Diep Hung T, Kaufman Miron, Kaufman Sanda
Laboratoire de Physique Théorique et Modélisation, CY Cergy Paris Université, CNRS, UMR 8089 2, Avenue Adolphe Chauvin, 95302 Cergy-Pontoise, France.
Department of Physics, Cleveland State University, Cleveland, OH 44115, USA.
Entropy (Basel). 2023 Jun 27;25(7):981. doi: 10.3390/e25070981.
World-wide, political polarization continues unabated, undermining collective decision-making ability. In this issue, we have examined polarization dynamics using a (mean-field) model borrowed from statistical physics, assuming that each individual interacted with each of the others. We use the model to generate scenarios of polarization trends in time in the USA and explore ways to reduce it, as measured by a polarization index that we propose. Here, we extend our work using a more realistic assumption that individuals interact only with "neighbors" (short-range interactions). We use agent-based Monte Carlo simulations to generate polarization scenarios, considering again three USA political groups: Democrats, Republicans, and Independents. We find that mean-field and Monte Carlo simulation results are quite similar. The model can be applied to other political systems with similar polarization dynamics.
在全球范围内，政治两极分化持续加剧，削弱了集体决策能力。在本期中，我们使用从统计物理学借用的（平均场）模型研究了两极分化动态，假设每个个体都与其他个体相互作用。我们使用该模型生成美国两极分化趋势的时间情景，并探索减少两极分化的方法，这是通过我们提出的两极分化指数来衡量的。在这里，我们使用更现实的假设扩展我们的工作，即个体仅与“邻居”（短程相互作用）相互作用。我们使用基于主体的蒙特卡罗模拟来生成两极分化情景，再次考虑美国的三个政治群体：民主党人、共和党人和无党派人士。我们发现平均场和蒙特卡罗模拟结果非常相似。该模型可应用于具有类似两极分化动态的其他政治系统。
