Em ecologia, o comportamento dos animais é freqüentemente estudado para entender melhor suas preferências por diferentes tipos de alimento e habitat. O presente trabalho esta relacionado a este tópico, dividindo-se em três capítulos. O primeiro capitulo refere-se à estimação da função da probabilidade da seleção de recurso (RSPF) comparado com um modelo de escolha discreta (DCM) com uma escolha, usando as estatísticas qui-quadrado para obter as estimativas. As melhores estimativas foram obtidas pelo método DCM com uma escolha. No entanto, os animais não fazem a sua seleção baseados apenas em uma escolha. Com RSPF, as estimativas de máxima verossimilhança, usadas pela regressão logística ainda não atingiram os objetivos, já que os animais têm mais de uma escolha. R e o software Minitab e a linguagem de programação Fortran foram usados para obter os resultados deste capítulo. No segundo capítulo discutimos mais a verossimilhança do primeiro capítulo. Uma nova verossimilhança para a RSPF é apresentada, a qual considera as unidades usadas e não usadas, e métodos de bootstrapping paramétrico e não paramétrico são usados para estudar o viés e a variância dos estimadores dos parâmetros, usando o programa FORTRAN para obter os resultados. No terceiro capítulo, a nova verossimilhança apresentada no capítulo 2 é usada com um modelo de escolha discreta, para resolver parte do problema apresentado no primeiro capítulo. A estrutura de encaixe é proposta para modelar a seleção de habitat de 28 corujas manchadas (Strix occidentalis), assim como a uma generalização do modelo logit encaixado, usando a maximização da utilidade aleatória e a RSPF aleatória. Métodos de otimização numérica, e o sistema computacional SAS, são usados para estimar os parâmetros de estrutura de encaixe. / In ecology, the behavior of animals is often studied to better understand their preferences for different types of habitat and food. The present work is concerned with this topic. It is divided into three chapters. The first concerns the estimation of a resource selection probability function (RSPF) compared with a discrete choice model (DCM) using chi-squared to obtain estimates. The best estimates were obtained by the DCM method. Nevertheless, animals were not selected based on choice alone. With RSPF, the maximum likelihood estimates used with the logistic regression still did not reach the objectives, since the animals have more than one choice. R and Minitab software and the FORTRAN programming language were used for the computations in this chapter. The second chapter discusses further the likelihood presented in the first chapter. A new likelihood for a RSPF is presented, which takes into account the units used and not used, and parametric and non-parametric bootstrapping are employed to study the bias and variance of parameter estimators, using a FORTRAN program for the calculations. In the third chapter, the new likelihood presented in chapter 2, with a discrete choice model is used to resolve a part of the problem presented in the first chapter. A nested structure is proposed for modelling selection by 28 spotted owls (Strix occidentalis) as well as a generalized nested logit model using random utility maximization and a random RSPF. Numerical optimization methods and the SAS system were employed to estimate the nested structural parameters.

Resource allocation in wireless networks is one of the most studied class of problems. Generally, these problems are formulated as utility maximization problems under relevant constraints. The challenges posed by these problems vary widely depending on the nature of the utility function under consideration. Recently, the widespread prevalence of wireless devices prompted researchers and engineers to delve into the security issues of wireless communication. As compared to the wired medium, ensuring security for the wireless medium is more challenging mainly due to the broadcast nature of the transmission. But the ongoing research on physical layer security promises robust and reliable security schemes for wireless communication. Contrary to conventional cryptographic schemes, physical layer security techniques are impregnable as the security is ensured by the inherent randomness present in the wireless medium. In this thesis, we consider several wireless scenarios and propose secrecy enhancing resource allocation schemes for them in the first few chapters. We initially address the problem of secure transmission by following the conventional approach in the secrecy literature|secrecy rate maximization. Needless to say, in these chapters, secrecy rate is the utility function and the constraints are posed by the available power budget. Then we consider a pragmatic approach where we target the signal-to-noise ratio (SNR) of participating nodes and ensure information secrecy by appropriately constraining the SNRs of those nodes. In those SNR based formulations, SNR at the destination is the utility function and we are interested in maximizing it. In the last two chapters, we study two scenarios in a non-secrecy setting. In one of them, end-to-end data rate is the utility, whereas, in the other one, two utility functions|based on revenue generated|are defined for two rational agents in a game-theoretic setting. In the second chapter, we study parallel independent Gaussian channels with imperfect