意见模型、数据与政治
Opinion models, data, and politics
AI总结：
本文从玻尔兹曼分布和格劳伯动力学角度，将q-选民模型视为Zealot模型的扩展，并拟合四种意见模型到多国选举数据，发现弱效应强化模型表现最佳，为政治学提供了模型驱动分析工具。
摘要：
我们从玻尔兹曼分布和随机格劳伯动力学的视角，研究了Potts（Curie-Weiss）模型与随机意见模型之间的联系。我们特别发现，q-选民模型可以被视为Zealot模型的一种自然扩展，并通过拉格朗日参数进行调整。我们还讨论了这些模型的弱效应和强效应连续极限。随后，我们将四种模型（Curie-Weiss模型、q-选民模型的强效应和弱效应极限，以及强化模型）拟合到来自美国、英国、法国和德国的选举数据中。我们发现，尤其是弱效应模型能够拟合数据（通过Kolmogorov-Smirnov检验），其中弱效应强化模型表现最佳（根据AIC）。所得估计结果从政治学视角进行了解读，并讨论了这种基于模型的方法对选举数据在政治学中的重要性。
英文摘要：
We investigate the connection between Potts (Curie-Weiss) models and stochastic opinion models in the view of the Boltzmann distribution and stochastic Glauber dynamics. We particularly find that the q-voter model can be considered as a natural extension of the Zealot model which is adapted by Lagrangian parameters. We also discuss weak and strong effects continuum limits for the models. We then fit four models (Curie-Weiss, strong and weak effects limit for the q-voter model, and the reinforcement model) to election data from United States, United Kingdom, France and Germany. We find that particularly the weak effects models are able to fit the data (Kolmogorov-Smirnov test), where the weak effects reinforcement model performs best (AIC). The resulting estimates are interpreted in the view of political sciences, and also the importance of this kind of model-based approaches to election data for the political sciences is discussed.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
