检验多种意见动力学模型的有效性
Testing the validity of multiple opinion dynamics models
AI总结：
本文通过实证数据验证了多种意见动力学模型的有效性，发现大多数模型在预测未来数据时表现不佳，指出需进一步研究提升模型有效性的特征。
摘要：
尽管意见动力学模型已被广泛研究作为简化模型，但将这些模型与实证数据结合的可能性正受到越来越多的关注。这种关注似乎是由许多社会问题驱动的，这些问题强烈依赖于人们的观点（如气候变化和疫苗接种），以及需要实证有效模型来设计相关政策干预措施的需求。尽管不同的模型以各种方式与实证数据结合，但标准化的模型与实证数据比较仍然缺乏。在本文中，我们测试了多种意见动力学模型的有效性，包括简化和更现实的模型。我们的方法遵循一种“数据科学式”的验证程序，首先使用初始时间段（例如2010-2015年）校准模型的自由参数，然后使用某一波数据（例如2016年）预测后续波的数据（例如2017年）。我们最初使用模拟数据测试了这种程序，然后在欧洲社会调查的不同主题上测试了不同的模型。玩具模型和实证模型在模拟数据上表现良好，但在实证数据中无法预测未来的年份。此外，在对实证数据进行校准阶段时，大多数模型学会了“冻结”——意味着它们对下一年的预测只是复制前一年的数据。这项工作通过提供一个比较不同意见动力学模型的基准，推动了文献的发展。此外，我们的测试表明，现实世界动态似乎与所测试模型的动力学完全不兼容。这要求更多努力探索哪些特征可以提高意见动力学模型的有效性和应用。
英文摘要：
While opinion dynamics models have been extensively studied as stylized models, there has been growing attention to the possibility of combining these models with empirical data. This attention seems to be driven by the many social issues that strongly depend on people's opinions (such as climate change and vaccination) and the need for empirically valid models to design related policy interventions. While different models have been combined in various ways with empirical data, standardised comparison of models against empirical data is still lacking. In this article, we test the validity of multiple opinion dynamics models--including both stylized and more realistic models. Our approach follows a "data science-like" validation procedure, where we first calibrate the model's free parameters using an initial range of years (e.g. 2010-2015), and then use data from one wave (e.g. 2016) to predict data in the following wave (e.g. 2017). We initially tested such a procedure using simulated data and then tested different models on various topics from the European Social Survey. Both toy models and empirical models perform well on the simulated data, but fail to predict future years in the empirical data. Furthermore, during the calibration phase on the empirical data, most models learned to "freeze"--meaning that their predictions for the following year are just a copy of the data from the previous year. This work advances the literature by offering a benchmark for comparing different opinion dynamics models. Furthermore, our tests show that real-world dynamics appear to be completely incompatible with the dynamics of the tested models. This calls for more effort in exploring what are the features that would improve validity and applications for opinion dynamics models.
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