Predictions in the Short Term

In an effort to aid in short-term planning for policymakers, we have also be working on short term predictions based on quarterly data.   Rather than aggregating the political violence events to the year, we aggregate them to the quarter. We think that these results are promising for both the academic and policymaking communities.

Based on our model results from the fourth quarter of 2009, we predicted the following list of countries to have increases in political violence in the short term.  Given both events during the “Arab Spring” and unrest in Europe in the past year, this method could be extremely valuable for those interested in short term increases in political violence.

Venezuela
Peru
Argentina
United Kingdom
Ireland
Belgium
France
Greece
Georgia
Niger
Nigeria
Chad
South Africa
Sudan
Turkey
Israel

Kazakhstan
India
Sri Lanka

Nepal
Malaysia
Singapore
Philippines
Indonesia

 

 

Venezuela
Peru
Argentina
United Kingdom
Ireland
Belgium
France
Greece
Georgia
Niger
Nigeria
Chad
South Africa
Sudan
Turkey
Israel

Kazakhstan
India
Sri Lanka

Nepal
Malaysia
Singapore
Philippines
Indonesia

Posted in Forecasts Research by amurdie. No Comments

Forecasting Increases in Political Violence: 2011-2015

Just in time for the new year, we are happy to release our list of countries predicted to have increases in political violence for 2011 to 2015.  The countries are listed below, based on our results from four separate Domestic Political Violence Model specifications.  The different specifications reflect coding decisions and data availability.

VERY HIGH RISK HIGH RISK MEDIUM RISK
United Kingdom China India
Israel Gambia Pakistan
Sri Lanka Brazil Russia
Iran Indonesia Mongolia
Colombia Italy Turkey
Zimbabwe Eritrea Australia
South Africa Saudi Arabia Gabon
Haiti Ecuador Kenya
Egypt Estonia Albania
Philippines Libya Singapore
Guinea-Bissau Canada Malawi
Venezuela UAE Peru
Chile Moldova Nigeria
Syria Japan Ukraine
Chad Central Africa Republic Swaziland
Belarus Congo Kinshasa Mauritius
Guinea Angola Cambodia
Kyrgyzstan Romania Switzerland
Greece Ghana Mauritania
Zambia Rwanda
Bulgaria Jordan
Spain Bhutan
Lebanon Bolivia
Honduras Croatia
Panama
Ireland
Austria
Burundi
Guyana
Thailand
Uganda
Portugal
Tajikistan
Algeria
Malaysia
Hungary
Bangladesh
Nicaragua
Belgium
Kazakhstan
Algeria

The first column lists the countries we deem at “Very High Risk,” these are the countries where all four models predicted an increase. The next column lists the countries we deem at “High Risk.” We include in this category countries where, if the country was included in all four models, three models predicted an increase or, if the country as only included in the two expanded data samples, both of these models predicted an increase. The third column lists the countries we deem at “Medium Risk” for increases in political violence: half of the models predict an increase in violence. Within each column, the list begins with the country predicted to have the greatest increase in political violence.

There are some new players on the list.  Most notably, the United Kingdom tops our list for “Very High Risk” countries.  This pent-up demand for increased violence was certainly evident in the London Riots over the Summer of 2011.  Other countries on the list include those with much violent protest in the “Arab Spring” of 2011, most strikingly Syria, Egypt, and Libya.  Zimbabwe, Haiti, and the Philippines are also “Very High Risk” countries that were not predicted to have increased political violence in the 2010-2014 model.  For Zimbabwe, for example, very recent violence against the ruling party may provide some face validity to its inclusion on our list.  Growing discontent in Haiti and the Philippines underscore their inclusion on our updated list. Other countries on the list include those with much violent protest in the “Arab Spring” of 2011, most strikingly Syria, Egypt, and Libya.

These predictions should allow policymakers to be proactive in human and national security decisions.

