Preference intensity in MCDM when an additive utility function represents DM preferences

We propose a new method for ranking alternatives in multicriteria decision-making problems when there is imprecision concerning the alternative performances, component utility functions and weights. We assume decision maker?s preferences are represented by an additive multiattribute utility function, in which weights can be modeled by independent normal variables, fuzzy numbers, value intervals or by an ordinal relation. The approaches are based on dominance measures or exploring the weight space in order to describe which ratings would make each alternative the preferred one. On the one hand, the approaches based on dominance measures compute the minimum utility difference among pairs of alternatives. Then, they compute a measure by which to rank the alternatives. On the other hand, the approaches based on exploring the weight space compute confidence factors describing the reliability of the analysis. These methods are compared using Monte Carlo simulation.

Brief Announcement: Node Sampling Using Centrifugal Random Walks.

We propose distributed algorithms for sampling networks based on a new class of random walks that we call Centrifugal Random Walks (CRW). A CRW is a random walk that starts at a source and always moves away from it. We propose CRW algorithms for connected networks with arbitrary probability distributions, and for grids and networks with regular concentric connectivity with distance based distributions. All CRW sampling algorithms select a node with the exact probability distribution, do not need warm-up, and end in a number of hops bounded by the network diameter.

Node sampling using random centrifugal walks

Sampling a network with a given probability distribution has been identified as a useful operation. In this paper we propose distributed algorithms for sampling networks, so that nodes are selected by a special node, called the source, with a given probability distribution. All these algorithms are based on a new class of random walks, that we call Random Centrifugal Walks (RCW). A RCW is a random walk that starts at the source and always moves away from it. Firstly, an algorithm to sample any connected network using RCW is proposed. The algorithm assumes that each node has a weight, so that the sampling process must select a node with a probability proportional to its weight. This algorithm requires a preprocessing phase before the sampling of nodes. In particular, a minimum diameter spanning tree (MDST) is created in the network, and then nodes weights are efficiently aggregated using the tree. The good news are that the preprocessing is done only once, regardless of the number of sources and the number of samples taken from the network. After that, every sample is done with a RCW whose length is bounded by the network diameter. Secondly, RCW algorithms that do not require preprocessing are proposed for grids and networks with regular concentric connectivity, for the case when the probability of selecting a node is a function of its distance to the source. The key features of the RCW algorithms (unlike previous Markovian approaches) are that (1) they do not need to warm-up (stabilize), (2) the sampling always finishes in a number of hops bounded by the network diameter, and (3) it selects a node with the exact probability distribution.

Novel fast random search clustering approach for mixing matrix identification in MIMO linear blind inverse problems with sparse inputs

In this paper we propose a novel fast random search clustering (RSC) algorithm for mixing matrix identification in multiple input multiple output (MIMO) linear blind inverse problems with sparse inputs. The proposed approach is based on the clustering of the observations around the directions given by the columns of the mixing matrix that occurs typically for sparse inputs. Exploiting this fact, the RSC algorithm proceeds by parameterizing the mixing matrix using hyperspherical coordinates, randomly selecting candidate basis vectors (i.e. clustering directions) from the observations, and accepting or rejecting them according to a binary hypothesis test based on the Neyman–Pearson criterion. The RSC algorithm is not tailored to any specific distribution for the sources, can deal with an arbitrary number of inputs and outputs (thus solving the difficult under-determined problem), and is applicable to both instantaneous and convolutive mixtures. Extensive simulations for synthetic and real data with different number of inputs and outputs, data size, sparsity factors of the inputs and signal to noise ratios confirm the good performance of the proposed approach under moderate/high signal to noise ratios. RESUMEN. Método de separación ciega de fuentes para señales dispersas basado en la identificación de la matriz de mezcla mediante técnicas de "clustering" aleatorio.

Efficient random variable generation: ratio of uniforms and polar rejection sampling

Monte Carlo techniques, which require the generation of samples from some target density, are often the only alternative for performing Bayesian inference. Two classic sampling techniques to draw independent samples are the ratio of uniforms (RoU) and rejection sampling (RS). An efficient sampling algorithm is proposed combining the RoU and polar RS (i.e. RS inside a sector of a circle using polar coordinates). Its efficiency is shown in drawing samples from truncated Cauchy and Gaussian random variables, which have many important applications in signal processing and communications. RESUMEN. Método eficiente para generar algunas variables aleatorias de uso común en procesado de señal y comunicaciones (por ejemplo, Gaussianas o Cauchy truncadas) mediante la combinación de dos técnicas: "ratio of uniforms" y "rejection sampling".

