From academia to policy makers: a methodology for real-time forecasting of infrequent events

Alfred Krzywicki (), David Muchlinski (), Benjamin E. Goldsmith () and Arcot Sowmya ()
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Alfred Krzywicki: UNSW Sydney
David Muchlinski: Georgia Institute of Technology
Benjamin E. Goldsmith: Australian National University
Arcot Sowmya: UNSW Sydney

Journal of Computational Social Science, 2022, vol. 5, issue 2, No 14, 1489-1510

Abstract: Abstract The field of conflict forecasting has matured greatly over the last decade. Advances in machine learning have allowed researchers to forecast rare political and social events in near real time. Yet the maturity of the field has led to a proliferation of diverse platforms for forecasting, divergent results across forecasts, and an explosion of forecasting methodologies. While the field has done much to establish some baseline results, true, consensual benchmarks against which future forecasts may be evaluated remain elusive, and thus, agreed upon empirical results are still rare. The aim of this work is to address these concerns and provide the field of conflict forecasting with a standardized analysis pipeline to evaluate future forecasts of political violence. We aim to open the black box of the conflict forecasting pipeline and provide empirical evidence on how modeling decisions along all steps of the pipeline affect end results. In this way, we empirically demonstrate best practices that conflict forecasting researchers may utilize in future endeavors. We employ forecasts of targeted mass killings and genocides to support our methodological claims.

Keywords: Machine learning; High-dimensional data; Data imbalance; Methodology (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s42001-022-00176-6

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