Early Warning Analysis for Social Diffusion Events

There is considerable interest in developing predictive capabilities for social diffusion processes, for instance to permit early identification of emerging contentious situations, rapid detection of disease outbreaks, or accurate forecasting of the ultimate reach of potentially viral ideas or behaviors. This paper proposes a new approach to this predictive analytics problem, in which analysis of meso-scale network dynamics is leveraged to generate useful predictions for complex social phenomena. We begin by deriving a stochastic hybrid dynamical systems (S-HDS) model for diffusion processes taking place over social networks with realistic topologies; this modeling approach is inspired by recent work in biology demonstrating that S-HDS offer a useful mathematical formalism with which to represent complex, multi-scale biological network dynamics. We then perform formal stochastic reachability analysis with this S-HDS model and conclude that the outcomes of social diffusion processes may depend crucially upon the way the early dynamics of the process interacts with the underlying network's community structure and core-periphery structure. This theoretical finding provides the foundations for developing a machine learning algorithm that enables accurate early warning analysis for social diffusion events. The utility of the warning algorithm, and the power of network-based predictive metrics, are demonstrated through an empirical investigation of the propagation of political memes over social media networks. Additionally, we illustrate the potential of the approach for security informatics applications through case studies involving early warning analysis of large-scale protests events and politically-motivated cyber attacks

Social Networks in Ghana

In this chapter we examine social networks among farmers in a developing country. We use detailed data on economic activities and social interactions between people living in four study villages in Ghana. It is clear that economic development in this region is being shaped by the networks of information, capital and influence that permeate these communities. This chapter explores the determinants of these important economic networks. We first describe the patterns of information, capital, labor and land transaction connections that are apparent in these villages. We then discuss the interconnections between the various economic networks. We relate the functional economic networks to more fundamental social relationships between people in a reduced form analysis. Finally, we propose an equilibrium model of multi-dimensional network formation that can provide a foundation for further data collection and empirical research.Endogenous Networks, Informal Credit, Social Learning

Optimal Multiphase Investment Strategies for Influencing Opinions in a Social Network

We study the problem of optimally investing in nodes of a social network in a competitive setting, where two camps aim to maximize adoption of their opinions by the population. In particular, we consider the possibility of campaigning in multiple phases, where the final opinion of a node in a phase acts as its initial biased opinion for the following phase. Using an extension of the popular DeGroot-Friedkin model, we formulate the utility functions of the camps, and show that they involve what can be interpreted as multiphase Katz centrality. Focusing on two phases, we analytically derive Nash equilibrium investment strategies, and the extent of loss that a camp would incur if it acted myopically. Our simulation study affirms that nodes attributing higher weightage to initial biases necessitate higher investment in the first phase, so as to influence these biases for the terminal phase. We then study the setting in which a camp's influence on a node depends on its initial bias. For single camp, we present a polynomial time algorithm for determining an optimal way to split the budget between the two phases. For competing camps, we show the existence of Nash equilibria under reasonable assumptions, and that they can be computed in polynomial time

Prediction and prevention of the next pandemic zoonosis.

Most pandemics--eg, HIV/AIDS, severe acute respiratory syndrome, pandemic influenza--originate in animals, are caused by viruses, and are driven to emerge by ecological, behavioural, or socioeconomic changes. Despite their substantial effects on global public health and growing understanding of the process by which they emerge, no pandemic has been predicted before infecting human beings. We review what is known about the pathogens that emerge, the hosts that they originate in, and the factors that drive their emergence. We discuss challenges to their control and new efforts to predict pandemics, target surveillance to the most crucial interfaces, and identify prevention strategies. New mathematical modelling, diagnostic, communications, and informatics technologies can identify and report hitherto unknown microbes in other species, and thus new risk assessment approaches are needed to identify microbes most likely to cause human disease. We lay out a series of research and surveillance opportunities and goals that could help to overcome these challenges and move the global pandemic strategy from response to pre-emption

Semantic discovery and reuse of business process patterns

Patterns currently play an important role in modern information systems (IS) development and their use has mainly been restricted to the design and implementation phases of the development lifecycle. Given the increasing significance of business modelling in IS development, patterns have the potential of providing a viable solution for promoting reusability of recurrent generalized models in the very early stages of development. As a statement of research-in-progress this paper focuses on business process patterns and proposes an initial methodological framework for the discovery and reuse of business process patterns within the IS development lifecycle. The framework borrows ideas from the domain engineering literature and proposes the use of semantics to drive both the discovery of patterns as well as their reuse

TWENTY-FIVE YEARS OF RESEARCH ON WOMEN FARMERS IN AFRICA: LESSONS AND IMPLICATIONS FOR AGRICULTURAL RESEARCH INSTITUTIONS; WITH AN ANNOTATED BIBLIOGRAPHY

