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Contrarian Majority Rule Model with External Oscillating Propaganda and Individual Inertias
We study the Galam majority rule dynamics with contrarian behavior and an oscillating external propaganda in a population of agents that can adopt one of two possible opinions. In an iteration step, a random agent interacts with three other random agents and takes the majority opinion among the agen...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10606753/ https://www.ncbi.nlm.nih.gov/pubmed/37895523 http://dx.doi.org/10.3390/e25101402 |
Sumario: | We study the Galam majority rule dynamics with contrarian behavior and an oscillating external propaganda in a population of agents that can adopt one of two possible opinions. In an iteration step, a random agent interacts with three other random agents and takes the majority opinion among the agents with probability [Formula: see text] (majority behavior) or the opposite opinion with probability [Formula: see text] (contrarian behavior). The probability of following the majority rule [Formula: see text] varies with the temperature T and is coupled to a time-dependent oscillating field that mimics a mass media propaganda, in a way that agents are more likely to adopt the majority opinion when it is aligned with the sign of the field. We investigate the dynamics of this model on a complete graph and find various regimes as T is varied. A transition temperature [Formula: see text] separates a bimodal oscillatory regime for [Formula: see text] , where the population’s mean opinion m oscillates around a positive or a negative value from a unimodal oscillatory regime for [Formula: see text] in which m oscillates around zero. These regimes are characterized by the distribution of residence times that exhibit a unique peak for a resonance temperature [Formula: see text] , where the response of the system is maximum. An insight into these results is given by a mean-field approach, which also shows that [Formula: see text] and [Formula: see text] are closely related. |
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