 

Posted in Forecasts by amurdie. No Comments

Political Violence in Tunisia

Tunisia experienced the onset of political violence on January 13th, with the violence increasing in subsequent days, and eventually leading to the ouster of President Zine el-Abidine Ben Ali. Since 1990 Tunisia has been relatively immune from political violence targeting the government, with all years in our dataset below the worldwide average level. They actually experienced no recorded violent events in the year 2009. This recent wave of political violence certainly represents an increase in violence as we predicted in our model.

Similar to many of the other states on our list, Tunisia has experienced steady increases in government repression since 1999. This confluence of repression and political violence directed towards governments is certainly part of a broader pattern.

Posted in Uncategorized by sbell. No Comments

More Violence in Democracies: Italy and Civil Unrest

Violence erupted in Italy today (December 14th) after Prime Minister Silvio Berlusconi remained in power following a confidence vote.  To many, the violence in Italy is surprising: this is an advanced democracy!  People aren’t supposed to take to the streets!  However unexpected the violence appears, Italy’s  increase in domestic violence was predicted by our statistical model.  In fact, in many regards, Italy is a perfect example of a country “ripe” for violence.

First, Italian human rights have not been ideal.  As shown in the global statistical model results, countries with less-than-perfect performance on key physical integrity rights (things like freedom from unlawful detention and political disappearances) actually fuel the domestic fire, leading people to take to the street.  Also, like found in the global sample, the ability of protesters to organize matters.  The violence in Italy has been coordinated by student groups armed with cell phones and internet access.  Although the media may be good for a whole host of political and economic outcomes, it also aids in mobilization against a government.  In this regard, the violence in Italy is similar to election protests in Iran in 2008.

The take away point: people upset at government practices, whether in a democracy or a dictatorship, now find it easier than ever to coordinate against their government.

Posted in Forecasts by amurdie. 1 Comment

Catalyzing events or structural conditions? The Case of Ireland

The protests in Ireland over the weekend had sporadic violence, as was predicted by our model.  The most interesting thing, though, is that our model predicted increased domestic political violence in Ireland based on indicators of state coercion, government capacity, and group coordination, not based on any catalyzing event (like the bailout).   This could indicate that Ireland was ripe for an increase in domestic political violence, even without this catalyzing event that ultimately led people to the street. For policymakers in the COCOMs, the ability to predict political violence without relying on catalyzing events is a good thing; it allows policymakers to plan a course of action without having to wait for these events.  Political scientists have long been interested in the debate between catalyzing events and structural conditions.  Most of the research has shown the importance of structural conditions (see William Thompson’s “A Streetcar Named Sarajevo: Catalysts, Multiple Causation Chains, and Rivalry Structures” in International Studies Quarterly 2003, for example).

Posted in Forecasts by amurdie. No Comments

Increased political violence in store for Italy and Czech Republic?

In collaboration with our academic partners Prof. Cingranelli at the Political Science Department, SUNY Binghamton University and Profs. Sam Bell and Amanda Murdie at the Department of Political Science, Kansas State University, we developed a Domestic Political Violence Model that forecasts political violence levels five years into the future. The model enables policymakers, particularly in the COCOMs, to proactively plan for instances of increased domestic political violence, with implications for resource allocation and intelligence asset assignment. Our model uses the IDEA dataset for political event coding, plus numerous indicators from the CIRI Human Rights Dataset, Polity IV Dataset, World Bank, OECD, Correlates of War project, and Fearon and Laitin datasets. Here is our model’s forecast for 2010 – 2014 as a ranked list:

