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Learning from the past and Stepping into the Future : the next Generation of Crisis Prediction
| Content Provider | Semantic Scholar |
|---|---|
| Author | Ward, Michael D. Metternich, Nils W. Dorff, Cassy L. Gallop, Max Hollenbach, Florian Max Benjamin Schultz, Anna Weschle, Simon |
| Copyright Year | 2012 |
| Abstract | Developing political forecasting models not only increases the ability of political scientists to inform public policy decisions, but is also relevant for scientific advancement. As part of a larger project, a team at Duke University created a series of geographically informed statistical models for conflict prediction. The generated predictions have been highly accurate, with few false negative and positive categorizations. Predictions are made at the monthly level for six months periods into the future, taking into account the social-spatial context of each individual country. The model has a high degree of accuracy in reproducing historical data measured monthly over the past 10 years, and is approximately equally accurate in making forecasts. This paper surveys the notion of forecasting and demonstrates the utility of creating forecasting models for predicting political conflicts in a diverse range of country settings. Apart from the benefit of making actual predictions, we argue that predictive heuristics are one gold standard of model development in the field of conflict studies and that the predictive heuristics shed light on an array of important components of the political science literature on conflict dynamics. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | https://pureportal.strath.ac.uk/files-asset/54161029/Ward_etal_ISR_2013_toward_a_new_generation_of_conflict_prediction.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
