The Model Challenges: Gathering, evaluating, and aggregating Covid mortality models

Co-authored with Tara Slough et al.

In 2020, shortly after the onset of the global Covid-19 pandemic, we devised a set of Model Challenges to explore what our discipline’s inherited knowledge had to offer to accurately model (and understand) how governments would respond to the pandemic. Set up as a crowdsourced tournament, we first gathered models of political drivers of Covid mortality and then asked experts to assess which models would perform well or poorly. In this paper, we describe and take stock of this large scale collective effort. Our analysis suggests three main conclusions. First, the ability of political scientists to predict or explain which polities will react effectively to the pandemic and which not appears very limited. Second, even when our models are relatively successful, as a discipline we are not good at telling apart effective and ineffective models. Third, the best results are generated when models are combined. On the basis of these findings, we suggest that our discipline would benefit from the establishment of continued structured interactions that encourage multiple perspectives on phenomena of common interest and that aggregate ideas across researchers. As a discipline, we should sharpen our theories by seeking to predict future events instead of predicting the past.

Covid_2025.pdf

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