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This book uses machine-learning to identify the causes of conflict
from among the top predictors of conflict. This methodology
elevates some complex causal pathways that cause civil conflict
over others, thus teasing out the complex interrelationships
between the most important variables that cause civil conflict.
Success in this realm will lead to scientific theories of conflict
that will be useful in preventing and ending civil conflict. After
setting out a current review of the literature and a case for using
machine learning to analyze and predict civil conflict, the authors
lay out the data set, important variables, and investigative
strategy of their methodology. The authors then investigate
institutional causes, economic causes, and sociological causes for
civil conflict, and how that feeds into their model. The
methodology provides an identifiable pathway for specifying causal
models. This book will be of interest to scholars in the areas of
economics, political science, sociology, and artificial
intelligence who want to learn more about leveraging machine
learning technologies to solve problems and who are invested in
preventing civil conflict.
This book develops a machine-learning framework for predicting
economic growth. It can also be considered as a primer for using
machine learning (also known as data mining or data analytics) to
answer economic questions. While machine learning itself is not a
new idea, advances in computing technology combined with a dawning
realization of its applicability to economic questions makes it a
new tool for economists.
This book should be useful to anyone interested in identifying the
causes of civil conflict and doing something to end it. It even
suggests a pathway for the lay reader. Civil conflict is a
persistent source of misery to humankind. Its study, however, lacks
a comprehensive theory of its causes. Nevertheless, the question of
cooperation or conflict is at the heart of political economy. This
book introduces Machine Learning to explore whether there even is a
unified theory of conflict, and if there is, whether it is a 'good'
one. A good theory is one that not only identifies the causes of
conflict, but also identifies those causes that predict conflict.
Machine learning algorithms use out of sample techniques to choose
between competing hypotheses about the sources of conflict
according to their predictive accuracy. This theoretically agnostic
'picking' has the added benefit of offering some protection against
many of the problems noted in the current literature; the tangled
causality between conflict and its correlates, the relative rarity
of civil conflict at a global level, missing data, and spectacular
statistical assumptions. This book argues that the search for a
unified theory of conflict must begin among these more predictive
sources of civil conflict. In fact, in the book, there is a clear
sense that game theoretic rational choice models of
bargaining/commitment failure predict conflict better than any
other approach. In addition, the algorithms highlight the fact that
conflict is path dependent - it tends to continue once started.
This is intuitive in many ways but is roundly ignored as a matter
of science. It should not. Further, those causes of conflict that
best predict conflict can be used as policy levers to end or
prevent conflict. This book should therefore be of interest to
military and civil leaders engaged in ending civil conflict. Last,
though not least, the book highlights how the sources of conflict
affect conflict. This additional insight may allow the crafting of
policies that match a country's specific circumstance.
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