In Predicting Hotspots: Using Machine Learning to Understand Civil Conflict James T. Bang, Atin Basuchoudhary, John David, and Tinni Sen provide a vital contribution to the social scientific study of civil wars and other forms of violence within states. Whereas most theoretical and empirical studies of intrastate conflicts emphasize the correlates or the causes of violence, this book offers a variety of standard and innovative methodologies to best predict future civil wars. The book is a must-have for scholars and policymakers concerned about predicting future civil wars and what can be done to prevent them.

Charles H. Anderton, College of the Holy Cross

Predicting Hotspots: Using Machine Learning to Understand Civil Conflict is an ambitious and successful demonstration of how machine learning can be employed towards a holistic understanding of civil conflict. It provides a concise and intuitive introduction to machine learning using conflict data. In so doing, the top socioeconomic predictors of civil conflict are identified. Of equal or greater value is the authors’ insightful discussion of how their findings can better inform policy making and theoretical model selection.

Dann Arce, University of Texas at Dallas

Basuchoudhary, Tinni, Bang and David make a compelling case for using machine learning to predict conflict. The book is a timely and very welcome addition to our knowledge on the correlates of conflict.

Günther Schulze, Professor of Economics, University of Freiburg