Conflicting Objectives in Decisions

This book deals with quantitative approaches in making decisions when conflicting objectives are present. This problem is central to many applications of decision analysis, policy analysis, operational research, etc. in a wide range of fields, for example, business, economics, engineering, psychology, and planning. The book surveys different approaches to the same problem area and each approach is discussed in considerable detail so that the coverage of the book is both broad and deep. The problem of conflicting objectives is of paramount importance, both in planned and market economies, and this book represents a cross-cultural mixture of approaches from many countries to the same class of problem

Stochastic analysis of nonlinear dynamics and feedback control for gene regulatory networks with applications to synthetic biology

The focus of the thesis is the investigation of the generalized repressilator model (repressing genes ordered in a ring structure). Using nonlinear bifurcation analysis stable and quasi-stable periodic orbits in this genetic network are characterized and a design for a switchable and controllable genetic oscillator is proposed. The oscillator operates around a quasi-stable periodic orbit using the classical engineering idea of read-out based control. Previous genetic oscillators have been designed around stable periodic orbits, however we explore the possibility of quasi-stable periodic orbit expecting better controllability. The ring topology of the generalized repressilator model has spatio-temporal symmetries that can be understood as propagating perturbations in discrete lattices. Network topology is a universal cross-discipline transferable concept and based on it analytical conditions for the emergence of stable and quasi-stable periodic orbits are derived. Also the length and distribution of quasi-stable oscillations are obtained. The findings suggest that long-lived transient dynamics due to feedback loops can dominate gene network dynamics. Taking the stochastic nature of gene expression into account a master equation for the generalized repressilator is derived. The stochasticity is shown to influence the onset of bifurcations and quality of oscillations. Internal noise is shown to have an overall stabilizing effect on the oscillating transients emerging from the quasi-stable periodic orbits. The insights from the read-out based control scheme for the genetic oscillator lead us to the idea to implement an algorithmic controller, which would direct any genetic circuit to a desired state. The algorithm operates model-free, i.e. in principle it is applicable to any genetic network and the input information is a data matrix of measured time series from the network dynamics. The application areas for readout-based control in genetic networks range from classical tissue engineering to stem cells specification, whenever a quantitatively and temporarily targeted intervention is required

Multiscale mathematical models for simulation and scale-up of green processes in the perspective of industrial sustainability

The present work presents research studies aimed at developing tools useful to design engineering solutions moving in the direction of industrial sustainability. The investigations hereinafter discussed regard an extraction process of active compounds \u2013 polyphenols \u2013 from agro-food industry wastes (olive and grape pomaces) and a biorefinery exploiting waste frying oil, solid organic wastes and algal biomass to produce biofuels. In particular, for the former topic, a procedure aimed at the evaluation of the technological feasibility at pilot scale of said process is discussed. The proposed approach takes into consideration the extended kinetic route coupled with mathematical simulation. Detailed physically-based dynamic mathematical models, taking into account mass and energy balance equations, are adopted to describe both the lab-scale and the pilot-scale reactors. Chemical physical parameters appearing in the models are estimated from the experimental data at lab-scale or are partially taken from literature. Different heating systems are designed for the pilot scale reactor and their performance is tested by simulation. Characteristic times are evaluated also during start-ups and different control loops are analyzed in order to set-up the best process and operating variables. Average yields in polyphenols are finally evaluated for both the batch and the continuous operated pilot reactor, by considering feed variability and fluctuations of process parameters. For what concerns the biorefinery, special attention was devoted to the modeling of the airlift reactor, its most delicate and complex component. In fact, to optimize this interesting microalgae cultivation system, a precise description of the moving interfaces formed by the liquid and gas phase is critical. In this study, coupled front capturing methods (standard and conservative level set methods) and finite difference method are used to simulate gas bubbles dynamics in a pilot-scale external loop air-lift photobioreactor in which microalgae are used to capture CO2 from flue gas and to treat wastewater. Numerical simulations are carried out on rectangular domains representing different sections of the vertical axis of the riser. The data employed was either acquired from previous experimental campaigns carried out in the airlift reactor or found in the literature. The rise, shape dynamics and coalescence process of the bubbles of flue gas are studied. Moreover, for each analyzed applications, a procedure based on Buckingham \u3c0-theorem to perform a rigorous scale-up is proposed. In this way, scale-invariant dimensionless groups describing and summarizing the considered processes could be identified. For the research focused on the scale-up of photobioreactors used to cultivate Chlorella Vulgaris, an experimental campaign at three levels was designed and carried out to evaluate the characteristic dimensionless numbers individuated by the theoretical formulation. Since scale-up regards both geometrical dimensions and type of reactor, passing from lab-scale stirred tanks to pilot scale tubular and airlift, particular attention was devoted to define characteristic lengths inside the dimensionless numbers

Essays on Cross-Sectionally Dependent Panel Data with an Application to Fiscal Policy in the European Monetary Union

