​​Linear-Quadratic Controls in Risk-Averse Decision Making cuts across control engineering (control feedback and decision optimization) and statistics (post-design performance analysis) with a common theme: reliability increase seen from the responsive angle of incorporating and engineering multi-level performance robustness beyond the long-run average performance into control feedback design and decision making and complex dynamic systems from the start. This monograph provides a complete description of statistical optimal control (also known as cost-cumulant control) theory. In control problems and topics, emphasis is primarily placed on major developments attained and explicit connections between mathematical statistics of performance appraisals and decision and control optimization. Chapter summaries shed light on the relevance of developed results, which makes this monograph suitable for graduate-level lectures in applied mathematics and electrical engineering with systems-theoretic concentration, elective study or a reference for interested readers, researchers, and graduate students who are interested in theoretical constructs and design principles for stochastic controlled systems.​

​​Linear-Quadratic Controls in Risk-Averse Decision Making cuts across control engineering (control feedback and decision optimization) and statistics (post-design performance analysis) with a common theme: reliability increase seen from the responsive angle of incorporating and engineering multi-level performance robustness beyond the long-run average performance into control feedback design and decision making and complex dynamic systems from the start. This monograph provides a complete description of statistical optimal control (also known as cost-cumulant control) theory. In control problems and topics, emphasis is primarily placed on major developments attained and explicit connections between mathematical statistics of performance appraisals and decision and control optimization. Chapter summaries shed light on the relevance of developed results, which makes this monograph suitable for graduate-level lectures in applied mathematics and electrical engineering with systems-theoretic concentration, elective study or a reference for interested readers, researchers, and graduate students who are interested in theoretical constructs and design principles for stochastic controlled systems.​

Providing readers with a detailed examination of resilient controls in risk-averse decision, this monograph is aimed toward researchers and graduate students in applied mathematics and electrical engineering with a systems-theoretic concentration. This work contains a timely and responsive evaluation of reforms on the use of asymmetry or skewness pertaining to the restrictive family of quadratic costs that have been appeared in various scholarly forums. Additionally, the book includes a discussion of the current and ongoing efforts in the usage of risk, dynamic game decision optimization and disturbance mitigation techniques with output feedback measurements tailored toward the worst-case scenarios. This work encompasses some of the current changes across uncertainty quantification, stochastic control communities, and the creative efforts that are being made to increase the understanding of resilient controls. Specific considerations are made in this book for the application of decision theory to resilient controls of the linear-quadratic class of stochastic dynamical systems. Each of these topics are examined explicitly in several chapters. This monograph also puts forward initiatives to reform both control decisions with risk consequences and correct-by-design paradigms for performance reliability associated with the class of stochastic linear dynamical systems with integral quadratic costs and subject to network delays, control and communication constraints.

We discuss how Whittle's (Whittle, 1990) approach to risk-sensitive optimal control problems can be applied in economics and finance. We show how his analysis of the class of Linear Exponential Quadratic Gaussian problems can be extended to accommodate time-discounting, while preserving its simple and general recursive solutions. We apply Whittle's methodology investigating two specific problems in financial economics and monetary policy.

The contributions of this volume stem from the “Fifth International Conference on the Dynamics of Information Systems” held in Gainesville, FL in February 2013, and discuss state-of the-art techniques in handling problems and solutions in the broad field of information systems. Dynamics of Information Systems: Computational and Mathematical Challenges presents diverse aspects of modern information systems with an emphasis on interconnected network systems and related topics, such as signal and message reconstruction, network connectivity, stochastic network analysis, cyber and computer security, community and cohesive structures in complex networks. Information systems are a vital part of modern societies. They are essential to our daily actions, including social networking, business and bank transactions, as well as sensor communications. The rapid increase in these capabilities has enabled us with more powerful systems, readily available to sense, control, disperse, and analyze information.

The theory of optimal decisions in a stochastic environment has seen many new developments in recent years. The implications of such theory for empirical and policy applications are several. This book attempts to analyze some of the impor tant applied aspects of this theory and its recent developments. The stochastic environment is considered here in specific form, e.g., (a) linear programs (LP) with parameters subject to a probabilistic mechanism, (b) decision models with risk aversion, (c) resource allocation in a team, and (d) national economic planning. The book attempts to provide new research insights into several areas, e.g., (a) mixed strategy solutions and econometric tests of hypotheses of LP models, (b) the dual problems of efficient estimation and optimal regulation, (c) input-output planning under imperfect competition, and (d) linear programs viewed as constrained statistical games. Methods of optimal decision rules developed here for quadratic and linear decision problems are applicable in three broad areas: (a) applied economic models in resource allocation, planning and team decision, (b) operations research models in management decisions involving portfolio analysis and stochastic programming, and (c) systems science models in stochastic control and adaptive behavior. Some results reported here have been published in professional journals be-. fore, and I would like to thank the following journals in particular: Inter national Journal of Systems Science, Journal of Optimization Theory and Applica tions and Journal of Mathematical Analysis and Applications.

Dynamics of Information Systems: Algorithmic Approaches presents recent developments and results found by participants of the Fourth International Conference on the Dynamics of Information Systems, which took place at the University of Florida, Gainesville FL, USA on February 20-22, 2012. The purpose of this conference was to bring together scientists and engineers from industry, government, and universities to exchange knowledge and results in a broad range of topics relevant to the theory and practice of the dynamics of information systems.​​​Dynamics of Information plays an increasingly critical role in our society. The influence of information on social, biological, genetic, and military systems must be better understood to achieve large advances in the capability and understanding of these systems. Applications are widespread and include: detection of terrorist networks, design of highly efficient businesses, computer networks, quantum entanglement, genome modeling, multi-robotic systems, and industrial and manufacturing safety. The book contains state-of-the-art work on theory and practice relevant to the dynamics of information systems. It covers algorithmic approaches to numerical computations with infinite and infinitesimal numbers; presents important problems arising in service-oriented systems, such as dynamic composition and analysis of modern service-oriented information systems and estimation of customer service times on a rail network from GPS data; addresses the complexity of the problems arising in stochastic and distributed systems; and discusses modulating communication for improving multi-agent learning convergence. Network issues—in particular minimum-risk maximum-clique problems, vulnerability of sensor networks, influence diffusion, community detection, and link prediction in social network analysis, as well as a comparative analysis of algorithms for transmission network expansion planning—are described in later chapters.