Assessing urban mobility through participatory scenario building and combined utility-regret assessment

La planificación de la movilidad sostenible urbana es una tarea compleja que implica un alto grado de incertidumbre debido al horizonte de planificación a largo plazo, la amplia gama de paquetes de políticas posibles, la necesidad de una aplicación efectiva y eficiente, la gran escala geográfica, la necesidad de considerar objetivos económicos, sociales y ambientales, y la respuesta del viajero a los diferentes cursos de acción y su aceptabilidad política (Shiftan et al., 2003). Además, con las tendencias inevitables en motorización y urbanización, la demanda de terrenos y recursos de movilidad en las ciudades está aumentando dramáticamente. Como consecuencia de ello, los problemas de congestión de tráfico, deterioro ambiental, contaminación del aire, consumo de energía, desigualdades en la comunidad, etc. se hacen más y más críticos para la sociedad. Esta situación no es estable a largo plazo. Para enfrentarse a estos desafíos y conseguir un desarrollo sostenible, es necesario considerar una estrategia de planificación urbana a largo plazo, que aborde las necesarias implicaciones potencialmente importantes. Esta tesis contribuye a las herramientas de evaluación a largo plazo de la movilidad urbana estableciendo una metodología innovadora para el análisis y optimización de dos tipos de medidas de gestión de la demanda del transporte (TDM). La metodología nueva realizado se basa en la flexibilización de la toma de decisiones basadas en utilidad, integrando diversos mecanismos de decisión contrariedad‐anticipada y combinados utilidad‐contrariedad en un marco integral de planificación del transporte. La metodología propuesta incluye dos aspectos principales: 1) La construcción de escenarios con una o varias medidas TDM usando el método de encuesta que incorpora la teoría “regret”. La construcción de escenarios para este trabajo se hace para considerar específicamente la implementación de cada medida TDM en el marco temporal y marco espacial. Al final, se construyen 13 escenarios TDM en términos del más deseable, el más posible y el de menor grado de “regret” como resultado de una encuesta en dos rondas a expertos en el tema. 2) A continuación se procede al desarrollo de un marco de evaluación estratégica, basado en un Análisis Multicriterio de Toma de Decisiones (Multicriteria Decision Analysis, MCDA) y en un modelo “regret”. Este marco de evaluación se utiliza para comparar la contribución de los distintos escenarios TDM a la movilidad sostenible y para determinar el mejor escenario utilizando no sólo el valor objetivo de utilidad objetivo obtenido en el análisis orientado a utilidad MCDA, sino también el valor de “regret” que se calcula por medio del modelo “regret” MCDA. La función objetivo del MCDA se integra en un modelo de interacción de uso del suelo y transporte que se usa para optimizar y evaluar los impactos a largo plazo de los escenarios TDM previamente construidos. Un modelo de “regret”, llamado “referencedependent regret model (RDRM)” (modelo de contrariedad dependiente de referencias), se ha adaptado para analizar la contribución de cada escenario TDM desde un punto de vista subjetivo. La validación de la metodología se realiza mediante su aplicación a un caso de estudio en la provincia de Madrid. La metodología propuesta define pues un procedimiento técnico detallado para la evaluación de los impactos estratégicos de la aplicación de medidas de gestión de la demanda en el transporte, que se considera que constituye una herramienta de planificación útil, transparente y flexible, tanto para los planificadores como para los responsables de la gestión del transporte. Planning sustainable urban mobility is a complex task involving a high degree of uncertainty due to the long‐term planning horizon, the wide spectrum of potential policy packages, the need for effective and efficient implementation, the large geographical scale, the necessity to consider economic, social, and environmental goals, and the traveller’s response to the various action courses and their political acceptability (Shiftan et al., 2003). Moreover, with the inevitable trends on motorisation and urbanisation, the demand for land and mobility in cities is growing dramatically. Consequently, the problems of traffic congestion, environmental deterioration, air pollution, energy consumption, and community inequity etc., are becoming more and more critical for the society (EU, 2011). Certainly, this course is not sustainable in the long term. To address this challenge and achieve sustainable development, a long‐term perspective strategic urban plan, with its potentially important implications, should be established. This thesis contributes on assessing long‐term urban mobility by establishing an innovative methodology for optimizing and evaluating two types of transport demand management measures (TDM). The new methodology aims at relaxing the utility‐based decision‐making assumption by embedding anticipated‐regret and combined utilityregret decision mechanisms in an integrated transport planning framework. The proposed methodology includes two major aspects: 1) Construction of policy scenarios within a single measure or combined TDM policy‐packages using the survey method incorporating the regret theory. The purpose of building the TDM scenarios in this work is to address the specific implementation in terms of time frame and geographic scale for each TDM measure. Finally, 13 TDM scenarios are built in terms of the most desirable, the most expected and the least regret choice by means of the two‐round Delphi based survey. 2) Development of the combined utility‐regret analysis framework based on multicriteria decision analysis (MCDA). This assessment framework is used to compare the contribution of the TDM scenario towards sustainable mobility and to determine the best scenario considering not only the objective utility value obtained from the utilitybased MCDA, but also a regret value that is calculated via a regret‐based MCDA. The objective function of the utility‐based MCDA is integrated in a land use and transport interaction model and is used for optimizing and assessing the long term impacts of the constructed TDM scenarios. A regret based model, called referente dependent regret model (RDRM) is adapted to analyse the contribution of each TDM scenario in terms of a subjective point of view. The suggested methodology is implemented and validated in the case of Madrid. It defines a comprehensive technical procedure for assessing strategic effects of transport demand management measures, which can be useful, transparent and flexible planning tool both for planners and decision‐makers.