channel state information (CSI) for the eavesdropper. Firstly, we evaluate the probability of zero secrecy rate in this system for (i) given instantaneous channel conditions and (ii) a Rayleigh fading scenario. Secondly, when non-zero secrecy is achievable in the low SNR regime, we aim to solve a robust power allocation problem which minimizes the outage probability at a target secrecy rate. In the third, fourth and fifth chapters, we consider scenarios where the source node transmits a message to the destination using M parallel amplify-and-forward (AF) relays in the presence of a single or multiple eavesdroppers. The third chapter addresses the problem of the maximum achievable secrecy rate for two specific network models: (a) degraded eavesdropper channel with complex channel gain and (b) scaled eavesdropper channel with real-valued channel gains. In the fourth chapter, we consider the SNR based approach and address two problems: (i) SNR maximization at the destination and (ii) Total relay power minimization. In the fifth chapter, we assume that the relay nodes are untrusted and to counter them, we deliberately introduce artificial noise in the source message. For this model, we propose and solve SNR maximization problems for the following two scenarios: (i) Total power constraint on all the relay nodes and (ii) Individual power constraints on each of the relay nodes. In the sixth chapter, we address the problem of passive eavesdroppers in multi-hop wire-less networks using the technique of friendly jamming. Assuming decode-and-forward (DF) relaying, we consider a scheduling and power allocation (PA) problem for a multiple-source multiple-sink scenario so that eavesdroppers are jammed, and source-destination throughput targets are met. When channel state information (CSI) of all the node are available, we intend to minimize the total power consumption of all the transmitting nodes. In the absence of eavesdroppers CSI, we minimize vulnerability region of the network. In chapter seven, the problem of cooperative beamforming for maximizing the achievable data rate of two-hop amplify-and-forward (AF) network (in the absence of eavesdropper(s)) is considered. Along with an individual power constraint on each of the relay nodes, we consider a weighted sum power constraint. To solve this problem, we propose a novel algorithm based on the Quadratic Eigenvalue Problem (QEP) and discuss its convergence. In chapter eight, we study a Stackelberg game between a base station and a multi-antenna power beacon for wireless energy harvesting in a multiple sensor node scenario. Assuming imperfect CSI between the sensor nodes and the power beacon, we propose a utility function that is based on throughput non-outage probability at the base station. We find the optimal strategies for the base station and the power beacon that maximize their respective utility functions.

Depuis la publication de l'ouvrage de Choquet (1955), la théorie d'espérance non linéaire a attiré avec grand intérêt des chercheurs pour ses applications potentielles dans les problèmes d'incertitude, les mesures de risque et le super-hedging en finance. Shige Peng a construit une sorte d'espérance entièrement non linéaire dynamiquement cohérente par l'approche des EDP. Un cas important d'espérance non linéaire cohérente en temps est la G-espérance, dans laquelle le processus canonique correspondant (B_{t})_{t≥0} est appelé G-mouvement brownien et joue un rôle analogue au processus de Wiener classique. L'objectif de cette thèse est d'étudier, dans le cadre de la G-espérance, certaines équations différentielles stochastiques rétrogrades (G-EDSR) à croissance quadratique avec applications aux problèmes de maximisation d'utilité robuste avec incertitude sur les modèles, certaines équations différentielles stochastiques (G-EDS) réfléchies et équations différentielles stochastiques rétrogrades réfléchies avec générateurs lipschitziens. On considère d'abord des G-EDSRs à croissance quadratique. Dans le Chapitre 2 nous fournissons un resultat d'existence et unicité pour des G-EDSRs à croissance quadratique. D'une part, nous établissons des estimations a priori en appliquant le théorème de type Girsanov, d'où l'on en déduit l'unicité. D'autre part, pour prouver l'existence de solutions, nous avons d'abord construit des solutions pour des G-EDSRs discretes en résolvant des EDPs non-linéaires correspondantes, puis des solutions pour les G-EDSRs quadratiques générales dans les espaces de Banach. Dans le Chapitre 3 nous appliquons les G-EDSRs quadratiques aux problèmes de maximisation d'utilité robuste. Nous donnons une caratérisation de la fonction valeur et une stratégie optimale pour les fonctions d'utilité exponentielle, puissance et logarithmique. Dans le Chapitre 4, nous traitons des G-EDSs réfléchies multidimensionnelles. Nous examinons d'abord la méthode de pénalisation pour résoudre des problèmes de Skorokhod déterministes dans des domaines non convexes et établissons des estimations pour des fonctions α-Hölder continues. A l'aide de ces résultats obtenus pour des problèmes déterministes, nous définissons le G-mouvement Brownien réfléchi et prouvons son existence et son unicité dans un espace de Banach. Ensuite, nous prouvons l'existence et l'unicité de solution pour les G-EDSRs multidimensionnelles réfléchies via un argument de point fixe. Dans le Chapitre 