Self-exciting point process models for political conflict forecasting

In 2008, the Defense Advanced Research Project Agency commissioned a database known as the Integrated Crisis Early Warning System to serve as the foundation for models capable of detecting and predicting increases in political conflict worldwide. Such models, by signalling expected increases in political conflict, would help inform and prepare policymakers to react accordingly to conflict proliferation both domestically and internationally. Using data from the Integrated Crisis Early Warning System, we construct and test a self-exciting point process, or Hawkes process, model to describe and predict amounts of domestic, political conflict; we focus on Colombia and Venezuela as examples for this model. By comparing the accuracy of fitted models to the observed data, we find that we are able to closely describe occurrences of conflict in each country. Thus, using this model can allow policymakers to anticipate relative increases in the amount of domestic political conflict following major events.

[1]  Hannah Fry,et al.  Spatio-temporal patterns of IED usage by the Provisional Irish Republican Army , 2016, European Journal of Applied Mathematics.

[2]  Mark P. Racette,et al.  Improving situational awareness for humanitarian logistics through predictive modeling , 2014, 2014 Systems and Information Engineering Design Symposium (SIEDS).

[3]  Gnana Bharathy,et al.  Holistically evaluating agent-based social systems models: a case study , 2013, Simul..

[4]  Ralph Weischedel,et al.  Automatic Extraction of Events from Open Source Text for Predictive Forecasting , 2013 .

[5]  Sean P. O'Brien A Multi-Method Approach for Near Real Time Conflict and Crisis Early Warning , 2013 .

[6]  Nils W. Metternich,et al.  Geographical models of crises: Evidence from ICEWS , 2012 .

[7]  Scott Atran,et al.  Religious and Sacred Imperatives in Human Conflict , 2012, Science.

[8]  Erik A. Lewis,et al.  Self-exciting point process models of civilian deaths in Iraq , 2011, Security Journal.

[9]  Suzanne M. Mahoney,et al.  Aggregating Forecasts Using a Learned Bayesian Network , 2011, FLAIRS.

[10]  George E. Tita,et al.  Self-Exciting Point Process Modeling of Crime , 2011 .

[11]  Patrick T. Brandt,et al.  Real Time, Time Series Forecasting of Inter- and Intra-State Political Conflict , 2011 .

[12]  J. Neuman,et al.  Statistical and Stochastic Modeling of Gang Rivalries in Los Angeles , 2010 .

[13]  Philip A. Schrodt,et al.  Conflict and Mediation Event Observations (CAMEO): An event data framework for a post-Cold War world , 2008 .

[14]  Philip A. Schrodt,et al.  The CAMEO (Conflict and Mediation Event Observations) Actor Coding Framework , 2008 .

[15]  S. Sheather Density Estimation , 2004 .

[16]  Kenneth M. Roberts Social Correlates of Party System Demise and Populist Resurgence in Venezuela , 2003, Latin American Politics and Society.

[17]  M. Hazelton Variable kernel density estimation , 2003 .

[18]  D. Sornette,et al.  Importance of direct and indirect triggered seismicity in the ETAS model of seismicity , 2003, physics/0303070.

[19]  Philip A. Schrodt,et al.  Conflict and Mediation Event Observations (CAMEO): A New Event Data Framework for the Analysis of Foreign Policy Interactions , 2002 .

[20]  J. Jenkins,et al.  Mapping Mass Political Conflict and Civil Society , 1997 .

[21]  Joshua S. Goldstein A Conflict-Cooperation Scale for WEIS Events Data , 1992 .

[22]  Donald L. Snyder,et al.  Self-Exciting Point Processes , 1991 .

[23]  P. J. Green,et al.  Density Estimation for Statistics and Data Analysis , 1987 .

[24]  T. Ozaki Maximum likelihood estimation of Hawkes' self-exciting point processes , 1979 .

[25]  John R. Freeman,et al.  Scientific Forecasts in International Relations: Problems of Definition and Epistemology , 1979 .

[26]  Michael D. Wallace,et al.  To augur well : early warning indicators in world politics , 1979 .

[27]  Stephen J. Andriole,et al.  Toward the Development of an Integrated Crisis Warning System , 1977 .

[28]  G. P. Moore,et al.  Neuronal spike trains and stochastic point processes. I. The single spike train. , 1967, Biophysical journal.