Forecasting the onset of genocide and politicide

Annual out-of-sample forecasts on a global dataset, 1988-2003

Datos Bibliográficos

ID3917810
AutoresBenjamin E Goldsmith (0000-0002-3247-3174, The University of Sydney, autor de correspondencia), Charles Butcher (0000-0003-2652-1229, University of Otago), Charles R Butcher (University of Otago), Dimitri Semenovich (UNSW Sydney), Arcot Sowmya (0000-0001-9236-5063, UNSW Sydney)
Año2013
Volumen50
Número4
Páginas437-452
Fecha de publicación2013-07-01
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaJournal of Peace Research (JOURNAL)
Identificadores de la revistaISSN: 0022-3433 • E-ISSN: 1460-3578
EditorialSAGE Publications Inc (PUBLISHER)
DOI10.1177/0022343313484167
OpenAlexW1995850706
IdiomaEN
Citas recibidas32
Referencias citadas37

We present what is, to the best of our knowledge, the first published set of annual out-of-sample forecasts of genocide and politicide based on a global dataset. Our goal is to produce a prototype for a real-time model capable of forecasting one year into the future. Building on the current literature, we take several important steps forward. We implement an unconditional two-stage model encompassing both instability and genocide, allowing our sample to be the available global data, rather than using conditional case selection or a case-control approach. We explore factors exhibiting considerable variance over time to improve yearly forecasting performance. And we produce annual lists of at-risk states in a format that should be of use to policymakers seeking to prevent such mass atrocities. Our out-of-sample forecasts for 1988-2003 predict 90.9% of genocide onsets correctly while also predicting 79.2% of non-onset years correctly, an improvement over a previous study using a case-control in-sample approach. We produce 16 annual forecasts based only on previous years' data, which identify six of 11 cases of genocide/politicide onset within the top 5% of at-risk countries per year. We believe this represents substantial progress towards useful real-time forecasting of such rare events. We conclude by suggesting ways to further enhance predictive performance

Control (management) · Econometrics · Economics · Genocide · Machine learning · Operations research · Political science · Sample (material) · Selection (genetic algorithm) · Set (abstract data type) · Variance (accounting) · Artificial Intelligence · Computer Science · Culture, Economy, and Development Studies · Engineering · Natural Resources and Economic Development · Political Conflict and Governance