Sociophysics: A new approach of sociological collective behaviour. I. mean‐behaviour description of a strike

Galam, Serge; Gefen, Yuval; Shapir, Yonathan

doi:10.1080/0022250x.1982.9989929

Cited by 387 publications

(269 citation statements)

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“…2 In Sec. 3 we present the simplest case where each individual interacts with his two nearest neighbors (r = 1), the mean field phase diagram and the one found from numerical experiments.…”

Section: Modeling Social Pressure and Political Transitionsmentioning

confidence: 99%

Section: Modeling Social Pressure and Political Transitionsmentioning

confidence: 99%

“…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.…”

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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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“…2 In Sec. 3 we present the simplest case where each individual interacts with his two nearest neighbors (r = 1), the mean field phase diagram and the one found from numerical experiments.…”

Section: Modeling Social Pressure and Political Transitionsmentioning

confidence: 99%

Section: Modeling Social Pressure and Political Transitionsmentioning

confidence: 99%

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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“…In Opinion Dynamics problems [10,11,12,13,14,15,16], a framework based on Bayesian methods has been proposed [17] as an extension of the ideas presented in the Continuous Opinions and Discrete Actions (CODA) model [15,18]. It is interesting to notice that this framework is able to generate many of the other Opinion Dynamics models, both discrete [19] and continuous [26], as particular or limit cases.…”

Section: Introductionmentioning

confidence: 99%

Opinion particles: Classical physics and opinion dynamics

Martins

2015

Physics Letters A

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A model for Opinion Particles, based on Bayesian-inspired models of Opinion Dynamics such as the CODA model is presented. By extending the discrete time characteristic of those models to continuous time, a theory for the movement of opinion particles is obtained, based only on inference ideas. This will allow inertia to be obtained as a consequence of an extended CODA model. For the general case, we will see that the likelihoods are associated with variables such as velocity and acceleration of the particles. Newtonian forces are easily defined and the relationship between a force and the equivalent likelihood provided. The case of the harmonic oscillator is solved as an example, to illustrate clearly the relationship between Opinion Particles and Mechanics. Finally, possible paths to apply these results to General Relativity are debated.

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“…Opinion dynamics is one of the social problems well-studied by physicists based on the famous Ising model from three decades ago [4]. Since then, quite a few opinion models have been proposed, such as the voter model [5,6], the Sznajd model [7][8][9][10][11][12][13] and the bounded confidence model [14][15][16], to name a few.…”

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Binary opinion dynamics with noise on random networks

Chi

2011

Chin. Sci. Bull.

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Two kinds of noise strategies in binary opinion dynamics on ER random networks are discussed. Random noise p 1 in the initial configuration plays a role in redistributing the opinion states associated with the network. Under synchronous updating, the system can attain a stable state within few time steps. The fraction of nodes with changed opinion states F decreases exponentially with time, and the ratio of one of the two opinion states R remains almost unchanged during the evolution. The average ratio crosses at the half-half initial concentration under different p 1 . For noise in the dynamical evolution p 2 , the system can reach a steady state with small fluctuations. With larger p 2 , more nodes have changed opinion states at each updating and more nodes with opposite opinions coexist. If p 2 is greater than 0.5, the two opinions coexist with equal support. binary opinion dynamics, random noise, complex networks Citation:Chi L P. Binary opinion dynamics with noise on random networks. Chinese Sci Bull, 2011, 56: 36303632, doi: 10.1007/s11434-011-4751-1In recent years, much effort has been invested in social dynamics formulated with concepts and methods from statistical physics [1][2][3]. Opinion dynamics is one of the social problems well-studied by physicists based on the famous Ising model from three decades ago [4]. Since then, quite a few opinion models have been proposed, such as the voter model [5,6], the Sznajd model [7][8][9][10][11][12][13] and the bounded confidence model [14][15][16], to name a few. In most of these models, each of N agents is assigned a finite number of available states of opinions. Opinion formation is modeled as a collective behavior of agents in which individuals evolve following either majority rule or imitation. In a binary opinion dynamics, two competing states, +1 or 1, are considered. Despite the complex dynamics of opinions among agents, the system attains total consensus of one of the two contrasting opinions, or a steady state with an equal distribution of opinions. The growing field of complex networks [17][18][19][20][21][22] enables us to obtain a better knowledge of social systems. The intense theoretical research currently taking place, examines the systems of nodes representing agents and links representing the interactions between them. In the random network proposed by Erdos and Renyi (ER), N nodes are connected by n edges selected randomly from the N(N1)/2 possible edges; the connection probability is p = n/N(N1)/2. The number k of edges connecting one node to others is called the degree of that node. The average degree of the random network is =2n/N = p(N1)≈pN if p<<1.The aim of our model in this paper is to explore the evolution and to determine the final state of the binary opinion dynamics with noise. The opinion dynamics is performed on the ER random network with nodes N = 10 5 and average degree =4. In the initial configuration, each node is assigned an opinion 1 with probability f and +1 with probability 1f. In this binary opinion dynam...

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Sociophysics: A new approach of sociological collective behaviour. I. mean‐behaviour description of a strike

Cited by 387 publications

References 3 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

Opinion particles: Classical physics and opinion dynamics

Binary opinion dynamics with noise on random networks

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