5, nous étudions l'existence et l'unicité pour les équations différentielles stochastiques rétrogrades réfléchies dirigées par un G-mouvement brownien lorsque la barrière S est un processus de G-Itô. / Since the publication of Choquet's (1955) book, the theory of nonlinear expectation has attracted great interest from researchers for its potential applications in uncertainty problems, risk measures and super-hedging in finance. Shige Peng has constructed a kind of fully nonlinear expectation dynamically coherent by the PDE approach. An important case of time-consistent nonlinear expectation is G-expectation, in which the corresponding canonical process (B_{t})_{t≥0} is called G-Brownian motion and plays a similar role to the classical Wiener process. The objective of this thesis is to study, in the framework of the G-expectation, some backward stochastic differential equations (G-BSDE) under a quadratic growth condition on their coefficients with applications to robust utility maximization problems with uncertainty on models, Reflected stochastic differential equations (reflected G-SDE) and reflected backward stochastic differential equations with Lipschitz coefficients (reflected G-BSDE). We first consider G-BSDE with quadratic growth. In Chapter 2 we provide a result of existence and uniqueness for quadratic G-BSDEs. On the one hand, we establish a priori estimates by applying the Girsanov-type theorem, from which we deduce the uniqueness. On the other hand, to prove the existence of solutions, we first constructed solutions for discrete G-BSDEs by solving corresponding nonlinear PDEs, then solutions for the general quadratic G-BSDEs in the spaces of Banach. In Chapter 3 we apply quadratic G-BSDE to robust utility maximization problems. We give a characterization of the value function and an optimal strategy for exponential, power and logarithmic utility functions. In Chapter 4, we discuss multidimensional reflected G-SDE. We first examine the penalization method to solve deterministic Skorokhod problems in non-convex domains and establish estimates for continuous α-Hölder functions. Using these results for deterministic problems, we define the reflected G-Brownian motion and prove its existence and its uniqueness in a Banach space. Then we prove the existence and uniqueness of the solution for the multidimensional reflected G-SDE via a fixed point argument. In Chapter 5, we study the existence and uniqueness of the reflected backward stochastic differential equations driven by a G-Brownian motion when the obstacle S is a G-Itô process.

Cette thèse est composée de deux parties indépendantes : la première partie traite des équations différentielles stochastiques dans le cadre de la G-espérance, tandis que la deuxième partie présente les résultats obtenus pour les équations différentielles stochastiques du seconde ordre. Dans un premier temps, on considère les intégrales stochastiques par rapport à un processus croissant, et on donne une extension de la formule d'Itô dans le cadre de la G-espérance. Ensuite, on étudie une classe d'équations différentielles stochastiques réfléchies unidimensionnelles dirigées par un G-mouvement brownien. Dans la suite, en utilisant une méthode de localisation, on prouve l'existence et l'unicité de solutions pour les équations différentielles stochastiques dirigées par un G-mouvement brownien, dont les coefficients sont localement lipschitziens. Enfin, dans le même cadre, on discute des problèmes de réflexion multidimensionnelle et on fournit quelques résultats de convergence. Dans un deuxième temps, on étudie une classe d'équations différentielles stochastiques rétrogrades du seconde ordre à croissance quadratique. Le but de ce travail est de généraliser le résultat obtenu par Possamaï et Zhou en 2012. On montre aussi l'existence et l'unicité des solutions pour ces équations, mais sous des hypothèses plus faibles. De plus, ce résultat théorique est appliqué aux problèmes de maximisation robuste de l'utilité du portefeuille en finance. / This thesis consists of two relatively independent parts : the first part concerns stochastic differential equations in the framework of the G-expectation, while the second part deals with a class of second order backward stochastic differential equations. In the first part, we first consider stochastic integrals with respect to an increasing process and give an extension of Itô's formula in the G-framework. Then, we study a class of scalar valued reflected stochastic differential equations driven by G-Brownian motion. Subsequently, we prove the existence and the uniqueness of solutions for some locally Lipschitz stochastic differential equations driven by G-Brownian motion. At the end of this part, we consider multidimensional reflected problems in the G-framework, and some convergence results are obtained. In the second part, we study the wellposedness of a class of second order backward stochastic differential equations (2BSDEs) under a quadratic growth condition on their coefficients. The aim of this part is to generalize a wellposedness result for quadratic 2BSDEs by Possamaï and Zhou in 2012. In this thesis, we work under some usual assumptions and deduce the existence and uniqueness theorem as well. Moreover, this theoretical result for quadratic 2BSDEs is applied to solve some robust utility maximization problems in finance.

