This book presents the fundamentals of evolutionary game theory and applies them to the analysis of epidemics, which is of paramount importance in the aftermath of the worldwide COVID-19 pandemic. The primary objective of this monograph is to deliver a powerful tool to model and analyze the spread of an infectious disease during a pandemic as well as the human decision dynamics. The book employs a variant of the "vaccination game," in which a mathematical epidemiological model dovetails with evolutionary game theory. From a social physics standpoint, this book introduces an extended concept of the vaccination game starting from the fundamental issues and touching on the newest practical applications. The book first outlines the fundamental basis of evolutionary game theory, in which a two-player and two-strategy game, the so-called 2 x 2 game, and a multi-player game are concisely introduced, and the important issue of how social dilemmas are quantified is highlighted. Subsequently, the book discusses various recent applications of the extended concept of the vaccination game so as to quantitatively evaluate provisions other than vaccination, including practical intermediate protective measures such as mask-wearing, efficiency of quarantine compared with that of isolation policies for suppressing epidemics, efficiency of preemptive versus late vaccination, and optimal subsidy policies for vaccination.

A remarkable facet of the human brain is its ability to manage multiple tasks with apparent simultaneity. Knowledge learned from one task can then be used to enhance problem-solving in other related tasks. In machine learning, the idea of leveraging relevant information across related tasks as inductive biases to enhance learning performance has attracted significant interest. In contrast, attempts to emulate the human brain's ability to generalize in optimization - particularly in population-based evolutionary algorithms - have received little attention to date. Recently, a novel evolutionary search paradigm, Evolutionary Multi-Task (EMT) optimization, has been proposed in the realm of evolutionary computation. In contrast to traditional evolutionary searches, which solve a single task in a single run, evolutionary multi-tasking algorithm conducts searches concurrently on multiple search spaces corresponding to different tasks or optimization problems, each possessing a unique function landscape. By exploiting the latent synergies among distinct problems, the superior search performance of EMT optimization in terms of solution quality and convergence speed has been demonstrated in a variety of continuous, discrete, and hybrid (mixture of continuous and discrete) tasks. This book discusses the foundations and methodologies of developing evolutionary multi-tasking algorithms for complex optimization, including in domains characterized by factors such as multiple objectives of interest, high-dimensional search spaces and NP-hardness.

This book offers a systematic and rigorous treatment of continuous-time Markov decision processes, covering both theory and possible applications to queueing systems, epidemiology, finance, and other fields. Unlike most books on the subject, much attention is paid to problems with functional constraints and the realizability of strategies. Three major methods of investigations are presented, based on dynamic programming, linear programming, and reduction to discrete-time problems. Although the main focus is on models with total (discounted or undiscounted) cost criteria, models with average cost criteria and with impulsive controls are also discussed in depth. The book is self-contained. A separate chapter is devoted to Markov pure jump processes and the appendices collect the requisite background on real analysis and applied probability. All the statements in the main text are proved in detail. Researchers and graduate students in applied probability, operational research, statistics and engineering will find this monograph interesting, useful and valuable.

This book focuses on distributed and economic Model Predictive Control (MPC) with applications in different fields. MPC is one of the most successful advanced control methodologies due to the simplicity of the basic idea (measure the current state, predict and optimize the future behavior of the plant to determine an input signal, and repeat this procedure ad infinitum) and its capability to deal with constrained nonlinear multi-input multi-output systems. While the basic idea is simple, the rigorous analysis of the MPC closed loop can be quite involved. Here, distributed means that either the computation is distributed to meet real-time requirements for (very) large-scale systems or that distributed agents act autonomously while being coupled via the constraints and/or the control objective. In the latter case, communication is necessary to maintain feasibility or to recover system-wide optimal performance. The term economic refers to general control tasks and, thus, goes beyond the typically predominant control objective of set-point stabilization. Here, recently developed concepts like (strict) dissipativity of optimal control problems or turnpike properties play a crucial role. The book collects research and survey articles on recent ideas and it provides perspectives on current trends in nonlinear model predictive control. Indeed, the book is the outcome of a series of six workshops funded by the German Research Foundation (DFG) involving early-stage career scientists from different countries and from leading European industry stakeholders.

This book presents recent research on bioinspired heuristics for optimization. Learning- based and black-box optimization exhibit some properties of intrinsic parallelization, and can be used for various optimizations problems. Featuring the most relevant work presented at the 6th International Conference on Metaheuristics and Nature Inspired Computing, held at Marrakech (Morocco) from 27th to 31st October 2016, the book presents solutions, methods, algorithms, case studies, and software. It is a valuable resource for research academics and industrial practitioners.

