Domain motion in the voter model with noise

Medeiros, N. G. F. de; Silva, Ana T.C.; Moreira, F. G. Brady

doi:10.1103/physreve.73.046120

Cited by 15 publications

(10 citation statements)

References 18 publications

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“…(8) stem from the harmonic functions in space χ of eqs. (3,4). For the long distance region, the poles will never dominate , neither at short times nor at asymptotically long times.…”

Section: Flux Continuitymentioning

confidence: 99%

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Assisted tunneling of a metastable state between barriers

Kälbermann

2008

Phys. Rev. C

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The assisted tunneling of a metastable state between barriers is investigated analytically by means of a simplified one dimensional model. A time dependent perturbation changes the pole spectrum of the wave function introducing a larger decay constant. New insights about the decay of a metastable state are found. The scheme is exemplified for parameters corresponding to the nuclear process of α decay.

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“…(8) stem from the harmonic functions in space χ of eqs. (3,4). For the long distance region, the poles will never dominate , neither at short times nor at asymptotically long times.…”

Section: Flux Continuitymentioning

confidence: 99%

“…[1,2] This framework has remained as reliable today as when it was proposed almost a century ago. [3,4] The question of assisting tunneling, i.e. accelerating or decelerating the decay process is of the utmost importance in the nuclear context and elsewhere.…”

Section: Introductionmentioning

confidence: 99%

Assisted tunneling of a metastable state between barriers

Kälbermann

2008

Phys. Rev. C

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“…This stochasticity of the decision‐making behavior could be due to many reasons, including mixed relationships or marriages between agents with different opinions or other factors of latent origin. These effects can be incorporated by introducing a certain amount of internal noise into the model 27 where, with some very low probability q at each iterative step, each agent switches to another randomly picked option. In our simulation, just as in Ref.…”

Section: Simulation Setupmentioning

confidence: 99%

“…Agent‐based simulations have stimulated much recent research and debate in sociophysics, 21 especially in the areas of language competition, 18,22 language death and survival, 23 self‐organization of social hierarchies, 24 evolution of social groups, 19 interethnic conflicts, 25 and opinion dynamics 10,12–15,26,27 . In studies of opinion dynamics, we want to know how different opinions of interacting agents percolate within a given community over many generations and whether this iterative process finally converges into the state of a consensus in one of the possible choices or, alternatively, into a state of polarization where different options can stably coexist.…”

Section: Models Of Opinion Dynamicsmentioning

confidence: 99%

Agent‐based Computer Simulations of Language Choice Dynamics

Hadzibeganovic

Stauffer

Schulze

2009

Annals of the New York Academy of Sciences

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We use agent-based Monte Carlo simulations to address the problem of language choice dynamics in a tripartite community that is linguistically homogeneous but politically divided. We observe the process of nonlocal pattern formation that causes populations to self-organize into stable antagonistic groups as a result of the local dynamics of attraction and influence between individual computational agents. Our findings uncover some of the unique properties of opinion formation in social groups when the process is affected by asymmetric noise distribution, unstable intergroup boundaries, and different migratory behaviors. Although we focus on one particular study, the proposed stochastic dynamic models can be easily generalized and applied to investigate the evolution of other complex and nonlinear features of human collective behavior.

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“…14 networks have been used to mediate opinion interaction. [15][16][17] Sociological and psychological features have also been introduced into opinion models, such as memory, 18 inertia, 19,20 noise, 21 and conviction, 22 characterizing the way in which these features change individual behavior and the global dynamics in a specific scenario.…”

Section: Introductionmentioning

confidence: 99%

Opinion formation on social media: An empirical approach

Xiong

Liu

2014

Chaos: An Interdisciplinary Journal of Nonlinear Science

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Opinion exchange models aim to describe the process of public opinion formation, seeking to uncover the intrinsic mechanism in social systems; however, the model results are seldom empirically justified using large-scale actual data. Online social media provide an abundance of data on opinion interaction, but the question of whether opinion models are suitable for characterizing opinion formation on social media still requires exploration. We collect a large amount of user interaction information from an actual social network, i.e., Twitter, and analyze the dynamic sentiments of users about different topics to investigate realistic opinion evolution. We find two nontrivial results from these data. First, public opinion often evolves to an ordered state in which one opinion predominates, but not to complete consensus. Second, agents are reluctant to change their opinions, and the distribution of the number of individual opinion changes follows a power law. Then, we suggest a model in which agents take external actions to express their internal opinions according to their activity. Conversely, individual actions can influence the activity and opinions of neighbors. The probability that an agent changes its opinion depends nonlinearly on the fraction of opponents who have taken an action. Simulation results show user action patterns and the evolution of public opinion in the model coincide with the empirical data. For different nonlinear parameters, the system may approach different regimes. A large decay in individual activity slows down the dynamics, but causes more ordering in the system. Opinion dynamics tries to describe the process of public opinion formation in social systems. Many opinion models have been presented that explore how the local individual behavior affects collective phenomena. However, those model results are seldom empirically justified using large-scale actual data, and whether traditional opinion models suitably describe online opinion interactions requires further exploration. We analyze users' opinions regarding a certain topic using large-scale actual data collected from a famous social network, i.e., Twitter, and discover two nontrivial results: first, consensus is difficult to achieve in a finite time and second, users seldom change their opinions, and the number of individual opinion changes decays as a power law. We present a discrete opinion model including agents' internal opinions and external actions that are determined by agents' activity. Agents' activity also evolves during the dynamics. Simulation results show our model can retrieve similar properties to those of actual data. We hope theoretical opinion models will be verified by actual data in different social systems so that they can better characterize actual social interactions. In the future, whether opinion models can predict the evolutionary trend of public opinion in actual situation will be investigated. This study will improve the applicability of research on opinion dynamics.

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Domain motion in the voter model with noise

Cited by 15 publications

References 18 publications

Assisted tunneling of a metastable state between barriers

Assisted tunneling of a metastable state between barriers

Agent‐based Computer Simulations of Language Choice Dynamics

Opinion formation on social media: An empirical approach

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