Altruism in the (Social) Network

This paper explores the role of social integration on altruistic behavior. To this aim, we develop a two-stage experimental protocol based on the classic Dictator Game. In the first stage, we ask a group of 77 undergraduate students in Economics to elicit their social network; in the second stage, each of them has to unilaterally decide over the division of a fixed amount of money to be shared with another anonymous member in the group. Our experimental design allows to control for other variables known to be relevant for altruistic behavior: framing and friendship/acquaintance relations. Consistently with previous research, we find that subjects favor their friends and that framing enhances altruistic behavior. Once we control for these effects, social integration (measured by betweenness, a standard centrality measure in network theory) has a positive effect on giving: the larger social isolation within the group, the more likely it is the emergence of selfish behavior. These results suggest that information on the network structure in which subjects are embedded is crucial to account for their behavior.

Using network mesures to test evolved NK-landscapes

In this paper we empirically investigate which are the structural characteristics that can help to predict the complexity of NK-landscape instances for estimation of distribution algorithms. To this end, we evolve instances that maximize the estimation of distribution algorithm complexity in terms of its success rate. Similarly, instances that minimize the algorithm complexity are evolved. We then identify network measures, computed from the structures of the NK-landscape instances, that have a statistically significant difference between the set of easy and hard instances. The features identified are consistently significant for different values of N and K.

Learning in Network Games

We report the findings of an experiment designed to study how people learn and make decisions in network games. Network games offer new opportunities to identify learning rules, since on networks (compared to e.g. random matching) more rules differ in terms of their information requirements. Our experimental design enables us to observe both which actions participants choose and which information they consult before making their choices. We use this information to estimate learning types using maximum likelihood methods. There is substantial heterogeneity in learning types. However, the vast majority of our participants' decisions are best characterized by reinforcement learning or (myopic) best-response learning. The distribution of learning types seems fairly stable across contexts. Neither network topology nor the position of a player in the network seem to substantially affect the estimated distribution of learning types.

New methods for generating populations in Markov network based EDAs: Decimation strategies and model-based template recombination

Methods for generating a new population are a fundamental component of estimation of distribution algorithms (EDAs). They serve to transfer the information contained in the probabilistic model to the new generated population. In EDAs based on Markov networks, methods for generating new populations usually discard information contained in the model to gain in efficiency. Other methods like Gibbs sampling use information about all interactions in the model but are computationally very costly. In this paper we propose new methods for generating new solutions in EDAs based on Markov networks. We introduce approaches based on inference methods for computing the most probable configurations and model-based template recombination. We show that the application of different variants of inference methods can increase the EDAs’ convergence rate and reduce the number of function evaluations needed to find the optimum of binary and non-binary discrete functions.

Crystal structure and phase transition studies in perovskite-type oxides using powder-diffraction techniques and symmetry-mode analysis : SrLnMRuO6 (Ln=La,Pr,Nd; M=Zn,Co,Mg,Ni,Fe) and ALn2CuTi2O9 (A=Ca,Ba; Ln=La,Pr,Nd,Sm)

La tesis se ha centrado en la síntesis y caracterización estructural de materiales tipo perovskita: SrLnMRuO6 (Ln=La,Pr,Nd; M=Zn,Co,Mg,Ni,Fe) y ALn2CuTi2O9 (A=Ca,Ba; Ln=La,Pr,Nd,Sm). El estudio de las estructuras de los materiales se ha realizado mediante el análisis de los patrones de difracción en polvo de rayos-X, sincrotrón y/o neutrones. En el refinamiento por el método de Rietveld de las estructuras se han sustituido las coordenadas atómicas (el método más común), por coordenadas colectivas: las amplitudes de los modos que describen la distorsión de la fase prototipo. Los resultados generales para la serie SrLnMRuO6 (Ln=La,Pr,Nd; M=Zn,Co,Mg,Ni) a temperatura ambiente se ha recogido en un diagrama en el que se han indicado las amplitudes de los modos que transforman de acuerdo a las irreps en función del factor de tolerancia, ya que todos ellos cristalizan en la misma fase monoclínica (P21/n); y a temperaturas altas se ha construido un diagrama de fase. Los materiales SrLnFeRuO6 ( Ln=La,Pr,Nd) y CaLn2CuTi2O9 cristalizan en la fase ortorrómbica Pbnm a temperatura ambiente; mientras que BaLn2CuTi2O9 tienen una estructura más simétrica, I4/mcm. A altas temperaturas se han identificado las transiciones de fase inducidas por el cambio de temperatura.A temperaturas bajas se han analizado las estructuras magnéticas de algunos de los compuestos mediante difracción de neutrones.

