Information, incentives, and goals in election forecasts
Information, incentives, and goals in election forecasts
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DOI：
10.1017/s1930297500007981
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发表时间：
2020-09
影响因子：
2.5
通讯作者：
A. Gelman;J. Hullman;Christopher Wlezien;G. E. Morris
中科院分区：
文献类型：
--
作者：
A. Gelman;J. Hullman;Christopher Wlezien;G. E. Morris
总统选举可以使用来自政治和经济状况、民意调查以及民意随时间变化的统计模型的信息来预测。然而，由于评估预测校准和沟通的挑战，这些关于如何做出良好的总统选举预测的“已知”伴随着许多未知数。我们强调激励措施如何影响预测，特别是预测的不确定性，鉴于校准的挑战。我们以《经济学人》和FiveThirtyeight对2020年大选的预测为例，说明了在创建、传播和评估选举预测方面的这些挑战，并为预测者和学者提供了建议。
Presidential elections can be forecast using information from political and economic conditions, polls, and a statistical model of changes in public opinion over time. However, these “knowns” about how to make a good presidential election forecast come with many unknowns due to the challenges of evaluating forecast calibration and communication. We highlight how incentives may shape forecasts, and particularly forecast uncertainty, in light of calibration challenges. We illustrate these challenges in creating, communicating, and evaluating election predictions, using the Economist and Fivethirtyeight forecasts of the 2020 election as examples, and offer recommendations for forecasters and scholars.