Predicting Hotspots: Using Machine Learning to Understand Civil Conflict
Autor Atin Basuchoudhary, James T. Bang, Tinni Sen, John Daviden Limba Engleză Hardback – 15 sep 2018
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Specificații
ISBN-13: 9781498520676
ISBN-10: 1498520677
Pagini: 182
Ilustrații: 5 tables; 11 graphs;
Dimensiuni: 159 x 231 x 20 mm
Greutate: 0.45 kg
Editura: Bloomsbury Publishing
Colecția Lexington Books
Locul publicării:New York, United States
ISBN-10: 1498520677
Pagini: 182
Ilustrații: 5 tables; 11 graphs;
Dimensiuni: 159 x 231 x 20 mm
Greutate: 0.45 kg
Editura: Bloomsbury Publishing
Colecția Lexington Books
Locul publicării:New York, United States
Cuprins
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
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
Recenzii
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.
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.
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.
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.
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.