Inferring electoral individual behaviour from aggregated data is a very active research area, with ramifications in sociology and political science. A new approach based on linear programming is proposed to estimate voter transitions among parties (or candidates) between two elections. Compared to other linear and quadratic programming models previously published, our approach presents two important innovations. Firstly, it explicitly deals with new entries and exits in the election census without assuming unrealistic hypotheses, enabling a reasonable estimation of vote behaviour of young electors voting for the first time. Secondly, by exploiting the information contained in the model residuals, we develop a procedure to assess the uncertainty in the estimates. This significantly distinguishes our model from other published mathematical programming methods. The method is illustrated estimating the vote transfer matrix between the first and second rounds of the 2017 French presidential election and measuring its level of uncertainty. Likewise, compared to the most current alternatives based on ecological regression, our approach is considerably simpler and faster, and has provided reasonable results in all the actual elections to which it has been applied. Interested scholars can easily use our procedure with the aid of the R-function provided in the Supplemental Material.

中文翻译：

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评估从汇总数据估计的选民过渡的不确定性。2017年法国总统选举申请

从汇总数据中推断选举个人行为是一个非常活跃的研究领域，在社会学和政治学中都有影响。提出了一种基于线性规划的新方法来估计两次选举之间政党（或候选人）之间的选民转换。与之前发布的其他线性和二次规划模型相比，我们的方法提出了两个重要的创新。首先，它明确地处理了选举普查中的新进入和退出，而不假设不切实际的假设，从而能够对首次投票的年轻选民的投票行为进行合理估计。其次，通过利用模型残差中包含的信息，我们开发了一个程序来评估估计中的不确定性。这将我们的模型与其他已发表的数学规划方法显着区别开来。该方法说明了估计 2017 年法国总统选举第一轮和第二轮之间的选票转移矩阵并测量其不确定性水平。同样，与基于生态回归的最新替代方案相比，我们的方法更加简单和快捷，并且在所有实际应用的选举中都提供了合理的结果。有兴趣的学者可以借助补充材料中提供的 R 函数轻松使用我们的程序。与基于生态回归的最新替代方案相比，我们的方法更加简单和快捷，并且在应用它的所有实际选举中提供了合理的结果。有兴趣的学者可以借助补充材料中提供的 R 函数轻松使用我们的程序。与基于生态回归的最新替代方案相比，我们的方法更加简单和快捷，并且在应用它的所有实际选举中提供了合理的结果。有兴趣的学者可以借助补充材料中提供的 R 函数轻松使用我们的程序。