State-building, war and violence : evidence from Latin America

In European history, war has played a major role in state‐building and the state monopoly on violence. But war is a very specific form of organized political violence, and it is decreasing on a global scale. Other patterns of armed violence now dominate, ones that seem to undermine state‐building, thus preventing the replication of European experiences. As a consequence, the main focus of the current state‐building debate is on fragility and a lack of violence control inside these states. Evidence from Latin American history shows that the specific patterns of the termination of both war and violence are more important than the specific patterns of their organization. Hence these patterns can be conceptualized as a critical juncture for state‐building. While military victories in war, the subordination of competing armed actors and the prosecution of perpetrators are conducive for state‐building, negotiated settlements, coexistence, and impunity produce instability due to competing patterns of authority, legitimacy, and social cohesion

Automated Discovery of Food Webs from Ecological Data Using Logic-Based Machine Learning

Networks of trophic links (food webs) are used to describe and understand mechanistic routes for translocation of energy (biomass) between species. However, a relatively low proportion of ecosystems have been studied using food web approaches due to difficulties in making observations on large numbers of species. In this paper we demonstrate that Machine Learning of food webs, using a logic-based approach called A/ILP, can generate plausible and testable food webs from field sample data. Our example data come from a national-scale Vortis suction sampling of invertebrates from arable fields in Great Britain. We found that 45 invertebrate species or taxa, representing approximately 25% of the sample and about 74% of the invertebrate individuals included in the learning, were hypothesized to be linked. As might be expected, detritivore Collembola were consistently the most important prey. Generalist and omnivorous carabid beetles were hypothesized to be the dominant predators of the system. We were, however, surprised by the importance of carabid larvae suggested by the machine learning as predators of a wide variety of prey. High probability links were hypothesized for widespread, potentially destabilizing, intra-guild predation; predictions that could be experimentally tested. Many of the high probability links in the model have already been observed or suggested for this system, supporting our contention that A/ILP learning can produce plausible food webs from sample data, independent of our preconceptions about “who eats whom.” Well-characterised links in the literature correspond with links ascribed with high probability through A/ILP. We believe that this very general Machine Learning approach has great power and could be used to extend and test our current theories of agricultural ecosystem dynamics and function. In particular, we believe it could be used to support the development of a wider theory of ecosystem responses to environmental change

SDM:A New Data Set on Self-determination Movements with an Application to the Reputational Theory of Conflict

This dataset, of self-determination movements (SDMs) with universal coverage for the period from 1945 to 2012, corrects the selection bias that characterizes previous efforts to code SDMs and significantly expands coverage relative to the extant literature. For a random sample of cases, we add information on state–movement interactions and several attributes of SDM groups. The data can be used to study the causes of SDMs, the escalation of self-determination (SD) conflicts over time, and several other theoretical arguments concerning separatist conflict that have previously been tested with incomplete or inferior data.The creators request that the associated paper is cited in place of this dataset