Myron P. Gutmann

Bio: Myron P. Gutmann is an academic researcher from University of Colorado Boulder. The author has contributed to research in topics: Population & Land use. The author has an hindex of 26, co-authored 108 publications receiving 5652 citations. Previous affiliations of Myron P. Gutmann include Inter-university Consortium for Political and Social Research & American Association for the Advancement of Science.

Computational Social Science

Abstract: A field is emerging that leverages the capacity to collect and analyze data at a scale that may reveal patterns of individual and group behaviors.

The Origin and Prevention of Major Wars: Domestic Politics and War

Abstract: Domestic Politics and War It is difficult to read both the theoretical literature in political science on the causes of war and historians' case studies of the origins of particular wars without being struck by the difference in their respective evaluations of the importance of domestic political factors. Whereas historians devote considerable attention to these variables, most political scientists minimize their importance. Domestic political variables are not included in any of the leading theories of the causes of war; instead, they appear only in a number of isolated hypotheses and in some empirical studies that are generally atheoretical and noncumulative. This gap is troubling and suggests that political scientists and historians who study war have learned little from each other. A greater recognition of the role of domestic factors by political scientists would increase the explanatory power of their theories and provide more useful conceptual frameworks for the historical analysis of individual wars. This study takes a first step toward bridging this gap by examining some of the disparate theoretical literature on domestic politics and war. It examines the relationship between national attributes and war behavior, the relative likelihood of democratic and non-democratic regimes going to war, Marxist and liberal theories regarding the impact of economic structure, the influence of nationalism and public opinion, and the scapegoat hypothesis. First, however, this article takes a closer look at the different treatment of domestic sources of war by political scientists and historians.

The Structure and Function of Ecosystems in the Central North American Grassland Region

Abstract: The central grassland region occupies the center of North America in the United States, Canada and Mexico and is a unique resource for the continent. While there are no other areas with comparable features, the largest similar grassland areas occur in Europe and Asia. The uniqueness of the region derives from its size, its relative flatness, and the smoothness of its physical gradients. The smooth gradients in precipitation and temperature are the reasons why most gradients in ecosystem properties are also smooth. The west-east gradient in precipitation and the north-south gradient in temperature result in corresponding gradients in plant community types, net biomass production by plants, soil carbon storage, and nitrogen availability to plants. One of the most striking features of the present condition of the central grassland region is that a huge fraction of the original native grassland have been replaced by cropland.

The Origin and Prevention of Major Wars: The Theory of Hegemonic War

Abstract: The Theory of Hegemonic War In the introduction to his history of the great war between the Spartans and the Athenians, Thucydides wrote that he was addressing "those inquirers who desire an exact knowledge of the past as an aid to the interpretation of the future, which in the course of human things must resemble if it does not reflect it. ... In fine, I have written my work, not as an essay which is to win the applause of the moment, but as a possession for all time."' Thucydides, assuming that the behavior and phenomena that he observed would repeat themselves throughout human history, intended to reveal the underlying and unalterable nature of what is today called international relations.

Critical questions for big data

Abstract: The era of Big Data has begun. Computer scientists, physicists, economists, mathematicians, political scientists, bio-informaticists, sociologists, and other scholars are clamoring for access to the massive quantities of information produced by and about people, things, and their interactions. Diverse groups argue about the potential benefits and costs of analyzing genetic sequences, social media interactions, health records, phone logs, government records, and other digital traces left by people. Significant questions emerge. Will large-scale search data help us create better tools, services, and public goods? Or will it usher in a new wave of privacy incursions and invasive marketing? Will data analytics help us understand online communities and political movements? Or will it be used to track protesters and suppress speech? Will it transform how we study human communication and culture, or narrow the palette of research options and alter what ‘research’ means? Given the rise of Big Data as a socio-tech...

Computational Social Science

Abstract: A field is emerging that leverages the capacity to collect and analyze data at a scale that may reveal patterns of individual and group behaviors.

Geometric Deep Learning: Going beyond Euclidean data

Abstract: Many scientific fields study data with an underlying structure that is non-Euclidean. Some examples include social networks in computational social sciences, sensor networks in communications, functional networks in brain imaging, regulatory networks in genetics, and meshed surfaces in computer graphics. In many applications, such geometric data are large and complex (in the case of social networks, on the scale of billions) and are natural targets for machine-learning techniques. In particular, we would like to use deep neural networks, which have recently proven to be powerful tools for a broad range of problems from computer vision, natural-language processing, and audio analysis. However, these tools have been most successful on data with an underlying Euclidean or grid-like structure and in cases where the invariances of these structures are built into networks used to model them.