This book presents the theory and methods of flexible and generalized uncertainty optimization. Particularly, it describes the theory of generalized uncertainty in the context of optimization modeling. The book starts with an overview of flexible and generalized uncertainty optimization. It covers uncertainties that are both associated with lack of information and are more general than stochastic theory, where well-defined distributions are assumed. Starting from families of distributions that are enclosed by upper and lower functions, the book presents construction methods for obtaining flexible and generalized uncertainty input data that can be used in a flexible and generalized uncertainty optimization model. It then describes the development of the associated optimization model in detail. Written for graduate students and professionals in the broad field of optimization and operations research, this second edition has been revised and extended to include more worked examples and a section on interval multi-objective mini-max regret theory along with its solution method.

This contributed volume presents the state-of-the-art of games and dynamic games, featuring several chapters based on plenary sessions at the ISDG-China Chapter Conference on Dynamic Games and Game Theoretic Analysis, which was held from August 3-5, 2017 at the Ningbo campus of the University of Nottingham, China. The chapters in this volume will provide readers with paths to further research, serving as a testimony to the vitality of the field. Experts cover a range of theory and applications related to games and dynamic games, with topics including: Dynamically stable cooperative provision of public goods under non-transferable utility Strongly time-consistent solutions in cooperative dynamic games Incentive Stackelberg games for stochastic systems Static and inverse Stackelberg games in political economy Cournot and Betrand competition on symmetric R&D networks Numerical Nash equilibria using curvilinear multistart algorithm Markov chain approximation numerical scheme for infinite-horizon mean field games Frontiers in Games and Dynamic Games will appeal to an interdisciplinary audience of researchers, practitioners, and graduate students interested in games and dynamic games.

Deep connections exist between harmonic and applied analysis and the diverse yet connected topics of machine learning, data analysis, and imaging science. This volume explores these rapidly growing areas and features contributions presented at the second and third editions of the Summer Schools on Applied Harmonic Analysis, held at the University of Genova in 2017 and 2019. Each chapter offers an introduction to essential material and then demonstrates connections to more advanced research, with the aim of providing an accessible entrance for students and researchers. Topics covered include ill-posed problems; concentration inequalities; regularization and large-scale machine learning; unitarization of the radon transform on symmetric spaces; and proximal gradient methods for machine learning and imaging.

This book provides a timely and comprehensive overview of current theories and methods in fuzzy logic, as well as relevant applications in a variety of fields of science and technology. Dedicated to Lotfi A. Zadeh on his one year death anniversary, the book goes beyond a pure commemorative text. Yet, it offers a fresh perspective on a number of relevant topics, such as computing with words, theory of perceptions, possibility theory, and decision-making in a fuzzy environment. Written by Zadeh's closest colleagues and friends, the different chapters are intended both as a timely reference guide and a source of inspiration for scientists, developers and researchers who have been dealing with fuzzy sets or would like to learn more about their potential for their future research.

This book reports on the implementation of evolutionary-game theory in the design of distributed optimization-based controllers. First, it discusses how the classical population-game approach can contribute to and complement the design of optimization-based controllers. It shows how the features of this approach can be exploited to extend their capabilities in the solution of distributed optimization problems, and examines density games in order to consider multiple coupled constraints and preserve the non-centralized information requirements. Furthermore, it establishes a close relationship between the possible interactions among agents in a population with constrained information sharing among different local controllers. It also discusses coalitional games, focusing on the Shapley power index and proposes an alternative method of computing the latter, which reduces computational time, as well as a different way of finding it using distributed communication structures. All the proposed strategies are then tested on various control problems, such as those related to the Barcelona water supply network, multiple continuous stirred tank reactors, various unmanned aerial vehicle systems, and a water distribution system. This thesis, examined at the Universitat Politecnica de Catalunya and Universidad de los Andes in 2017, received the award for best thesis in control from the control group of the Spanish Committee of Automatic Control (CEA) in the same year.

This book collects some recent works on the application of dynamic game and control theory to the analysis of environmental problems. This collec tion of papers is not the outcome of a conference or of a workshop. It is rather the result of a careful screening from among a number of contribu tions that we have solicited across the world. In particular, we have been able to attract the work of some of the most prominent scholars in the field of dynamic analyses of the environment. Engineers, mathematicians and economists provide their views and analytical tools to better interpret the interactions between economic and environmental phenomena, thus achiev ing, through this interdisciplinary effort, new and interesting results. The goal of the book is more normative than descriptive. All papers include careful modelling of the dynamics of the main variables involved in the game between nature and economic agents and among economic agents themselves, as well-described in Vrieze's introductory chapter. Fur thermore, all papers use this careful modelling framework to provide policy prescriptions to the public agencies authorized to regulate emission dy namics. Several diverse problems are addressed: from global issues, such as the greenhouse effect or deforestation, to international ones, such as the management of fisheries, to local ones, for example, the control of effluent discharges. Moreover, pollution problems are not the only concern of this book."

