Unraveling Public Good Games: The Role of Priors

This paper provides experimental evidence on how players predict end game effects in a linear public good game. Our regression analysis yields a measure of the relative importance of priors and signals on subjects\' beliefs on contributions and allow us to conclude that, firstly, the weight of the signal is relatively unimportant, while priors have a large weight and, secondly, priors are the same for all periods. Hence, subjects do not expect end game effects and there is very little updating of beliefs.

MPI parallel programming of mixed integer optimization problems using CPLEX with COIN-OR

The aim of this technical report is to present some detailed explanations in order to help to understand and use the Message Passing Interface (MPI) parallel programming for solving several mixed integer optimization problems. We have developed a C++ experimental code that uses the IBM ILOG CPLEX optimizer within the COmputational INfrastructure for Operations Research (COIN-OR) and MPI parallel computing for solving the optimization models under UNIX-like systems. The computational experience illustrates how can we solve 44 optimization problems which are asymmetric with respect to the number of integer and continuous variables and the number of constraints. We also report a comparative with the speedup and efficiency of several strategies implemented for some available number of threads.

Scenario Cluster Lagrangian Decomposition in two stage stochastic mixed 0-1 optimization

In this paper we introduce four scenario Cluster based Lagrangian Decomposition (CLD) procedures for obtaining strong lower bounds to the (optimal) solution value of two-stage stochastic mixed 0-1 problems. At each iteration of the Lagrangian based procedures, the traditional aim consists of obtaining the solution value of the corresponding Lagrangian dual via solving scenario submodels once the nonanticipativity constraints have been dualized. Instead of considering a splitting variable representation over the set of scenarios, we propose to decompose the model into a set of scenario clusters. We compare the computational performance of the four Lagrange multiplier updating procedures, namely the Subgradient Method, the Volume Algorithm, the Progressive Hedging Algorithm and the Dynamic Constrained Cutting Plane scheme for different numbers of scenario clusters and different dimensions of the original problem. Our computational experience shows that the CLD bound and its computational effort depend on the number of scenario clusters to consider. In any case, our results show that the CLD procedures outperform the traditional LD scheme for single scenarios both in the quality of the bounds and computational effort. All the procedures have been implemented in a C++ experimental code. A broad computational experience is reported on a test of randomly generated instances by using the MIP solvers COIN-OR and CPLEX for the auxiliary mixed 0-1 cluster submodels, this last solver within the open source engine COIN-OR. We also give computational evidence of the model tightening effect that the preprocessing techniques, cut generation and appending and parallel computing tools have in stochastic integer optimization. Finally, we have observed that the plain use of both solvers does not provide the optimal solution of the instances included in the testbed with which we have experimented but for two toy instances in affordable elapsed time. On the other hand the proposed procedures provide strong lower bounds (or the same solution value) in a considerably shorter elapsed time for the quasi-optimal solution obtained by other means for the original stochastic problem.

Reference Points Based on Dynamic Optimisation: A Versatil Algorithm for Mixed Fishery Management with Bio-economic Agestructured Models

Single-species management objectives may not be consistent within mixed fisheries. They may lead species to unsafe situations, promote discarding of over-quota and/or misreporting of catches. We provide an algorithm for characterising bio-economic reference points for a mixed fishery as the steady-state solution of a dynamic optimal management problem. The optimisation problem takes into account: i) that species are fishing simultaneously in unselective fishing operations and ii)intertemporal discounting and fleet costs to relate reference points to discounted economic profits along optimal trajectories. We illustrate how the algorithm can be implemented by applying it to the European Northern Stock of Hake (Merluccius merluccius), where fleets also capture Northern megrim (Lepidorhombus whiffiagonis) and Northern anglerfish (Lophius piscatorius and Lophius budegassa). We find that optimal mixed management leads to a target reference point that is quite similar to the 2/3 of the Fmsy single-species (hake) target. Mixed management is superior to singlespecies management because it leads the fishery to higher discounted profits with higher long-term SSB for all species. We calculate that the losses due to the use of the Fmsy single-species (hake) target in this mixed fishery account for 11.4% of total discounted profits.

Generating cluster submodels from a multistage stochastic mixed integer optimization model using break stage

We present a scheme to generate clusters submodels with stage ordering from a (symmetric or a nonsymmetric one) multistage stochastic mixed integer optimization model using break stage. We consider a stochastic model in compact representation and MPS format with a known scenario tree. The cluster submodels are built by storing first the 0-1 the variables, stage by stage, and then the continuous ones, also stage by stage. A C++ experimental code has been implemented for reordering the stochastic model as well as the cluster decomposition after the relaxation of the non-anticipativiy constraints until the so-called breakstage. The computational experience shows better performance of the stage ordering in terms of elapsed time in a randomly generated testbed of multistage stochastic mixed integer problems.

Some experiments on solving multistage stochastic mixed 0-1 programs with time stochastic dominance constraints

In this work we extend to the multistage case two recent risk averse measures for two-stage stochastic programs based on first- and second-order stochastic dominance constraints induced by mixed-integer linear recourse. Additionally, we consider Time Stochastic Dominance (TSD) along a given horizon. Given the dimensions of medium-sized problems augmented by the new variables and constraints required by those risk measures, it is unrealistic to solve the problem up to optimality by plain use of MIP solvers in a reasonable computing time, at least. Instead of it, decomposition algorithms of some type should be used. We present an extension of our Branch-and-Fix Coordination algorithm, so named BFC-TSD, where a special treatment is given to cross scenario group constraints that link variables from different scenario groups. A broad computational experience is presented by comparing the risk neutral approach and the tested risk averse strategies. The performance of the new version of the BFC algorithm versus the plain use of a state-of-the-artMIP solver is also reported.