Influence of Training Set Selection in Artificial Neural Network-Based Propagation Path Loss Predictions

This paper analyzes the use of artificial neural networks (ANNs) for predicting the received power/path loss in both outdoor and indoor links. The approach followed has been a combined use of ANNs and ray-tracing, the latter allowing the identification and parameterization of the so-called dominant path. A complete description of the process for creating and training an ANN-based model is presented with special emphasis on the training process. More specifically, we will be discussing various techniques to arrive at valid predictions focusing on an optimum selection of the training set. A quantitative analysis based on results from two narrowband measurement campaigns, one outdoors and the other indoors, is also presented.

Expected Fair Allocation in Farsighted Network Formation

I consider cooperation situations where players have network relations. Networks evolve according to a stationary transition probability matrix and at each moment in time players receive payoffs from a stationary allocation rule. Players discount the future by a common factor. The pair formed by an allocation rule and a transition probability matrix is called expected fair if for every link in the network both participants gain, marginally, and in discounted, expected terms, the same from it; and it is called a pairwise network formation procedure if the probability that a link is created (or eliminated) is positive if the discounted, expected gains to its two participants are positive too. The main result is the existence, for the discount factor small enough, of an expected fair and pairwise network formation procedure where the allocation rule is component balanced, meaning it distributes the total value of any maximal connected subnetwork among its participants. This existence result holds for all discount factors when the pairwise network formation procedure is restricted. I finally provide some comparison with previous models of farsighted network formation.

Analysis of Spanish text-thesaurus as a complex network

[EN]Based on the theoretical tools of Complex Networks, this work provides a basic descriptive study of a synonyms dictionary, the Spanish Open Thesaurus represented as a graph. We study the main structural measures of the network compared with those of a random graph. Numerical results show that Open-Thesaurus is a graph whose topological properties approximate a scale-free network, but seems not to present the small-world property because of its sparse structure. We also found that the words of highest betweenness centrality are terms that suggest the vocabulary of psychoanalysis: placer (pleasure), ayudante (in the sense of assistant or worker), and regular (to regulate).

Enhanced Operation of Electricity Distribution Grids Through Smart Metering PLC Network Monitoring, Analysis and Grid Conditioning

Low Voltage (LV) electricity distribution grid operations can be improved through a combination of new smart metering systems' capabilities based on real time Power Line Communications (PLC) and LV grid topology mapping. This paper presents two novel contributions. The first one is a new methodology developed for smart metering PLC network monitoring and analysis. It can be used to obtain relevant information from the grid, thus adding value to existing smart metering deployments and facilitating utility operational activities. A second contribution describes grid conditioning used to obtain LV feeder and phase identification of all connected smart electric meters. Real time availability of such information may help utilities with grid planning, fault location and a more accurate point of supply management.

Analytical efficiency evaluation of a network mobility management protocol for Intelligent Transportation Systems

One of the major concerns in an Intelligent Transportation System (ITS) scenario, such as that which may be found on a long-distance train service, is the provision of efficient communication services, satisfying users' expectations, and fulfilling even highly demanding application requirements, such as safety-oriented services. In an ITS scenario, it is common to have a significant amount of onboard devices that comprise a cluster of nodes (a mobile network) that demand connectivity to the outside networks. This demand has to be satisfied without service disruption. Consequently, the mobility of the mobile network has to be managed. Due to the nature of mobile networks, efficient and lightweight protocols are desired in the ITS context to ensure adequate service performance. However, the security is also a key factor in this scenario. Since the management of the mobility is essential for providing communications, the protocol for managing this mobility has to be protected. Furthermore, there are safety-oriented services in this scenario, so user application data should also be protected. Nevertheless, providing security is expensive in terms of efficiency. Based on this considerations, we have developed a solution for managing the network mobility for ITS scenarios: the NeMHIP protocol. This approach provides a secure management of network mobility in an efficient manner. In this article, we present this protocol and the strategy developed to maintain its security and efficiency in satisfactory levels. We also present the developed analytical models to analyze quantitatively the efficiency of the protocol. More specifically, we have developed models for assessing it in terms of signaling cost, which demonstrates that NeMHIP generates up to 73.47% less signaling compared to other relevant approaches. Therefore, the results obtained demonstrate that NeMHIP is the most efficient and secure solution for providing communications in mobile network scenarios such as in an ITS context.

