Climate change and population migration in Brazil’s Northeast: scenarios for 2025–2050

This research contributes to an understanding of the relationship between climate change, economic impacts and migration. We model the long-term relationship (up to 45 years of projection) between demographic dynamics—particularly migration—driven by changes in the performance of the economy due to climate changes in the Northeast region of Brazil. The region is of particular relevance to the study of climate change impacts given its large human population (28% of Brazil’s population) and high levels of impoverishment, having an extensive semi-dry area which will be severely impacted by growing temperatures. Ultimately, the integrated model generates state- and municipal-level migration scenarios based on climate change impacts on the primary economic sectors and their articulations with other sectors. Results suggest that the predicted climate changes will impact severely the agriculture sector in the region, acting as a potential migration push factor to other regions in the country. Finally, we discuss how the increased vulnerability of some groups, particularly migrants, can be factored into Brazilian public policy and planning.     

Competence effects for choices involving gains and losses

We investigate how choices for uncertain gain and loss prospects are affected by the decision maker’s perceived level of knowledge about the underlying domain of uncertainty. Specifically, we test whether Heath and Tversky’s (J Risk Uncertain 4:5–28, 1991) competence hypothesis extends from gains to losses. We predict that the commonly-observed preference for high knowledge over low knowledge prospects for gains reverses for losses. We employ an empirical setup in which participants make hypothetical choices between gain or loss prospects in which the outcome depends on whether a high or low knowledge event occurs. We infer decision weighting functions for high and low knowledge events from choices using a representative agent preference model. For gains, we replicate the results of Kilka and Weber (Manage Sci 47:1712–1726, 2001), finding that decision makers are more attracted to choices that they feel more knowledgeable about. However, for losses, we find limited support for our extension of the competence effect.     

A Genetic Algorithm for the Weight Setting Problem in OSPF Routing

With the growth of the Internet, Internet Service Providers (ISPs) try to meet the increasing traffic demand with new technology and improved utilization of existing resources. Routing of data packets can affect network utilization. Packets are sent along network paths from source to destination following a protocol. Open Shortest Path First (OSPF) is the most commonly used intra-domain Internet routing protocol (IRP). Traffic flow is routed along shortest paths, splitting flow at nodes with several outgoing links on a shortest path to the destination IP address. Link weights are assigned by the network operator. A path length is the sum of the weights of the links in the path. The OSPF weight setting (OSPFWS) problem seeks a set of weights that optimizes network performance. We study the problem of optimizing OSPF weights, given a set of projected demands, with the objective of minimizing network congestion. The weight assignment problem is NP-hard. We present a genetic algorithm (GA) to solve the OSPFWS problem. We compare our results with the best known and commonly used heuristics for OSPF weight setting, as well as with a lower bound of the optimal multi-commodity flow routing, which is a linear programming relaxation of the OSPFWS problem. Computational experiments are made on the AT&T Worldnet backbone with projected demands, and on twelve instances of synthetic networks.     

Catching a glimpse: Corona‐life and its micro‐politics in academia

The spread of COVID‐19 acutely challenges and affects not just economic markets, demographic statistics and healthcare systems, but indeed also the politics of organizing and becoming in a new everyday life of academia emerging in our homes. Through a collage of stories, snapshots, vignettes, photos and other reflections of everyday life, this collective contribution is catching a glimpse of corona‐life and its micro‐politics of multiple, often contradicting claims on practices as many of us live, work and care at home. It embodies concerns, dreams, anger, hope, numbness, passion and much more emerging amongst academics from across the world in response to the crisis. As such, this piece manifests a shared need to — together, apart — enact and explore constitutive relations of resistance, care and solidarity in these dis/organizing times of contested spaces, identities and agencies as we are living–working–caring at home during lockdowns.     

Procedure to identify outliers through cumulative distribution of extremes in a Gamma response model

This work aimed at proposing a procedure based on the cumulative distribution of maximums and minimums to identify outliers in generalized Gamma-response models. In order to validate such method, we used simulations scenarios defined by the combination of different samples, contamination rate and distributions with different degrees of asymmetry. In this context, probabilities related to errors in classification and accuracy were obtained by carrying by Monte Carlo simulations. Using cumulative distribution of extremes to identify outliers in a Gamma-response model is recommended, since it is not likely to present errors and was highly accurate in all assessed scenarios.     

Revised GRASP with path-relinking for the linear ordering problem

The linear ordering problem (LOP) is an NP\mathcal{NP}-hard combinatorial optimization problem with a wide range of applications in economics, archaeology, the social sciences, scheduling, and biology. It has, however, drawn little attention compared to other closely related problems such as the quadratic assignment problem and the traveling salesman problem. Due to its computational complexity, it is essential in practice to develop solution approaches to rapidly search for solution of high-quality. In this paper we propose a new algorithm based on a greedy randomized adaptive search procedure (GRASP) to efficiently solve the LOP. The algorithm is integrated with a Path-Relinking (PR) procedure and a new local search scheme. We tested our implementation on the set of 49 real-world instances of input-output tables (LOLIB instances) proposed in Reinelt (Linear ordering library (LOLIB) 2002). In addition, we tested a set of 30 large randomly-generated instances proposed in Mitchell (Computational experience with an interior point cutting plane algorithm, Tech. rep., Mathematical Sciences, Rensellaer Polytechnic Institute, Troy, NY 12180-3590, USA 1997). Most of the LOLIB instances were solved to optimality within 0.87 seconds on average. The average gap for the randomly-generated instances was 0.0173% with an average running time of 21.98 seconds. The results indicate the efficiency and high-quality of the proposed heuristic procedure.     

A parallel multi-population genetic algorithm for a constrained two-dimensional orthogonal packing problem

This paper addresses a constrained two-dimensional (2D), non-guillotine restricted, packing problem, where a fixed set of small rectangles has to be placed into a larger stock rectangle so as to maximize the value of the rectangles packed. The algorithm we propose hybridizes a novel placement procedure with a genetic algorithm based on random keys. We propose also a new fitness function to drive the optimization. The approach is tested on a set of instances taken from the literature and compared with other approaches. The experimental results validate the quality of the solutions and the effectiveness of the proposed algorithm.