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- Grossmann, Igor, et al.
(författare)
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Insights into the accuracy of social scientists' forecasts of societal change
- 2023
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Ingår i: Nature Human Behaviour. - : Springer Nature. - 2397-3374. ; 7, s. 484-501
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Tidskriftsartikel (refereegranskat)abstract
- How well can social scientists predict societal change, and what processes underlie their predictions? To answer these questions, we ran two forecasting tournaments testing the accuracy of predictions of societal change in domains commonly studied in the social sciences: ideological preferences, political polarization, life satisfaction, sentiment on social media, and gender-career and racial bias. After we provided them with historical trend data on the relevant domain, social scientists submitted pre-registered monthly forecasts for a year (Tournament 1; N = 86 teams and 359 forecasts), with an opportunity to update forecasts on the basis of new data six months later (Tournament 2; N = 120 teams and 546 forecasts). Benchmarking forecasting accuracy revealed that social scientists' forecasts were on average no more accurate than those of simple statistical models (historical means, random walks or linear regressions) or the aggregate forecasts of a sample from the general public (N = 802). However, scientists were more accurate if they had scientific expertise in a prediction domain, were interdisciplinary, used simpler models and based predictions on prior data. How accurate are social scientists in predicting societal change, and what processes underlie their predictions? Grossmann et al. report the findings of two forecasting tournaments. Social scientists' forecasts were on average no more accurate than those of simple statistical models.
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| 2. |
- Halkia, Matina, et al.
(författare)
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The Global Conflict Risk Index : A quantitative tool for policy support on conflict prevention
- 2020
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Ingår i: Progress in Disaster Science. - : Elsevier BV. - 2590-0617. ; 6
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Tidskriftsartikel (refereegranskat)abstract
- In an effort to bridge the gap between academic and governmental initiatives on quantitative conflict modelling, this article presents, validates and discusses the Global Conflict Risk Index (GCRI), the quantitative starting point of the European Union Conflict Early Warning System. Based on open-source data of five risk areas representing the structural conditions characterising a given country (political, economic, social, environmental and security areas), it evaluates the risk of violent conflict in the next one to four years. Using logistic regression, the GCRI calculates the probability of national and subnational conflict risk. Several model design decisions, including definition of the dependent variable, predictor variable selection, data imputation, and probability threshold definition, are tested and discussed in light of the model's direct application in the EU policy support on conflict prevention. While the GCRI remains firmly rooted by its conception and development in the European conflict prevention policy agenda, it is validated as a scientifically robust and rigorous method for a baseline quantitative evaluation of armed conflict risk. Despite its standard, simple methodology, the model predicts better than six other published quantitative conflict early warning systems for ten out of twelve reported performance metrics. Thereby, this article aims to contribute to a cross-fertilisation of academic and governmental efforts in quantitative conflict risk modelling.
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