Explainable models for forecasting the emergence of political instability.

Building on previous research on the use of macroeconomic factors for conflict prediction and using data on political instability provided by the Political Instability Task Force, this article proposes two minimal forecasting models of political instability optimised to have the greatest possible pr...

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Autores principales:	Emma Baillie, Piers D L Howe, Andrew Perfors, Tim Miller, Yoshihisa Kashima, Andreas Beger
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/1ad8c938827e49c4af8e9f3d5043bc9f
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spelling	oai:doaj.org-article:1ad8c938827e49c4af8e9f3d5043bc9f2021-12-02T20:08:59ZExplainable models for forecasting the emergence of political instability.1932-620310.1371/journal.pone.0254350https://doaj.org/article/1ad8c938827e49c4af8e9f3d5043bc9f2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0254350https://doaj.org/toc/1932-6203Building on previous research on the use of macroeconomic factors for conflict prediction and using data on political instability provided by the Political Instability Task Force, this article proposes two minimal forecasting models of political instability optimised to have the greatest possible predictive power for one-year and two-year event horizons, while still making predictions that are fully explainable. Both models employ logistic regression and use just three predictors: polity code (a measure of government type), infant mortality, and years of stability (i.e., years since the last instability event). These models make predictions for 176 countries on a country-year basis and achieve AUPRC's of 0.108 and 0.115 for the one-year and two-year models respectively. They use public data with ongoing availability so are readily reproducible. They use Monte Carlo simulations to construct confidence intervals for their predictions and are validated by testing their predictions for a set of reference years separate from the set of reference years used to train them. This validation shows that the models are not overfitted but suggests that some of the previous models in the literature may have been. The models developed in this article are able to explain their predictions by showing, for a given prediction, which predictors were the most influential and by using counterfactuals to show how the predictions would have been altered had these predictors taken different values. These models are compared to models created by lasso regression and it is shown that they have at least as good predictive power but that their predictions can be more readily explained. Because policy makers are more likely to be influenced by models whose predictions can explained, the more interpretable a model is the more likely it is to influence policy.Emma BailliePiers D L HoweAndrew PerforsTim MillerYoshihisa KashimaAndreas BegerPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 7, p e0254350 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Emma Baillie Piers D L Howe Andrew Perfors Tim Miller Yoshihisa Kashima Andreas Beger Explainable models for forecasting the emergence of political instability.
description	Building on previous research on the use of macroeconomic factors for conflict prediction and using data on political instability provided by the Political Instability Task Force, this article proposes two minimal forecasting models of political instability optimised to have the greatest possible predictive power for one-year and two-year event horizons, while still making predictions that are fully explainable. Both models employ logistic regression and use just three predictors: polity code (a measure of government type), infant mortality, and years of stability (i.e., years since the last instability event). These models make predictions for 176 countries on a country-year basis and achieve AUPRC's of 0.108 and 0.115 for the one-year and two-year models respectively. They use public data with ongoing availability so are readily reproducible. They use Monte Carlo simulations to construct confidence intervals for their predictions and are validated by testing their predictions for a set of reference years separate from the set of reference years used to train them. This validation shows that the models are not overfitted but suggests that some of the previous models in the literature may have been. The models developed in this article are able to explain their predictions by showing, for a given prediction, which predictors were the most influential and by using counterfactuals to show how the predictions would have been altered had these predictors taken different values. These models are compared to models created by lasso regression and it is shown that they have at least as good predictive power but that their predictions can be more readily explained. Because policy makers are more likely to be influenced by models whose predictions can explained, the more interpretable a model is the more likely it is to influence policy.
format	article
author	Emma Baillie Piers D L Howe Andrew Perfors Tim Miller Yoshihisa Kashima Andreas Beger
author_facet	Emma Baillie Piers D L Howe Andrew Perfors Tim Miller Yoshihisa Kashima Andreas Beger
author_sort	Emma Baillie
title	Explainable models for forecasting the emergence of political instability.
title_short	Explainable models for forecasting the emergence of political instability.
title_full	Explainable models for forecasting the emergence of political instability.
title_fullStr	Explainable models for forecasting the emergence of political instability.
title_full_unstemmed	Explainable models for forecasting the emergence of political instability.
title_sort	explainable models for forecasting the emergence of political instability.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/1ad8c938827e49c4af8e9f3d5043bc9f
work_keys_str_mv	AT emmabaillie explainablemodelsforforecastingtheemergenceofpoliticalinstability AT piersdlhowe explainablemodelsforforecastingtheemergenceofpoliticalinstability AT andrewperfors explainablemodelsforforecastingtheemergenceofpoliticalinstability AT timmiller explainablemodelsforforecastingtheemergenceofpoliticalinstability AT yoshihisakashima explainablemodelsforforecastingtheemergenceofpoliticalinstability AT andreasbeger explainablemodelsforforecastingtheemergenceofpoliticalinstability
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Explainable models for forecasting the emergence of political instability.

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