In the past two decades, macroeconomists have used panel data to study the merits of fiscal policy for economic stabilisation. The datasets considered in these studies typically consist of a small number of time series corresponding to countries. This configuration does not match with the archetypical survey-style panel ataset for which a large literature concerning estimation and hypothesis testing exists. This PhD develops an estimation methodology that is catered towards macroeconomists: in four self-contained chapters, we develop a methodology for the estimation of dynamic models in the small N, large T framework in the presence of cross-sectional dependence in the error term. In the first chapter we examine the effect of factors on the point estimates of several commonly-used estimators in the empirical literature and we find that these estimators are inconsistent. We also propose an estimator that is consistent for the parameters for of the model studied in that chapter. In the second chapter we develop consistent quasi-difference GMM estimators and inferential procedures for the small N, large T dynamic panel data model with factor error structures. We also prove consistency and mixed-normality of the estimator when the number of factors is over-estimated. In the third chapter we consider the large N, large T framework and show the first eigenvalues of the covariance matrix of an approximate factor model are dominated by the factors whereas the remainder is controlled by the residual noise. We show that this result is the basis for any consistent inferential procedure about R and continues to hold when R grows large, when the factors are weak and, importantly, in the large N, large T interactive fixed effects model. In the fourth chapter we study fiscal policy using the methods developed in the thesis. We estimate vector autoregressions from European countries and restrict the impulse-response functions to adhere to the Stability and Growth Pact. We find that this one-size-fits-all approach is not appropriate for stabilization of the European economy

Advances in approximate Bayesian computation and trans-dimensional sampling methodology

Bayesian statistical models continue to grow in complexity, driven in part by a few key factors: the massive computational resources now available to statisticians; the substantial gains made in sampling methodology and algorithms such as Markov chain Monte Carlo (MCMC), trans-dimensional MCMC (TDMCMC), sequential Monte Carlo (SMC), adaptive algorithms and stochastic approximation methods and approximate Bayesian computation (ABC); and development of more realistic models for real world phenomena as demonstrated in this thesis for financial models and telecommunications engineering. Sophisticated statistical models are increasingly proposed for practical solutions to real world problems in order to better capture salient features of increasingly more complex data. With sophistication comes a parallel requirement for more advanced and automated statistical computational methodologies. The key focus of this thesis revolves around innovation related to the following three significant Bayesian research questions. 1. How can one develop practically useful Bayesian models and corresponding computationally efficient sampling methodology, when the likelihood model is intractable? 2. How can one develop methodology in order to automate Markov chain Monte Carlo sampling approaches to efficiently explore the support of a posterior distribution, defined across multiple Bayesian statistical models? 3. How can these sophisticated Bayesian modelling frameworks and sampling methodologies be utilized to solve practically relevant and important problems in the research fields of financial risk modeling and telecommunications engineering ? This thesis is split into three bodies of work represented in three parts. Each part contains journal papers with novel statistical model and sampling methodological development. The coherent link between each part involves the novel sampling methodologies developed in Part I and utilized in Part II and Part III. Papers contained in each part make progress at addressing the core research questions posed. Part I of this thesis presents generally applicable key statistical sampling methodologies that will be utilized and extended in the subsequent two parts. In particular it presents novel developments in statistical methodology pertaining to likelihood-free or ABC and TDMCMC methodology. The TDMCMC methodology focuses on several aspects of automation in the between model proposal construction, including approximation of the optimal between model proposal kernel via a conditional path sampling density estimator. Then this methodology is explored for several novel Bayesian model selection applications including cointegrated vector autoregressions (CVAR) models and mixture models in which there is an unknown number of mixture components. The second area relates to development of ABC methodology with particular focus on SMC Samplers methodology in an ABC context via Partial Rejection Control (PRC). In addition to novel algorithmic development, key theoretical properties are also studied for the classes of algorithms developed. Then this methodology is developed for a highly challenging practically significant application relating to multivariate Bayesian $\alpha$-stable models. Then Part II focuses on novel statistical model development in the areas of financial risk and non-life insurance claims reserving. In each of the papers in this part the focus is on two aspects: foremost the development of novel statistical models to improve the modeling of risk and insurance; and then the associated problem of how to fit and sample from such statistical models efficiently. In particular novel statistical models are developed for Operational Risk (OpRisk) under a Loss Distributional Approach (LDA) and for claims reserving in Actuarial non-life insurance modelling. In each case the models developed include an additional level of complexity which adds flexibility to the model in order to better capture salient features observed in real data. The consequence of the additional complexity comes at the cost that standard fitting and sampling methodologies are generally not applicable, as a result one is required to develop and apply the methodology from Part I. Part III focuses on novel statistical model development in the area of statistical signal processing for wireless communications engineering. Statistical models will be developed or extended for two general classes of wireless communications problem: the first relates to detection of transmitted symbols and joint channel estimation in Multiple Input Multiple Output (MIMO) systems coupled with Orthogonal Frequency Division Multiplexing (OFDM); the second relates to co-operative wireless communications relay systems in which the key focus is on detection of transmitted symbols. Both these areas will require advanced sampling methodology developed in Part I to find solutions to these real world engineering problems