An Integrated Transport Planning Framework Involving a Combined Utility-Regret Approach

Sustainable transport planning requires an integrated approach involving strategic planning, impact analysis and multi-criteria evaluation. This study aims at relaxing the utility-based decision-making assumption by newly embedding anticipated-regret and combined utility-regret decision mechanisms in an integrated transport planning framework. The framework consists of a two-round Delphi survey, an integrated land-use and transport model for Madrid, and multi-criteria analysis. Results show that (i) regret-based ranking has similar mean but larger variance than utility-based ranking; (ii) the least-regret scenario forms a compromise between the desired and the expected scenarios; (iii) the least-regret scenario can lead to higher user benefits in the short-term and lower user benefits in the long-term; (iv) utility-based, regret-based and combined utility-regret-based multi-criteria analysis result in different rankings of policy packages; and (v) the combined utility-regret ranking is more informative compared with utility-based or regret-based ranking.

Contribuciones al análisis de problemas supercomplejos de toma de decisiones

Los fundamentos de la Teoría de la Decisión Bayesiana proporcionan un marco coherente en el que se pueden resolver los problemas de toma de decisiones. La creciente disponibilidad de ordenadores potentes está llevando a tratar problemas cada vez más complejos con numerosas fuentes de incertidumbre multidimensionales; varios objetivos conflictivos; preferencias, metas y creencias cambiantes en el tiempo y distintos grupos afectados por las decisiones. Estos factores, a su vez, exigen mejores herramientas de representación de problemas; imponen fuertes restricciones cognitivas sobre los decisores y conllevan difíciles problemas computacionales. Esta tesis tratará estos tres aspectos. En el Capítulo 1, proporcionamos una revisión crítica de los principales métodos gráficos de representación y resolución de problemas, concluyendo con algunas recomendaciones fundamentales y generalizaciones. Nuestro segundo comentario nos lleva a estudiar tales métodos cuando sólo disponemos de información parcial sobre las preferencias y creencias del decisor. En el Capítulo 2, estudiamos este problema cuando empleamos diagramas de influencia (DI). Damos un algoritmo para calcular las soluciones no dominadas en un DI y analizamos varios conceptos de solución ad hoc. El último aspecto se estudia en los Capítulos 3 y 4. Motivado por una aplicación de gestión de embalses, introducimos un método heurístico para resolver problemas de decisión secuenciales. Como muestra resultados muy buenos, extendemos la idea a problemas secuenciales generales y cuantificamos su bondad. Exploramos después en varias direcciones la aplicación de métodos de simulación al Análisis de Decisiones. Introducimos primero métodos de Monte Cario para aproximar el conjunto no dominado en problemas continuos. Después, proporcionamos un método de Monte Cario basado en cadenas de Markov para problemas con información completa con estructura general: las decisiones y las variables aleatorias pueden ser continuas, y la función de utilidad puede ser arbitraria. Nuestro esquema es aplicable a muchos problemas modelizados como DI. Finalizamos con un capítulo de conclusiones y problemas abiertos.---ABSTRACT---The foundations of Bayesian Decisión Theory provide a coherent framework in which decisión making problems may be solved. With the advent of powerful computers and given the many challenging problems we face, we are gradually attempting to solve more and more complex decisión making problems with high and multidimensional uncertainty, múltiple objectives, influence of time over decisión tasks and influence over many groups. These complexity factors demand better representation tools for decisión making problems; place strong cognitive demands on the decison maker judgements; and lead to involved computational problems. This thesis will deal with these three topics. In recent years, many representation tools have been developed for decisión making problems. In Chapter 1, we provide a critical review of most of them and conclude with recommendations and generalisations. Given our second query, we could wonder how may we deal with those representation tools when there is only partial information. In Chapter 2, we find out how to deal with such a problem when it is structured as an influence diagram (ID). We give an algorithm to compute nondominated solutions in ID's and analyse several ad hoc solution concepts.