Efficiency vs. Stability in a Mixed Network Formation Model

Documentos de Trabajo

Coincidence point theorems in quasi-metric spaces without assuming the mixed monotone property and consequences in G-metric spaces

In this paper, we present some coincidence point theorems in the setting of quasi-metric spaces that can be applied to operators which not necessarily have the mixed monotone property. As a consequence, we particularize our results to the field of metric spaces, partially ordered metric spaces and G-metric spaces, obtaining some very recent results. Finally, we show how to use our main theorems to obtain coupled, tripled, quadrupled and multidimensional coincidence point results.

Influence of synthetic method on the properties of La0.5Ba0.5FeO3 SOFC cathode

Power Point presentado en The Energy and Materials Research Conference - EMR2015 celebrado en Madrid (España) entre el 25-27 de febrero de 2015

Reutilización de escoria negra de acería como soporte en biofiltros para la eliminación de H2S del biogás.

[EN]Due to the limitations associated with fossil fuels it is necessary to promote energy sources that are renewable as well as eco-friendly, such as biogas generated in anaerobic digesters. The biogas, composed principally of methane and CO2, is the result of the biodegradation of organic matter under anaerobic conditions. Its use as fuel is limited by the presence of minority compounds such as hydrogen sulphide (H2S); therefore, its pre-treatment is necessary. Currently there are various technologies for the removal of H2S from a gas stream, but most of them are based on physic-chemical treatments which have a number of drawbacks as reactive consumption, generation of secondary flows, etc. Biofiltration has been used as an efficient and low cost alternative to conventional purification processes, and excellent results for the degradation of H2S have been obtained. However process can be limited due to the progressive ageing of the support material, along with the loss of nutrients and other specific characteristics necessary for the good development of biomass. The purpose of this project is to develop a mixed support consisting of a mixture of an organic material and an inorganic support for its application in the removal of the H2S from biogas. This support material helps to optimize the characteristics of the bed and extend its lifespan. The development of such material will contribute to the implementation of biofiltration for treating biogas from anaerobic digesters for its use as biofuel. The inorganic material used is electric arc furnace (EAF) black slag, a by-product generated in large quantities in the production of steel in the Basque Country. Although traditionally the slag has been used in civil engineering, its physicochemical characteristics make it suitable for reuse as a filter medium in biofiltration. The main conclusion drawn from the experimental results is that EAF black slag is a suitable co-packing material in organic biofilters treating H2S-polluted gaseous streams. High pollutant removal rates have been achieved during the whole experimental period. The removal capacity recorded in biofilters with less inorganic material was higher than in those with higher slag portion. Nevertheless, all the biofilters have shown a satisfactory response even at high inlet loads (48 g·m-3·h-1), where the RE has not decreased over 82%.

Plan de Marketing Fisioterapia BE GOOD

El objetivo del presente documento es definir y presentar el Plan de Marketing asociado a una empresa basada en una propuesta empresarial. La empresa a la que se va a hacer referencia en dicho Plan es un centro fisioterapéutico que se denominará “Be Good”. La clínica fisioterapia “Be Good”, se situará en la calle “Villa de Plencia” localizada en el municipio de Getxo y nacerá con el objetivo de ofrecer un servicio de calidad dirigido a la cura y prevención de lesiones músculo-esqueléticas, afecciones respiratorias, patologías neurológicas y, en general, a la satisfacción de cualquier tipo de persona que solicite sus servicios. Aunque existan diferentes metodologías, para llevar a cabo el Plan de Marketing se ha decidido seguir el procedimiento propuesto por José María Sainz de Vicuña (2013). Por medio de la metodología seleccionada, el Plan de Marketing descrito en el presente documento tratará de mejorar la eficacia en la toma de decisiones que influyan en la empresa. Adicionalmente, se actuará de medio para el logro de los objetivos planteados a continuación. Por último, facilitará y optimizará la comunicación interna de la empresa de cara a disponer de procesos de evaluación y control más eficientes responsabilizando al área de marketing de los resultados obtenidos.

Efficacy of a cognitive and behavioural psychotherapy applied by primary care psychologists in patients with mixed anxiety-depressive disorder: a research protocol

Background: In contrast with the recommendations of clinical practice guidelines, the most common treatment for anxiety and depressive disorders in primary care is pharmacological. The aim of this study is to assess the efficacy of a cognitive-behavioural psychological intervention, delivered by primary care psychologists in patients with mixed anxiety-depressive disorder compared to usual care. Methods/Design: This is an open-label, multicentre, randomized, and controlled study with two parallel groups. A random sample of 246 patients will be recruited with mild-to-moderate mixed anxiety-depressive disorder, from the target population on the lists of 41 primary care doctors. Patients will be randomly assigned to the intervention group, who will receive standardised cognitive-behavioural therapy delivered by psychologists together with usual care, or to a control group, who will receive usual care alone. The cognitive-behavioural therapy intervention is composed of eight individual 60-minute face-to face sessions conducted in eight consecutive weeks. A follow-up session will be conducted over the telephone, for reinforcement or referral as appropriate, 6 months after the intervention, as required. The primary outcome variable will be the change in scores on the Short Form-36 General Health Survey. We will also measure the change in the frequency and intensity of anxiety symptoms (State-Trait Anxiety Inventory) and depression (Beck Depression Inventory) at baseline, and 3, 6 and 12 months later. Additionally, we will collect information on the use of drugs and health care services. Discussion: The aim of this study is to assess the efficacy of a primary care-based cognitive-behavioural psychological intervention in patients with mixed anxiety-depressive disorder. The international scientific evidence has demonstrated the need for psychologists in primary care. However, given the differences between health policies and health services, it is important to test the effect of these psychological interventions in our geographical setting.