Cette thèse porte sur la modélisation du trafic dans les réseaux routiers et comment celle-ci est intégrée dans des modèles d'optimisation. Ces deux sujets ont évolué de manière plutôt disjointe: le trafic est prédit par des modèles mathématiques de plus en plus complexes, mais ce progrès n'a pas été incorporé dans les modèles de design de réseau dans lesquels les usagers de la route jouent un rôle crucial. Le but de cet ouvrage est d'intégrer des modèles d'utilités aléatoires calibrés avec de vraies données dans certains modèles biniveaux d'optimisation et ce, par une décomposition de Benders efficace. Cette décomposition particulière s'avère être généralisable par rapport à une grande classe de problèmes communs dans la litérature et permet d'en résoudre des exemples de grande taille. Le premier article présente une méthodologie générale pour utiliser des données GPS d'une flotte de véhicules afin d'estimer les paramètres d'un modèle de demande dit recursive logit. Les traces GPS sont d'abord associées aux liens d'un réseau à l'aide d'un algorithme tenant compte de plusieurs facteurs. Les chemins formés par ces suites de liens et leurs caractéristiques sont utilisés afin d'estimer les paramètres d'un modèle de choix. Ces paramètres représentent la perception qu'ont les usagers de chacune de ces caractéristiques par rapport au choix de leur chemin. Les données utilisées dans cet article proviennent des véhicules appartenant à plusieurs compagnies de transport opérant principalement dans la région de Montréal. Le deuxième article aborde l'intégration d'un modèle de choix de chemin avec utilités aléatoires dans une nouvelle formulation biniveau pour le problème de capture de flot de trafic. Le modèle proposé permet de représenter différents comportements des usagers par rapport à leur choix de chemin en définissant les utilités d'arcs appropriées. Ces utilités sont stochastiques ce qui contribue d'autant plus à capturer un comportement réaliste des usagers. Le modèle biniveau est rendu linéaire à travers l'ajout d'un terme lagrangien basé sur la dualité forte et ceci mène à une décomposition de Benders particulièrement efficace. Les expériences numériques sont principalement menés sur un réseau représentant la ville de Winnipeg ce qui démontre la possibilité de résoudre des problèmes de taille relativement grande. Le troisième article démontre que l'approche du second article peut s'appliquer à une forme particulière de modèles biniveaux qui comprennent plusieurs problèmes différents. La décomposition est d'abord présentée dans un cadre général, puis dans un contexte où le second niveau du modèle biniveau est un problème de plus courts chemins. Afin d'établir que ce contexte inclut plusieurs applications, deux applications distinctes sont adaptées à la forme requise: le transport de matières dangeureuses et la capture de flot de trafic déterministe. Une troisième application, la conception et l'établissement de prix de réseau simultanés, est aussi présentée de manière similaire à l'Annexe B de cette thèse. / The subject of this thesis is the modeling of traffic in road networks and its integration in optimization models. In the literature, these two topics have to a large extent evolved independently: traffic is predicted more accurately by increasingly complex mathematical models, but this progress has not been incorporated in network design models where road users play a crucial role. The goal of this work is to integrate random utility models calibrated with real data into bilevel optimization models through an efficient Benders decomposition. This particular decomposition generalizes to a wide class of problems commonly found in the literature and can be used to solved large-scale instances. The first article presents a general methodology to use GPS data gathered from a fleet of vehicles to estimate the parameters of a recursive logit demand model. The GPS traces are first matched to the arcs of a network through an algorithm taking into account various factors. The paths resulting from these sequences of arcs, along with their characteristics, are used to estimate parameters of a choice model. The parameters represent users' perception of each of these characteristics in regards to their path choice behaviour. The data used in this article comes from trucks used by a number of transportation companies operating mainly in the Montreal region. The second article addresses the integration of a random utility maximization model in a new bilevel formulation for the general flow capture problem. The proposed model allows for a representation of different user behaviors in regards to