KOALA: A new paradigm for election coverage

TL;DR

This paper introduces KOALA, a Bayesian Monte Carlo method for election coverage that estimates probabilities of coalition majorities, providing a more comprehensive and uncertainty-aware alternative to traditional poll reporting.

Contribution

It presents a novel Bayesian approach for election probability estimation focusing on coalition outcomes, with visualization techniques and open-source implementation.

Findings

Applied to German elections 2013 and 2017, demonstrating practical utility.

Provides more nuanced election insights by quantifying uncertainties.

Enhances election coverage with probabilistic and visual tools.

Abstract

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.