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Generalized characteristic functions extend characteristic functions of 'classical' TU-games by assigning a real number to every ordered coalition being a permutation of any subset of the player set. Such generalized characteristic functions can be applied when the earnings or costs of cooperation among a set of players depend on the order in which the players enter a coalition. In the literature, the two main solutions for generalized characteristic functions are the one of Nowak and Radzik (1994), shortly called NR-value, and the one introduced by Sanchez and Bergantinos (1997), shortly called SB-value. In this paper, we introduce the axiom of order monotonicity with respect to the order of the players in a unanimity coalition, requiring that players who enter earlier should get not more in the corresponding (ordered) unanimity game than players who enter later. We propose several classes of order monotonic solutions for generalized characteristic functions that contain the NR-value and SB-value as special (extreme) cases. We also provide axiomatizations of these classes., Depto. de Estadística y Ciencia de los Datos, Fac. de Estudios Estadísticos, TRUE, pub

In this paper we deal with TU games in which cooperation is restricted by means of a weighted network. We admit several interpretations for the weights of a link: capacity of the communication channel, flow across it, intimacy or intensity in the relation, distance between both incident nodes/players, cost of building or main-taining the communication link or even probability of the relation (as in Calvo et al., 1999). Then, accordingly with the different interpretations, we introduce several point solutions for these restricted games in a parallel way to the familiar environ of Myerson. Finally, we characterize these values in terms of the (adapted) component efficiency, fairness and balanced contributions properties and we analyze the extend to which they satisfy the stability property., Spanish Government. Plan Nacional de I+D+i, Depto. de Estadística y Ciencia de los Datos, Fac. de Estudios Estadísticos, TRUE, pub

© 2017 Elsevier B.V. The European Journal of Operational Research (EJOR) published its first issue in 1977. This paper presents a general overview of the journal over its lifetime by using bibliometric indicators. We discuss its performance compared to other journals in the field and identify key contributing countries/institutions/authors as well as trends in research topics based on the Web of Science Core Collection database. The results indicate that EJOR is one of the leading journals in the area of operational research (OR) and management science (MS), with a wide range of authors from institutions and countries from all over the world publishing in it. Graphical visualization of similarities (VOS) provides further insights into how EJOR links to other journals and how it links researchers across the globe.

In this work, we investigate methods for gaining greater insight from hydrological model runs conducted for uncertainty quantification and model differentiation. We frame the sensitivity analysis questions in terms of the main purposes of sensitivity analysis: parameter prioritization, trend identification, and interaction quantification. For parameter prioritization, we consider variance-based sensitivity measures, sensitivity indices based on the L1-norm, the Kuiper metric, and the sensitivity indices of the DELSA methods. For trend identification, we investigate insights derived from graphing the one-way ANOVA sensitivity functions, the recently introduced CUSUNORO plots, and derivative scatterplots. For interaction quantification, we consider information delivered by variance-based sensitivity indices. We rely on the so-called given-data principle, in which results from a set of model runs are used to perform a defined set of analyses. One avoids using specific designs for each insight, thus controlling the computational burden. The methodology is applied to a hydrological model of a river in Belgium simulated using the well-established Framework for Understanding Structural Errors (FUSE) on five alternative configurations. The findings show that the integration of the chosen methods provides insights unavailable in most other analyses.

Scenarios showing future greenhouse gas emissions are needed to estimate climate impacts and the mitigation efforts required for climate stabilization. Recently, the Shared Socioeconomic Pathways (SSPs) have been introduced to describe alternative social, economic and technical narratives, spanning a wide range of plausible futures in terms of challenges to mitigation and adaptation1. Thus far the key drivers of the uncertainty in emissions projections have not been robustly disentangled. Here we assess the sensitivities of future CO2 emissions to key drivers characterizing the SSPs. We use six state-of-the-art integrated assessment models with different structural characteristics, and study the impact of five families of parameters, related to population, income, energy efficiency, fossil fuel availability, and low-carbon energy technology development. A recently developed sensitivity analysis algorithm2 allows us to parsimoniously compute both the direct and interaction effects of each of these drivers on cumulative emissions. The study reveals that the SSP assumptions about energy intensity and economic growth are the most important determinants of future CO2 emissions from energy combustion, both with and without a climate policy. Interaction terms between parameters are shown to be important determinants of the total sensitivities.