- The last issue is studied in Chapters 3 and 4. In a reservoir management case study, we have introduced a heuristic method for solving sequential decisión making problems. Since it shows very good performance, we extend the idea to general problems and quantify its goodness. We explore then in several directions the application of simulation based methods to Decisión Analysis. We first introduce Monte Cario methods to approximate the nondominated set in continuous problems. Then, we provide a Monte Cario Markov Chain method for problems under total information with general structure: decisions and random variables may be continuous, and the utility function may be arbitrary. Our scheme is applicable to many problems modeled as IDs. We conclude with discussions and several open problems.

Random positions of dendritic spines in human cerebral cortex

Dendritic spines establish most excitatory synapses in the brain and are located in Purkinje cell’s dendrites along helical paths, perhaps maximizing the probability to contact different axons. To test whether spine helixes also occur in neocortex, we reconstructed >500 dendritic segments from adult human cortex obtained from autopsies. With Fourier analysis and spatial statistics, we analyzed spine position along apical and basal dendrites of layer 3 pyramidal neurons from frontal, temporal, and cingulate cortex. Although we occasionally detected helical positioning, for the great majority of dendrites we could not reject the null hypothesis of spatial randomness in spine locations, either in apical or basal dendrites, in neurons of different cortical areas or among spines of different volumes and lengths. We conclude that in adult human neocortex spine positions are mostly random. We discuss the relevance of these results for spine formation and plasticity and their functional impact for cortical circuits.

Random positioning of dendritic spines in the human cerebral cortex

Dendritic spines establish most excitatory synapses in the brain and are located in Purkinje cell?s dendrites along helical paths, perhaps maximizing the probability to contact different axons. To test whether spine helixes also occur in neocortex, we reconstructed ?500 dendritic segments from adult human cortex obtained from autopsies. With Fourier analysis and spatial statistics, we analyzed spine position along apical and basal dendrites of layer 3 pyramidal neurons from frontal, temporal, and cingulate cortex. Although we occasionally detected helical positioning, for the great majority of dendrites we could not reject the null hypothesis of spatial randomness in spine locations, either in apical or basal dendrites, in neurons of different cortical areas or among spines of different volumes and lengths. We conclude that in adult human neocortex spine positions are mostly random. We discuss the relevance of these results for spine formation and plasticity and their functional impact for cortical circuits.

Computer simulation of random parckings for self-similar particle size distributions in soil and granular materials: porosity and pore size distribution

A 2D computer simulation method of random packings is applied to sets of particles generated by a self-similar uniparametric model for particle size distributions (PSDs) in granular media. The parameter p which controls the model is the proportion of mass of particles corresponding to the left half of the normalized size interval [0,1]. First the influence on the total porosity of the parameter p is analyzed and interpreted. It is shown that such parameter, and the fractal exponent of the associated power scaling, are efficient packing parameters, but this last one is not in the way predicted in a former published work addressing an analogous research in artificial granular materials. The total porosity reaches the minimum value for p = 0.6. Limited information on the pore size distribution is obtained from the packing simulations and by means of morphological analysis methods. Results show that the range of pore sizes increases for decreasing values of p showing also different shape in the volume pore size distribution. Further research including simulations with a greater number of particles and image resolution are required to obtain finer results on the hierarchical structure of pore space.

