This thesis presents two classes of models of boundedly rational decision makers - one with application to finance and the other to pricing. It consists of three parts. The first part of the thesis investigates the impact of investors' boundedly rational forecasting on asset price bubbles. We present a class of models, called extrapolation-correction models, of boundedly rational investor behavior. That is, the investors in our model, quite reasonably, use data available to them, i.e. past price data, to form forecasts about future prices. We relate the model parameters to various behavioral aspects like investor memory, caution/confidence, and panic. We present the resulting dynamical system model of asset price bubbles and relate the behavior of the dynamical system to the parameters capturing investor forecasting behavior. We show that, depending on the behavioral parameters, the associated dynamical system can converge to the fundamental value, go into predictable price cycles, or go into unpredictable price cycles. In particular, we find that the greater the weight investors' forecasts put on the most recent observations, the greater the tendency for the asset prices to exhibit cycles, forming positive and negative bubbles. We also find that when forecasts are strongly affected by recent prices, the price process becomes chaotic and it becomes increasingly difficult to forecast future prices accurately. The second part of the thesis addresses the question: How do investors make their price forecasts? We present the design of an experiment where investors participate in a virtual asset market run over a computer network. During the course of the experiment, the participants report their price forecasts and enter buy and sell orders. The computer software determines the market clearing prices. Despite full disclosure of the assets' dividends and the fundamental value, the price trajectories in all three experimental sessions exhibited cycles. We calibrated various models, including rational expectations based models and the extrapolation-correction family of models presented in the first part of the thesis. The results indicate that rational expectations hypothesis does not provide an accurate model of forecast formation. Moreover, a simple one-parameter exponential smoothing model is much better at modeling forecast formation, with the extrapolation-correction models making the fit slightly better. The third part of the thesis explores a different aspect of customer rationality - that of customer impatience - and its effect on pricing of product versions. We consider a setting in which impatient customers are faced with frequent product introductions, for example, products like Apple iPhones. This raises the following questions regarding customers: Given the pricing strategy of the firm, what are the optimal buying behaviors of the customers? How does customer buying behavior change in relation to impatience? We consider two settings. In the first setting, the firm offers a trade-in price for existing customers and a higher full price for new customers. In the second setting, the firm offers the same prices to new and existing customers, however there is an introductory full price and a discounted price later in the product cycle. We model the customer's problem in these two settings and characterize their optimal actions as a function of the price parameters. We also analyze the bilevel program for the firm's pricing decisions. We see that in both settings considered there are certain well-defined regions in the price space wherein the firm's optimal decision lies. We also provide some numerical computations to study the behavior of the optimal prices as the cost per unit increases.

Modeling and prediction of gene-expression patterns has an important place in computational biology and bioinformatics. The measure of gene expression is determined from the genomic analysis at the mRNA level by means of microarray technologies. Thus, mRNA analysis informs us not only about genetic viewpoints of an organism but also about the dynamic changes in environment of that organism. Different mathematical methods have been developed for analyzing experimental data. In this study, we discuss the modeling approaches and the reasons why we concentrate on models derived from differential equations and improve the pioneering works in this field by including affine terms on the right-hand side of the nonlinear differential equations and by using Runge- Kutta instead of Euler discretization, especially, with Heun&rsquo / s method. Herewith, for stability analysis we apply modified Brayton and Tong algorithm to time-discrete dynamics in an extended space.

abstract: In this work, we focused on the stability and reducibility of quasi-periodic systems. We examined the quasi-periodic linear Mathieu equation of the form x ̈+(ä+ϵ[cost+cosùt])x=0 The stability of solutions of Mathieu's equation as a function of parameter values (ä,ϵ) had been analyzed in this work. We used the Floquet type theory to generate stability diagrams which were used to determine the bounded regions of stability in the ä-ù plane for fixed ϵ. In the case of reducibility, we first applied the Lyapunov- Floquet (LF) transformation and modal transformation, which converted the linear part of the system into the Jordan form. Very importantly, quasi-periodic near-identity transformation was applied to reduce the system equations to a constant coefficient system by solving homological equations via harmonic balance. In this process we obtained the reducibility/resonance conditions that needed to be satisfied to convert a quasi-periodic system to a constant one. / Dissertation/Thesis / M.S.Tech Engineering 2012