Valuing Expansions of the Electricity Transmission Network under Uncertainty: The Binodal Case

Transmission investments are currently needed to meet an increasing electricity demand, to address security of supply concerns, and to reach carbon-emissions targets. A key issue when assessing the benefits from an expanded grid concerns the valuation of the uncertain cash flows that result from the expansion. We propose a valuation model that accommodates both physical and economic uncertainties following the Real Options approach. It combines optimization techniques with Monte Carlo simulation. We illustrate the use of our model in a simplified, two-node grid and assess the decision whether to invest or not in a particular upgrade. The generation mix includes coal-and natural gas-fired stations that operate under carbon constraints. The underlying parameters are estimated from observed market data.

Valuing a Natura 2000 network site to inform land use options using a discrete choice experiment: An illustration from the Basque Country

One of the main problems that public institutions face in the management of protected areas, such as the European Natura 2000 network, is determining how to design and implement sustainable management plans that account for the wide range of marketed and non-marketed benefits they provide to society. This paper presents an application of a stated preference valuation approach aimed at evaluating the social preferences of the population of the Basque Country, Spain, for the key attributes of a regional Natura 2000 network site. According to our results, individuals’ willingness-to-pay (WTP) is higher for attributes associated with non-use values (native tree species and biodiversity conservation) than for attributes associated with use values (agricultural development and commercial forestry). The paper concludes that management policies related to Natura 2000 network sites should account for both for the importance of non-use values and the heterogeneity of the population's preferences in order to minimize potential land use conflicts.

Towards a participatory integrated assessment approach for planning and managing Natura 2000 network sites

Managing protected areas implies dealing with complex social-ecological systems where multiple dimensions (social, institutional, economic and ecological) interact over time for the delivery of ecosystem services. Uni-dimensional and top-down management approaches have been unable to capture this complexity. Instead, new integrated approaches that acknowledge the diversity of social actors in the decision making process are required. In this paper we put forward a novel participatory assessment approach which integrates multiple methodologies to reflect different value articulating institutions in the case of a Natura 2000 network site in the Basque Country. It integrates within a social multi-criteria evaluation framework, both the economic values of ecosystem services through a choice experiment model and ecological values by means of a spatial bio-geographic assessment. By capturing confronting social and institutional conflicts in protected areas the participatory integrated assessment approach presented here can help decision makers for better planning and managing Natura 2000 sites.

INTERPOL's surveillance network in curbing transnational terrorism

This paper investigates the role that INTERPOL surveillance – the Mobile INTERPOL Network Database (MIND) and the Fixed INTERPOL Network Database (FIND) – played in the War on Terror since its inception in 2005. MIND/FIND surveillance allows countries to screen people and documents systematically at border crossings against INTERPOL databases on terrorists, fugitives, and stolen and lost travel documents. Such documents have been used in the past by terrorists to transit borders. By applying methods developed in the treatment-effects literature, this paper establishes that countries adopting MIND/FIND experienced fewer transnational terrorist attacks than had they not adopted MIND/FIND. Our estimates indicate that, on average, during 2008–2011, adopting and using MIND/FIND results in 1.23 fewer transnational terrorist incidents each year per 100 million people. Thus, a country like France with a population just above 64 million people in 2008 would have 0.79 fewer transnational terrorist incidents per year owing to its use of INTERPOL surveillance. For most treatment countries, this amounts to a sizeable proportional reduction of about 60 per cent.

Towards a Participatory Integrated Assessment Approach for Planning and Managing Natura 2000 Network Sites

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