  1. Iran
  2. Sri Lanka
  3. Russia
  4. Georgia
  5. Israel
  6. Turkey
  7. Burundi
  8. Chad
  9. Honduras
  10. Czech Republic
  11. China
  12. Italy
  13. Colombia
  14. Ukraine
  15. Indonesia
  16. Malaysia
  17. Jordan
  18. Mexico
  19. Kenya
  20. South Africa
  21. Ireland
  22. Peru
  23. Chile
  24. Armenia
  25. Tunisia
  26. Democratic Republic of the Congo
  27. Belarus
  28. Argentina
  29. Albania
  30. Ecuador
  31. Sudan
  32. Austria
  33. Nigeria
  34. Syria
  35. Kyrgyz Republic
  36. Egypt
  37. Belgium

Using a regression model applied to a large number of drivers of conflict variables spanning numerous open source social science datasets, our model uses a novel Negative Residuals technique. Negative Residuals result from the model predicting higher levels of violence than actually experienced, indicating nation states that are pre-disposed to increasing levels of violence based on the presence of environmental conditions and drivers of conflict with demonstrated correlation with measured political violence. The residuals imply that these are states that we expect to observe increases in violence although not necessarily high levels of violence. So Iran and Sri Lanka are not expected to have the same level of violence but are expected to have the same magnitude increase in violence.

There some unexpected countries on our list like Czech Republic and Italy. Time will tell the accuracy of our model’s predictions although recent political violence in Ecuador is an early indicator of the model’s effective performance. The model uses nuanced measures of repression and captures variables that can be manipulated by policy makers. Our project page has further details on the model.

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Milcord Releases Domestic Political Violence Forecast Model Results for the Next Five Years

Milcord today announced the results of its Domestic Political Violence Forecast Model developed with Prof. David Cingranelli of Binghamton University, Profs. Sam Bell and Amanda Murdie of Kansas State University.

“We are excited to work with Milcord to develop the Domestic Political Violence Forecast Model that predicts for all countries of the world the overall level of domestic political violence directed against the state, highlighting probable risk areas for this type of violence which is often seen as a harbinger of low-intensity conflict and nascent insurgent movements,” said Prof. Cingranelli of CIRI Human Rights Data Project. Measuring domestic political violence targeted at the government captures the pulse of a population and how it views governing authorities, and can help policy makers proactively plan for instances of domestic political violence, with implications for resource allocation and intelligence asset assignment. “The model uses nuanced measures of repression, captures variables that can be manipulated by policy makers such as those relating to not only intent but capability for violence, making the model relevant for the policy community,” said Prof. Murdie of the Department of Political Science, Kansas State University. The model uses the IDEA dataset for political event coding, plus numerous indicators from the CIRI Human Rights Dataset, Polity IV Dataset, World Bank, OECD, Correlates of War project, and Fearon and Laitin datasets. Using a regression model applied to a large number of drivers of conflict variables spanning numerous open source social science datasets, the model uses a novel Negative Residuals technique. Negative Residuals result from the model predicting higher levels of violence than actually experienced, indicating nation states that are pre-disposed to increasing levels of violence based on the presence of environmental conditions and drivers of conflict with demonstrated correlation with measured political violence.

“While the model’s precision, accuracy and recall performance on our sample data sets are very encouraging, only time will tell the validity of our forecast based on 2009 data sets. It is worth noting that Ecuador which was number 30 on our list of countries expected to show increased political violence in the next five years is already showing political violence,” said Prof. Sam Bell of the Department of Political Science, Kansas State University.

About Milcord
Milcord builds knowledge powered solutions for social behavior, information security, and geospatial intelligence applications. As an Open Innovation practitioner, Milcord collaborates with university labs, Federally- funded R&D Centers (FFRDC), information technology companies, systems integrators, community data and open source software projects. The Domestic Political Violence Forecasting Model has been developed under the Predictive Societal Indicators of Radicalism (PSIR) project sponsored by Air Force Research Labs (AFRL) Rome Research Laboratory. For more information on the PSIR project and the model’s results, visit http://wiki.milcord.com/index.php/Domestic_Political_Violence_Forecast_Model.

Contacts
PR Director, Milcord Tel: +1 781-839-7138 Email: pr@milcord.com

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