This article presents ViEWS – a political violence early-warning system that seeks to be maximally transparent, publicly available, and have uniform coverage, and sketches the methodological innovations required to achieve these objectives. ViEWS produces monthly forecasts at the country and subnational level for 36 months into the future and all three UCDP types of organized violence: state-based conflict, non-state conflict, and one-sided violence in Africa. The article presents the methodology and data behind these forecasts, evaluates their predictive performance, provides selected forecasts for October 2018 through October 2021, and indicates future extensions. ViEWS is built as an ensemble of constituent models designed to optimize its predictions. Each of these represents a theme that the conflict research literature suggests is relevant, or implements a specific statistical/machine-learning approach. Current forecasts indicate a persistence of conflict in regions in Africa with a recent history of political violence but also alert to new conflicts such as in Southern Cameroon and Northern Mozambique. The subsequent evaluation additionally shows that ViEWS is able to accurately capture the long-term behavior of established political violence, as well as diffusion processes such as the spread of violence in Cameroon. The performance demonstrated here indicates that ViEWS can be a useful complement to non-public conflict-warning systems, and also serves as a reference against which future improvements can be evaluated.
Does internal migration following natural hazards increase the likelihood of protests in migrant-receiving areas? To address the question, this study first looks at the extent to which experiencing different forms of natural hazards contributes to a household’s decision to leave their district of residence. In a second step, the article explores whether that internal migration flow increases the number of protest events in migrant-hosting districts. In doing so, it contributes to the existing debate on the extent to which natural hazards impact the likelihood of social contention, and the role of migration as a linking pathway in that relationship. The impact of climate-related shocks may erode household assets and therefore adaptive capacity in ways that can eventually influence decisions to migrate to larger urban centres. Although migrants are agents of economical and technological change, urban environments may impose challenges to recently arrived migrants and their host communities, affecting the motivations and mobilization resources of urban social groups to protest. As a consequence, the probability of urban unrest in these locations is expected to increase. To test this, I use geo-referenced household-level data from Bangladesh for the period 2010–15, which records households’ experiences of different forms of natural hazard and internal migration flows, available from the Bangladesh Integrated Household Survey. It combines this with data on protests, derived from the Armed Conflict Location and Event Data. Findings suggest that flood hazards in combination with loss of assets increase the likelihood of internal migration, but unlike other types of domestic mobility, hazard-related migration does not increase the frequency of protests in migrants’ districts of destination.
The Sustainable Development Goals (SDGs) adopted in 2015 integrate diverse issues such as addressing hunger, gender equality and clean energy and set a common agenda for all United Nations member states until 2030. The 17 SDGs interact and by working towards achieving one goal countries may further—or jeopardise—progress on others. However, the direction and strength of these interactions are still poorly understood and it remains an analytical challenge to capture the relationships between the multi-dimensional goals, comprising 169 targets and over 200 indicators. Here, we use principal component analysis (PCA), an in this context novel approach, to summarise each goal and interactions in the global SDG agenda. Applying PCA allows us to map trends, synergies and trade-offs at the level of goals for all SDGs while using all available information on indicators. While our approach does not allow us to investigate causal relationships, it provides important evidence of the degree of compatibility of goal attainment over time. Based on global data 2000–2016, our results indicate that synergies between and within the SDGs prevail, both in terms of levels and over time change. An exception is SDG 10 ‘Reducing inequalities’ which has not progressed in tandem with other goals.
The study is aimed at solving the problem of thinking and the sources of building thought processes in systems empowered with intelligence as one of the fundamental steps to the creation of artificial intelligence. Artificial intelligence performs creative functions traditionally considered human prerogative, using computer programs to understand human intelligence and not being limited to biologically plausible methods. In this regard the evolution of a traditional computer program into a system capable of self-creation is being implemented, depending on the conditions of the external and internal events and processes. The article authors present a number of intermediate results of the research in the field of advanced technologies and artificial intelligence achieved on the basis of experiments run on the study of the semantic structures construction – sources aimed at shaping a thinking process in systems empowered with intelligence. The research carried out by the authors of the article contributes to building of basic algorithms as a parametrically polymorphic system. Scheme of one of the main functions of the master algorithm is presented. An array of constructions, semantically related and called by a route determined by a vector, the direction of which is aimed at minimal costs winning, which together act as the fundamental method for creating a master algorithm.
Armed conflict and economic growth are inherently coupled; armed conflict substantially reduces economic growth, while economic growth is strongly correlated with a reduction in the propensity of armed conflict. Here, we simulate the incidence of armed conflict and its effect on economic growth simultaneously along the economic pathways defined by the Shared Socioeconomic Pathways (SSPs). We argue that GDP per capita projections through the 21st century currently in use are too optimistic since they disregard the harm to growth caused by conflict. Our analysis indicates that the correction required to account for this is substantial – expected income is 25% lower on average across countries when taking conflict into account. The correction is particularly strong for the more pessimistic SSP3 and SSP4 where expected future incidence of armed conflict is high. There are strong regional patterns with countries with contemporaneous conflicts experiencing much higher conflict burdens and reduced economic growth by the end of century. The implications of this research indicate that today’s most marginalized societies will be substantially more vulnerable to the impact of climate change than indicated by existing income projections.
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