Based on an extensive review of the literature on women farmers in Africa, this paper explores the potential reasons why women farmers have not adopted improved maize technologies and discusses the implications for agricultural research. Women farmers are often constrained by their lack of access to labor, land, and inputs. In addition, women may prefer different outputs than men. Finally, the dynamics of household decision-making affects technology adoption; roles and responsibilities within the household are often renegotiated when new technologies are adopted, and women may be reluctant to provide labor if they do not receive some of the benefits. Each section of this paper includes a number of questions that may provide insights into the gender roles and dynamics in a particular community. Three general conclusions can be drawn from the available literature. First, there is enormous complexity and heterogeneity among African households. Second, there is no simple way to summarize gender roles within African households and communities. Third, gender roles and responsibilities are dynamic; in particular, they change with new economic circumstances. An extensive annotated bibliography on gender issues and the adoption of maize technologies in Africa follows the review of studies.Farm Management, Labor and Human Capital,

Climate Change Hysteria and the Supreme Court: The Economic Impact of Global Warming on the U.S. and the Misguided Regulation of Greenhouse Gas Emissions under the Clean Air Act

In the spring of 2007, the U.S. Supreme Court ruled in Massachusetts v. EPA that the U.S. Environmental Protection Agency (EPA) must promulgate automobile tailpipe C02 emission standards under Section 202 of the Clean Air Act (CAA). American environmentalists hailed the Supreme Court's decision as an important victory in the battle to curb global warming. This article argues to the contrary that: 1) a large body of economic work demonstrates that the likely pattern of costs and benefits from climate change in the United States bears no resemblance to the pollution problems that Congress intended to deal with in the Clean Air Act, with moderate climate change predominantly benefiting, rather than harming, the U.S. -- so that that the Clean Air Act cannot reasonably be interpreted to cover greenhouse gas emissions; 2) By effectively forcing the EPA to regulate ghg emissions under a statute that was never intended to cover the very different problem of climate change, the Court has changed the policy status quo in a way that makes socially desirable federal climate change legislation less likely; and 3) given the global nature of the greenhouse gas emission problem, unilateral emission limits in the U.S. are likely to be worse than ineffective, in that they will likely have the perverse effect of lessening the incentive for latecomers to climate change regulation (such as China) to themselves take costly action to reduce such emissions. The article concludes by arguing that a sensible formulation of U.S. climate change policy would involve measures to respond both to the long-term threat to the U.S. and the short-term threat to developing countries. There are policy instruments appropriate to these goals: large increases in subsidies for research and development into clean coal and alternative fuels to respond to the long term threat to the U.S.; redirecting foreign aid to fund climate change adaptation in developing countries to respond to the short term threat to developing countries.

Factors for Measuring Photovoltaic Adoption from the Perspective of Operators

The diffusion of photovoltaic distributed generation is relevant for addressing the political, economic, and environmental issues in the electricity sector. However, the proliferation of distributed generation brings new administrative and operational challenges for the sustainability of electric power utilities. Electricity distributors operate in economies of scale, and the high photovoltaic penetration means that these companies have economic and financial impacts, in addition to influencing the migration of other consumers. Thus, this paper aims to systematically identify and evaluate critical factors and indicators that may influence electricity distributors in predicting their consumers’ adoption of photovoltaic technology, which were subjected to the analysis of 20 industry experts. Results show that the cost of electricity, generation capacity, and cost of the photovoltaic systems are the most relevant indicators, and it is possible to measure a considerable part of them using the internal data of the electricity distributors. The study contributes to the understanding of the critical factors for the forecast of the adoption of consumers to distributed photovoltaic generation, to assist the distribution network operators in the decision making, and the distribution sustainability. Also, it establishes the theoretical, political, and practical implications for the Brazilian scenario and developing countries.This research was funded by the Conselho Nacional de Desenvolvimento Científico e Tecnológico [grant numbers 311926/2017-7, 142448/2018-4, 310594/2017-0 and 465640/2014-1], Coordenação de Aperfeiçoamento de Pessoal de Nível Superior [grant numbers 1773252/2018, 1845395/2019 and 23038.000776/2017–54] and Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul [grant number 17/2551-0000517-1].info:eu-repo/semantics/publishedVersio

Sustainable Population Growth in Low-Density Areas in a New Technological Era: Prospective Thinking on How to Support Planning Policies Using Complex Spatial Models

Urban development is the result of the interaction between anthropogenic and environmental dimensions. From the perspective of its density, it ranges from high-density populated areas, associated with large cities that concentrate the main economic and social thrust of societies, to low-density populated areas (e.g., rural areas, small–medium-sized cities). Against the backdrop of the new technological and environmental era, this commentary offers insights on how to support spatial planning policies for sustainable urban growth in low-density areas. We propose the integration of technological drivers such as Internet networks, telecommuting, distance-learning education, the use of electric cars, etc. into the complex spatial models to project and thus to identify the best locations for urban development in low-density areas. This understanding can help to mitigate the disparities between high- and low-density populated areas, and to reduce the inequality among regions as promoted in the UN 2030 Agenda for Sustainable Development Goals.info:eu-repo/semantics/publishedVersio