Qualitative operational value at risk for an electric utility based on the guidelines of the basel committee

This paper presents a model for determining value at operational risk ?OpVaR? in electric utilities, with the aim to confirm the versatility of the Bank for International Settlements (BIS) proposals. The model intends to open a new methodological approach in risk management, paying special attention to underlying operational sources of risk.

Incorporating equity into transport planning: utility, priority and sufficiency approaches

The analysis addresses the issue of transport equity and explores three different approaches to equity in transport: utilitarianism, sufficientarianism and prioritarianism. Each approach calls for a different treatment of the benefits reaped by different population groups in the assessment of transport investments or policies. In utilitarianism, which underlies much of the current practice of transport project appraisal, all benefits receive the same weight, irrespective of the recipient of the benefits. In both sufficientarianism and prioritarianism, benefits are weighed in distinct ways, depending on the characteristics of the recipients. The three approaches are illustrated using a fictive case study, in which three different transport investment are assessed and compared to each other. Finally, the assessment of transport investments will be explored using the cost-effectiveness analysis (CEA). The CEA assesses the distributional effects of transport investments for utilitarism, sufficientarism and prioritarism approaches and addresses distinct needs associated with different population groups in respect to their transport

Pseudo-random single photon counting for space-borne atmospheric sensing applications

The ability to accurately observe the Earth's carbon cycles from space gives scientists an important tool to analyze climate change. Current space-borne Integrated-Path Differential Absorption (IPDA) Iidar concepts have the potential to meet this need. They are mainly based on the pulsed time-offlight principle, in which two high energy pulses of different wavelengths interrogate the atmosphere for its transmission properties and are backscattered by the ground. In this paper, feasibility study results of a Pseudo-Random Single Photon Counting (PRSPC) IPDA lidar are reported. The proposed approach replaces the high energy pulsed source (e.g. a solidstate laser), with a semiconductor laser in CW operation with a similar average power of a few Watts, benefiting from better efficiency and reliability. The auto-correlation property of Pseudo-Random Binary Sequence (PRBS) and temporal shifting of the codes can be utilized to transmit both wavelengths simultaneously, avoiding the beam misalignment problem experienced by pulsed techniques. The envelope signal to noise ratio has been analyzed, and various system parameters have been selected. By restricting the telescopes field-of-view, the dominant noise source of ambient light can be suppressed, and in addition with a low noise single photon counting detector, a retrieval precision of 1.5 ppm over 50 km along-track averaging could be attained. We also describe preliminary experimental results involving a negative feedback Indium Gallium Arsenide (InGaAs) single photon avalanche photodiode and a low power Distributed Feedback laser diode modulated with PRBS driven acoustic optical modulator. The results demonstrate that higher detector saturation count rates will be needed for use in future spacebourne missions but measurement linearity and precision should meet the stringent requirements set out by future Earthobserving missions.

Random-modulation cw lidar system for space-borne carbon dioxide remote sensing based on a high-brightness semiconductor laser

In this paper, we report on the progresses of the BRITESPACE Consortium in order to achieve space-borne LIDAR measurements of atmospheric carbon dioxide concentration based on an all semiconductor laser source at 1.57 ?m. The complete design of the proposed RM-CW IPDA LIDAR has been presented and described in detail. Complete descriptions of the laser module and the FSU have been presented. Two bended MOPAs, emitting at the sounding frequency of the on- and off- IPDA channels, have been proposed as the transmitter optical sources with the required high brightness. Experimental results on the bended MOPAs have been presented showing a high spectral purity and promising expectations on the high output power requirements. Finally, the RM-CW approach has been modelled and an estimation of the expected SNR for the entire system is presented. Preliminary results indicate that a CO2 retrieval precision of 1.5 ppm could be achieved with an average output power of 2 W for each channel.