Mathematics in War

Mathematics has for centuries been stimulated, financed and credited by military purposes. Some mathematical thoughts and mathematical technology have also been vital in war. During World War II mathematical work by the Anti-Hitler coalition was part of an aspiration to serve humanity and not help destroy it. At present, it is not an easy task to view the bellicose potentials of mathematics in a proper perspective.

A persistent problem when finding communities in large complex networks is the so-called resolution limit. This thesis addresses this issue meticulously, and introduces the important notion of resolution-limit-free. Remarkably, only few methods possess this desirable property, and this thesis puts forward one such method. Moreover, it discusses how to asses whether communities can occur by chance or not. One aspect that is often ignored in this field is treated here: links can also be negative, as in war or conflict.

Philanthropies funded by the Rockefeller family have been prominent in the social history of the twentieth century for their involvement in medicine and applied science. This book provides the first detailed study of their relatively brief but nonetheless influential foray into the field of mathematics. The careers of a generation of pathbreakers in modern mathematics, such as S.Banach, B.L.van der Waerden and André Weil, were decisively affected by their becoming fellows of the Rockefeller-funded International Education Board in the 1920s.

Philanthropies funded by the Rockefeller family have been prominent in the social history of the twentieth century for their involvement in medicine and applied science. This book provides the first detailed study of their relatively brief but nonetheless influential foray into the field of mathematics. The careers of a generation of pathbreakers in modern mathematics, such as S.Banach, B.L.van der Waerden and André Weil, were decisively affected by their becoming fellows of the Rockefeller-funded International Education Board in the 1920s.

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.