O lado escuro do universo é misterioso e sua natureza é ainda desconhecida. De fato, isto talvez constitua o maior desafio da cosmologia moderna. As duas com- ponentes do setor escuro (mat´ eria escura e energia escura) correspondem hoje a cerca de noventa e cinco por cento do universo. O candidato mais simples para a energia energia é uma constante cosmológica. Contudo, esta tentativa apresenta uma enorme discrepância de 120 ordens de magnitude entre a predição teórica e os dados observados. Tal disparidade motiva os físicos a investigar modelos mais sofisticados. Isto pode ser feito tanto buscando um entendimento mais profundo de onde a constante cosmológica vem, se deseja-se derivá-la de primeiros princípios, quanto considerando outras possibilidades para a expansão acelerada, tais como modificações da relatividade geral, campos de matéria adi- cionais e assim por diante. Ainda considerando uma energia escura dinâmica, pode existir a possibilidade de interação entre energia e matéria escuras, uma vez que suas densidades são comparáveis e, dependendo do acoplamento usado, a interação pode também aliviar a questão de porquê as densidades de matéria e energia escura são da mesma ordem hoje. Modelos fenomenológicos tem sido amplamente estudados na literatura. Por outro lado, modelos de teoria de cam- pos que visam uma descrição consistente da interação energia escura/matéria escura ainda são poucos. Nesta tese, nós exploramos como candidato à energia escura um campo escalar ou vetorial em várias abordagens diferentes, levando em conta uma possível interação entre as duas componentes do setor escuro. A tese é dividida em três partes, que podem ser lidas independentemente. Na primeira parte, nós analisamos o comportamento asintótico de alguns modelos cosmológicos usando campos escalares ou vetorial como candidatos para a energia escura, à luz da teoria de sistemas dinâmicos. Na segunda parte, nós usamos um campo escalar em supergravidade para construir um modelo de energia escura dinâmico e também para incorporar um modelo de energia escura holográfica em supergravidade mínima. Finalmente, na terceira parte, nós propomos um modelo de energia escura metaestável, no qual a energia escura é um campo escalar com um potencial dado pela soma de auto-interações pares até ordem seis. Nós inserimos a energia escura metaestável em um modelo SU(2)R escuro, onde o dubleto de energia escura e o dubleto de matéria escura interagem nat- uramente. Tal interação abre uma nova janela para investigar o setor escuro do ponto-de-vista de física de partículas. Esta tese é baseada nos seguintes artigos, disponíveis também no arXiv: 1611.00428, 1605.03550, 1509.04980, 1508.07248, 1507.00902 e 1505.03243. O autor também colaborou nos trabalhos: 1607.03506 e 1605.05264. / The dark side of the universe is mysterious and its nature is still unknown. In fact, this poses perhaps as the biggest challenge in the modern cosmology. The two components of the dark sector (dark matter and dark energy) correspond today to around ninety five percent of the universe. The simplest dark energy candidate is a cosmological constant. However, this attempt presents a huge discrepancy of 120 orders of magnitude between the theoretical prediction and the observed data. Such a huge disparity motivates physicists to look into a more sophisticated models. This can be done either looking for a deeper understanding of where the cosmological constant comes from, if one wants to derive it from first principles, or considering other possibilities for accelerated expansion, such as modifications of general relativity, additional matter fields and so on. Still regarding a dynamical dark energy, there may exist a possibility of interaction between dark energy and dark matter, since their densities are comparable and, depending on the coupling used, the interaction can also alleviate the issue of why dark energy and matter densities are of the same order today. Phenomenological models have been widely explored in the literature. On the other hand, field theory models that aim a consistent description of the dark energy/dark matter interaction are still few. In this thesis, we explore either a scalar or a vector field as a dark energy candidate in several different approaches, taking into account a possible interaction between the two components of the dark sector. The thesis is divided in three parts, which can be read independently of each other. In the first part, we analyze the asymptotic behavior of some cosmological models using either scalar or vector fields as dark energy candidates, in the light of the dynamical system theory. In the second part, we use a scalar field in the supergravity framework to build a model of dynamical dark energy and also to embed a holographic dark energy model into minimal supergravity. Finally, in the third part, we propose a model of metastable dark energy, in which the dark energy is a scalar field with a potential given by the sum of even self-interactions up to order six. We insert the metastable dark energy into a dark SU(2)R model, where the dark energy doublet and the dark matter doublet naturally interact with each other. Such an interaction opens a new window to investigate the dark sector from the point-of-view of particle physics. This thesis is based on the following papers, available also in the arXiv: 1611.00428, 1605.03550, 1509.04980, 1508.07248, 1507.00902 and 1505.03243. The author also collaborated in the works 1607.03506 and 1605.05264.

O presente trabalho apresenta a teoria de sistemas dinâmicos dissipativos impulsivos. Apresentamos resultados suficientes e necessários para obtermos dissipatividade para sistemas impulsivos autônomos e não autônomos utilizando funções de Lyapunov. No que segue, desenvolvemos a teoria de estabilidade para a seção nula de um sistema dinâmico não autônomo com impulsos. Utilizando os resultados da teoria abstrata para sistemas não autônomos com impulsos, apresentamos o estudo da estabilidade de um modelo presa-predador com controle e impulsos. / The present work presents the theory of impulsive dissipative dynamical systems. We present necessary and sufficient conditions to obtain dissipativity for autonomous and non-autonomous impulsive dynamical systems via Lyapunov functions. In the sequel, we develop the theory of stability for the null section of non-autonomous dynamical systems with impulses. Using the results from the abstract theory we present the study of stability for a controlled prey-predator model under impulse conditions.

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The diploma thesis deals with nonlinear dynamical systems with emphasis on typical phenomena like bifurcation or chaotic behavior. The basic theoretical knowledge is applied to analysis of selected (chaotic) models, namely, Lorenz, Rössler and Chen system. The practical part of the work is then focused on a numerical simulation to confirm the correctness of the theoretical results. In particular, an algorithm for calculating the largest Lyapunov exponent is created (under the MATLAB environment). It represents the main tool for indicating chaos in a system.

We introduce numerical methods for the analysis of random dynamical systems. The subdivision and the continuation algorithm are powerful tools which will be demonstrated for a system from ship dynamics. With our software package we are able to show that the well known safe basin is a moving fractal set. We will also give a numerical approximation of the attracting invariant set (which contains a local attractor) and its evolution.

In this work, we study return and hitting times in measure-preserving dy- namical systems. We consider a special type of skew-products of two Bernoulli schemes, called a random walk in random scenery. For these systems, the limit distribution of normalized hitting times for cylinders of increasing length is proved to be exponential under the assumption of finite variance of the first order dis- tribution of the Bernoulli scheme representing the walk, and provided the drift is non-zero or the scenery alphabet is finite. Mixing properties of the skew-products are discussed in order to relate our work with some known results on rescaled hitting times for strongly-mixing systems. 1

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