their path choice by defining appropriate arc utilities. These arc utilities are stochastic which further contributes in capturing real user behavior. This bilevel model is linearized through the inclusion of a Lagrangian term based on strong duality which paves the way for a particularly efficient Benders decomposition. The numerical experiments are mostly conducted on a network representing the city of Winnipeg which demonstrates the ability to solve problems of a relatively large size. The third article illustrates how the approach used in the second article can be generalized to a particular form of bilevel models which encompasses many different problems. The decomposition is first presented in a general setting and subsequently in a context where the lower level of the bilevel model is a shortest path problem. In order to demonstrate that this form is general, two distinct applications are adapted to fit the required form: hazmat transportation network design and general flow capture. A third application, joint network design and pricing, is also similarly explored in Appendix B of this thesis.

<p>It is predicted that there would be 41.6 billion IoT devices by 2025, which has kindled new interests on the timing coordination between sensors and controllers, i.e., how to use the waiting time to improve the performance. Sun et al. showed that a <i>controller</i> can strictly improve the data freshness, the so-called Age-of-Information (AoI), via careful scheduling designs. The optimal waiting policy for the <i>sensor</i> side was later characterized in the context of remote estimation. The first part of this work develops the jointly optimal sensor/controller waiting policy. It generalizes the above two important results in that not only do we consider joint sensor/controller designs, but we also assume random delay in both the forward and feedback directions. </p> <p> </p> <p>The second part of the work revisits and significantly strengthens the seminal results of Sun et al on the following fronts: (i) When designing the optimal offline schemes with full knowledge of the delay distributions, a new <i>fixed-point-based</i> method is proposed with <i>quadratic convergence rate</i>; (ii) When the distributional knowledge is unavailable, two new low-complexity online algorithms are proposed, which provably attain the optimal average AoI penalty; and (iii) the online schemes also admit a modular architecture, which allows the designer to <i>upgrade</i> certain components to handle additional practical challenges. Two such upgrades are proposed: (iii.1) the AoI penalty function incurred at the destination is unknown to the source node and must also be estimated on the fly, and (iii.2) the unknown delay distribution is Markovian instead of i.i.d. </p> <p> </p> <p>With the exponential growth of interconnected IoT devices and the increasing risk of excessive resource consumption in mind, the third part of this work derives an optimal joint cost-and-AoI minimization solution for multiple coexisting source-destination (S-D) pairs. The results admit a new <i>AoI-market-price</i>-based interpretation and are applicable to the setting of (i) general heterogeneous AoI penalty functions and Markov delay distributions for each S-D pair, and (ii) a general network cost function of aggregate throughput of all S-D pairs. </p> <p> </p> <p>In each part of this work, extensive simulation is used to demonstrate the superior performance of the proposed schemes. The discussion on analytical as well as numerical results sheds some light on designing practical network utility maximization protocols.</p>

The philosophical literature on akrasia and/or weakness of the will tends to focus on individual actions, removed from their wider socio-political context. This is problematic because actions, when removed from their wider context, can seem absurd or irrational when they may, in fact, be completely rational or, at least, coherent. Much of akrasia's apparent mystery or absurdity is eliminated when people's behaviours are considered within their cultural and political context. I apply theories from the social and behavioural sciences to a particular behaviour in order to show where the philosophical literature on akrasia and/or weakness of the will is insightful and where it is lacking. The problem used as the basis for my analysis is obesity caused by overeating. On the whole, I conclude that our intuitions about agency are unreliable, that we may have good reasons to overeat and/or neglect our health, and that willpower is, to some degree, a matter of luck. / Graduate / 0630 / 0573 / 0422 / felshereeno@aol.com