arXiv 2412.024612024-12-03physics.soc-phnlin.AO

意见形成动力学模型中的选民人口统计特征与社会经济因素

Voter demographics and socio-economic factors in kinetic models for opinion formation

Bertram Düring, Oliver Wright

备注：: 20 pages. arXiv admin note: text overlap with arXiv:2107.05964

AI总结：

本文重新审视Düring和Wright（2022）的动力学意见形成模型，基于Toscani（2006）及Düring等人（2009）的模型，详细比较不同人口统计因素和初始条件的影响，并结合英国2024大选“红墙”地区情况与其他选举预测对比。

摘要：

选民人口统计特征和社会经济因素（如年龄、性别、种族、教育水平、收入）以及其他可测量因素（如以往选举或公投中的行为）在意见形成动力学建模中至关重要。本文重新审视了Düring和Wright（2022）提出的动力学意见形成模型，并更详细地比较了不同特征人口统计因素选择和初始条件的影响。该模型基于Toscani（2006）的动力学意见形成模型和Düring等人（2009）的领导者-追随者模型，后者推导出一个Fokker-Planck型偏微分方程组。在2024年英国大选前夕，我们在数值实验中考虑了英格兰北部和中部所谓“红墙”地区的情况，并将模拟结果与其他选举预测进行了比较。

英文摘要：

Voter demographics and socio-economic factors like age, sex, ethnicity, education level, income, and other measurable factors like behaviour in previous elections or referenda are of key importance in modelling opinion formation dynamics. Here, we revisit the kinetic opinion formation model from Düring and Wright (2022) and compare in more detail the influence of different choices of characteristic demographic factors and initial conditions. The model is based on the kinetic opinion formation model by Toscani (2006) and the leader-follower model of Düring et al. (2009) which leads to a system of Fokker-Planck-type partial differential equations. In the run-up to the 2024 general election in the United Kingdom, we consider in our numerical experiments the situation in the so-called `Red Wall' in North and Midlands of England and compare our simulation results to other election forecasts.

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