PeerJ Computer Science

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One of the main motivations behind social network analysis is the quest for understanding opinion formation and diffusion. Previous models have limitations, as they typically assume opinion interaction mechanisms based on thresholds which are either fixed or evolve according to a random process that is external to the social agent. Indeed, our empirical analysis on large real-world datasets such as Twitter, Meme Tracker, and Yelp, uncovers previously unaccounted for dynamic phenomena at population-level, namely the existence of distinct opinion formation phases and social balancing. We also reveal that a phase transition from an erratic behavior to social balancing can be triggered by network topology and by the ratio of opinion sources. Consequently, in order to build a model that properly accounts for these phenomena, we propose a new (individual-level) opinion interaction model based on tolerance. As opposed to the existing opinion interaction models, the new tolerance model assumes that individual's inner willingness to accept new opinions evolves over time according to basic human traits. Finally, by employing discrete event simulation on diverse social network topologies, we validate our opinion interaction model and show that, although the network size and opinion source ratio are important, the phase transition to social balancing is mainly fostered by the democratic structure of the small-world topology.

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“…The cellular automata with intrinsic disorder was later solved analytically in the continuous limit by Plewczynski [49], and proved that in the model of Cartesian social space (therefore not fully connected) and containing no learning rules, one can also observe different phases (small clusters in the sparse phase with large role of strong individuals, and high density phase with almost uniform opinion). The later results of Hołyst et al, where numerical simulations and analytical models were tested in simplified geometries, proved the usefulness of mean-field formalism in describing the social impact theory, and the presence of the equilibrium states of the system with complex intermittent behavior [50][51][52][53].…”

Section: In Nt Tr Ro Od Du Uc Ct Ti Io On Nmentioning

confidence: 93%

Mean-Field Theory of Meta-learning

SpringerReference

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A Ab bs st tr ra ac ct tWe discuss here the mean-field theory for a cellular automata model of meta-learning. The metalearning is the process of combining outcomes of individual learning procedures in order to determine the final decision with higher accuracy than any single learning method. Our method is constructed from an ensemble of interacting, learning agents, that acquire and process incoming information using various types, or different versions of machine learning algorithms. The abstract learning space, where all agents are located, is constructed here using a fully connected model that couples all agents with random strength values. The cellular automata network simulates the higher level integration of information acquired from the independent learning trials. The final classification of incoming input data is therefore defined as the stationary state of the meta-learning system using simple majority rule, yet the minority clusters that share opposite classification outcome can be observed in the system. Therefore, the probability of selecting proper class for a given input data, can be estimated even without the prior knowledge of its affiliation. The fuzzy logic can be easily introduced into the system, even if learning agents are build from simple binary classification machine learning algorithms by calculating the percentage of agreeing agents.

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Phase transitions in social impact models of opinion formation

Cited by 133 publications

References 24 publications

Opinion Formation and Phase Transitions in a Probabilistic Cellular Automaton with Two Absorbing States

Opinion Formation and Phase Transitions in a Probabilistic Cellular Automaton with Two Absorbing States

Tolerance-based interaction: a new model targeting opinion formation and diffusion in social networks

Mean-Field Theory of Meta-learning

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