Center for Complex Decision Analysis, Fudan University, Shanghai, China.
MOE Laboratory for National Development and Intelligent Governance, Fudan University, Shanghai, China.
PLoS One. 2022 Jun 30;17(6):e0270194. doi: 10.1371/journal.pone.0270194. eCollection 2022.
Election forecasting has been traditionally dominated by subjective surveys and polls or methods centered upon them. We have developed a novel platform for forecasting elections based on agent-based modeling (ABM), which is entirely independent from surveys and polls. The platform uses statistical results from objective data along with simulation models to capture how voters have voted in past elections and how they are likely to vote in an upcoming election. We screen for models that can reproduce results that are very close to the actual results of historical elections and then deploy these selected models to forecast an upcoming election with simulations by combining extrapolated data from historical demographic record and more updated data on economic growth, employment, shock events, and other factors. Here, we report the results of two recent experiments of real-time election forecasting: the 2020 general election in Taiwan and six states in the 2020 general election in the United States. Our mostly objective method using ABM may transform how elections are forecasted and studied.
选举预测传统上主要由主观调查和民意测验或以此为中心的方法主导。我们开发了一个基于基于代理的建模（ABM）的选举预测新平台，该平台完全独立于调查和民意测验。该平台使用客观数据的统计结果和模拟模型来捕获选民在过去选举中的投票方式以及他们在即将举行的选举中可能的投票方式。我们筛选出可以复制非常接近历史选举实际结果的结果的模型，然后通过结合历史人口统计记录的外推数据和有关经济增长、就业、冲击事件和其他因素的最新数据，使用这些选定的模型进行模拟来预测即将举行的选举。在这里，我们报告了两个最近的实时选举预测实验的结果：2020 年台湾地区的大选和 2020 年美国六个州的大选。我们使用 ABM 的这种主要是客观的方法可能会改变选举预测和研究的方式。
