Atin Basuchoudhary, James T. Bang, Tinni Sen

Predicting Hotspots

Using Machine Learning to Understand Civil Conflict

• Gebundenes Buch

Produktbeschreibung

In spite of intense but traditional academic effort, a unique formal framework to study civil conflict has been elusive. This book uses predictive machine learning to highlight a framework to identify potential causes of civil conflict. Machine learning also improves the human ability to predict and therefore prevent conflict.

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Produktdetails

• Verlag: Globe Pequot Publishing Group Inc/Bloomsbury
• Seitenzahl: 180
• Erscheinungstermin: 15. September 2018
• Englisch
• Abmessung: 235mm x 157mm x 14mm
• Gewicht: 422g
• ISBN-13: 9781498520676
• ISBN-10: 1498520677
• Artikelnr.: 53019618

Autorenporträt

Atin Basuchoudhary, is professor of business and economics at Virginia Military Institute James T. Bang, is professor of economics at St. Ambrose University Tinni Sen, is professor of business and economics at Virginia Military Institute John David, is professor of applied mathematics at Virginia Military Institute

Inhaltsangabe

Chapter 1: An Overview of the Literature review Chapter 2: An Overview of
Machine Learning Techniques Chapter 3: A Description of Our Variables
Chapter 4: Preparing the Data Chapter 5: Implementing Machine Learning to
Predict Conflict Using R Chapter 6: Models and Results Chapter 7: Choosing
Among Seminal Models of Conflict Theory Chapter 8: Choosing between
Microeconomic Models of Conflict Chapter 9: Bargaining Failure, Commitment
Problems, and The Likelihood of Conflict Chapter 10: Toward a Predictive
Theoretical Model of Civil Conflict: Some Speculation