KOALA：选举报道的新范式
KOALA: A new paradigm for election coverage
摘要：
普通选举民意调查报告往往具有误导性，因为样本的不确定性没有得到充分解决，或者根本没有被报道。此外，主要利益通常不限于简单的当事人股份。为了更全面地报道民意调查和选举情况，我们建议把重点转移到报道特定事件的调查概率。我们提出了这样一种方法，多党选举制度，集中在联盟多数的概率。采用基于贝叶斯多项狄利克雷模型的蒙特卡罗方法进行估计。概率是估计的，假设选举是今天举行的（“现在铸造”），不考虑潜在的变化，在选民，直到选举日“预测”。由于我们的方法是基于党的份额的后验分布，该方法可以用来回答各种问题的选举结果。我们还介绍了可视化技术，方便更充分地描述相关的数量以及各自的不确定性。我们的方法的好处进行了讨论，应用于德国联邦选举在2013年和2017年。我们的方法的开源实现可以在R包联盟中免费获得。
英文摘要：
Common election poll reporting is often misleading as sample uncertainty is addressed insufficiently or not covered at all. Furthermore, main interest usually lies beyond the simple party shares. For a more comprehensive opinion poll and election coverage, we propose shifting the focus towards the reporting of survey-based probabilities for specific events of interest. We present such an approach for multi-party electoral systems, focusing on probabilities of coalition majorities. A Monte Carlo approach based on a Bayesian Multinomial-Dirichlet model is used for estimation. Probabilities are estimated, assuming the election was held today (''now-cast''), not accounting for potential shifts in the electorate until election day ''fore-cast''. Since our method is based on the posterior distribution of party shares, the approach can be used to answer a variety of questions related to the outcome of an election. We also introduce visualization techniques that facilitate a more adequate depiction of relevant quantities as well as respective uncertainties. The benefits of our approach are discussed by application to the German federal elections in 2013 and 2017. An open source implementation of our methods is freely available in the R package coalitions.
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