1,721,069 research outputs found
Alternative Characterizations of the Proportional Solution for Nonconvex Bargaining Problems with Claims
Full text linkWe provide three alternative characterizations of the proportional solution defined on compact and comprehensive bargaining problems with claims that are not necessarily convex. One characterization result is obtained by using, together with other standard axioms, two solidarity axioms. Another characterization theorem shows that the single-valuedness axiom is dispensable even within the class of nonconvex problems if the standard symmetry axiom is imposed.Bargaining problems, Claims point, Proportional solution, Nonconvexity, Solidarity axioms
Uncovered Set Choice Rule
Full text linkI study necessary and sufficient conditions for a choice function to be rationalised in the following sense: there exists a complete asymmetric relation T (a tournament ) such that for each feasible (finite) choice situation, the choice coincides with the uncovered set of T . This notion of rationality explains not only cyclical and context dependent choices observed in practice, but also provides testable restrictions on observable choice behavior.Rationalizability, Uncovered set, Intransitive choice
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
The weighted average constraint
No full textAbstract. Weighted average expressions frequently appear in the con-text of allocation problems with balancing based constraints. In combi-natorial optimization they are typically avoided by exploiting problems specificities or by operating on the search process. This approach fails to apply when the weights are decision variables and when the average value is part of a more complex expression. In this paper, we introduce a novel average constraint to provide a convenient model and efficient propagation for weighted average expressions appearing in a combinato-rial model. This result is especially useful for Empirical Models extracted via Machine Learning (see [2]), which frequently count average expres-sions among their inputs. We provide basic and incremental filtering algorithms. The approach is tested on classical benchmarks from the OR literature and on a workload dispatching problem featuring an Empirical Model. In our experimentation the novel constraint, in particular with incremental filtering, proved to be even more efficient than traditional techniques to tackle weighted average expressions.
Ensuring Fairness Stability for Disentangling Social Inequality in Access to Education: the FAiRDAS General Method
Full text linkRecent advancements in Artificial Intelligence in Education (AIEd) have revolutionized educational practices using machine learning to extract insights from students' activities and behaviours. Performance prediction, a key domain within AIEd, aims to enhance student achievement levels and address sustainable development goals related to education, health, gender equality, and economic growth. However, the potential of AIEd to contribute to these goals is hindered by the lack of attention to fairness in prediction algorithms, leading to educational inequality. To address this gap, we introduce FAiRDAS a general framework that models long-term fairness as an abstract dynamic system. Our approach, illustrated through a case study in AIEd with real data, offers a customizable solution to promote long-term fairness while promoting the stability of mitigation actions over time
A new propagator for two-layer neural networks in empirical model learning
No full textThis paper proposes a new propagator for a set of Neuron Constraints representing a two-layer network. Neuron Constraints are employed in the context of the Empirical Model Learning technique, that enables optimal decision making over complex systems, beyond the reach of most conventional optimization techniques. The approach is based on embedding a Machine Learning-extracted model into a combinatorial model. Specifically, a Neural Network can be embedded in a Constraint Model by simply encoding each neuron as a Neuron Constraint, which is then propagated individually. The price for such simplicity is the lack of a global view of the network, which may lead to weak bounds. To overcome this issue, we propose a new network-level propagator based on a Lagrangian relaxation, that is solved with a subgradient algorithm. The approach is tested on a thermal-aware dispatching problem on multicore CPUs, and it leads to a massive reduction of the size of the search tree, which is only partially countered by an increased propagation time
A simple and effective decomposition for the multidimensional binpacking constraint
No full textThe multibin-packing constraint captures a fundamental substructure of many assignment problems, where a set of items, each with a fixed number of dimensions, must be assigned to a number of bins with limited capacities. In this work we propose a simple decomposition for multibin-packing that uses a bin-packing constraint for each dimension, a set of all-different constraints automatically derived from a conflict graph, plus two alternative symmetry breaking approaches. Despite its simplicity, the proposed decomposition is very effective on a number of instances recently proposed in the literature
Informed Deep Learning for Epidemics Forecasting
Full text linkThe SARS-CoV-2 pandemic has galvanized the interest of the scientific community toward methodologies apt at predicting the trend of the epidemiological curve, namely, the daily number of infected individuals in the population. One of the critical issues, is providing reliable predictions based on interventions enacted by policy-makers, which is of crucial relevance to assess their effectiveness. In this paper, we provide a novel data-driven application incorporating sub-symbolic knowledge to forecast the spreading of an epidemic depending on a set of interventions. More specifically, we focus on the embedding of classical epidemiological approaches, i.e., compartmental models, into Deep Learning models, to enhance the learning process and provide higher predictive accuracy
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
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