This proceedings book presents state-of-the-art developments in theory, methodology, and applications of network analysis across sociology, computational science, education research, literature studies, political science, international relations, social media research, and urban studies. The papers comprising this collection were presented at the Fifth 'Networks in the Global World' conference organized by the Centre for German and European Studies of St. Petersburg University and Bielefeld University and held on July 7-9, 2020. This biannual conference series revolves around key interdisciplinary issues in the focus of network analysts, such as the multidimensional approach to social reality, translation of theories and methods across disciplines, and mixing of data and methods. The distinctive features of this book are the emphasis on in-depth linkages between theory, method, and applications, the blend of qualitative and quantitative methods, and the joint consideration of different network levels, types, and contexts. The topics covered by the papers include interrelation of social and cultural structures, constellations of power, and patterns of interaction in areas ranging from various types of communities (local, international, educational, political, and so on) to social media and literature. The book is useful for practicing researchers, graduate and postgraduate students, and educators interested in network analysis of social relations, politics, economy, and culture. Features that set the book apart from others in the field: * The book offers a unique cross-disciplinary blend of computational and ethnographic network analyses applied to a diverse spectrum of spheres, from literature and education to urban planning and policymaking. * Embracing conceptual, methodological, and empirical works, the book is among the few in network analysis to emphasize connections between theory, method, and applications. * The book brings together authors and empirical contexts from all over the globe, with a particular emphasis on European societies.

This book is the first comprehensive tutorial on matheuristics. Matheuristics are based on mathematical extensions of previously known heuristics, mainly metaheuristics, and on original, area-specific approaches. This tutorial provides a detailed discussion of both contributions, presenting the pseudocodes of over 40 algorithms, abundant literature references, and for each case a step-by-step description of a sample run on a common Generalized Assignment Problem example. C++ source codes of all algorithms are available in an associated SW repository.

This book is an original-the first-ever treatment of the mathematics of Luck. Setting out from the principle that luck can be measured by the gap between reasonable expectation and eventual realization, the book develops step-by-step a mathematical theory that accommodates the entire range of our pre-systematic understanding of the way in which luck functions in human affairs. In so moving from explanatory exposition to mathematical treatment, the book provides a clear and accessible account of the way in which luck assessment enters into the calculations of rational decision theory.

This monograph is devoted to the analysis and solution of singular differential games and singular $H_{\inf}$ control problems in both finite- and infinite-horizon settings. Expanding on the authors' previous work in this area, this novel text is the first to study the aforementioned singular problems using the regularization approach. After a brief introduction, solvability conditions are presented for the regular differential games and $H_{\inf}$ control problems. In the following chapter, the authors solve the singular finite-horizon linear-quadratic differential game using the regularization method. Next, they apply this method to the solution of an infinite-horizon type. The last two chapters are dedicated to the solution of singular finite-horizon and infinite-horizon linear-quadratic $H_{\inf}$ control problems. The authors use theoretical and real-world examples to illustrate the results and their applicability throughout the text, and have carefully organized the content to be as self-contained as possible, making it possible to study each chapter independently or in succession. Each chapter includes its own introduction, list of notations, a brief literature review on the topic, and a corresponding bibliography. For easier readability, detailed proofs are presented in separate subsections. Singular Linear-Quadratic Zero-Sum Differential Games and $H_{\inf}$ Control Problems will be of interest to researchers and engineers working in the areas of applied mathematics, dynamic games, control engineering, mechanical and aerospace engineering, electrical engineering, and biology. This book can also serve as a useful reference for graduate students in these area

Robert J Aumann has received numerous prizes, including the Nobel Memorial Prize in Economic Sciences for 2005.With his 1976 paper, 'Agreeing to Disagree', Robert Aumann pioneered the subject of interactive epistemology: the study of what people know, and what they know about what others know. Since then, the discipline has burgeoned enormously. This book documents Aumann's work leading to the 1976 paper and his subsequent contributions to the discipline. The scientific controversies emanating from his work are also included.

This volume collects papers, based on invited talks given at the IMA workshop in Modeling, Stochastic Control, Optimization, and Related Applications, held at the Institute for Mathematics and Its Applications, University of Minnesota, during May and June, 2018. There were four week-long workshops during the conference. They are (1) stochastic control, computation methods, and applications, (2) queueing theory and networked systems, (3) ecological and biological applications, and (4) finance and economics applications. For broader impacts, researchers from different fields covering both theoretically oriented and application intensive areas were invited to participate in the conference. It brought together researchers from multi-disciplinary communities in applied mathematics, applied probability, engineering, biology, ecology, and networked science, to review, and substantially update most recent progress. As an archive, this volume presents some of the highlights of the workshops, and collect papers covering a broad range of topics.

This book discusses one of the major applications of artificial intelligence: the use of machine learning to extract useful information from multimodal data. It discusses the optimization methods that help minimize the error in developing patterns and classifications, which further helps improve prediction and decision-making. The book also presents formulations of real-world machine learning problems, and discusses AI solution methodologies as standalone or hybrid approaches. Lastly, it proposes novel metaheuristic methods to solve complex machine learning problems. Featuring valuable insights, the book helps readers explore new avenues leading toward multidisciplinary research discussions.

For a long time the techniques of solving linear optimization (LP) problems improved only marginally. Fifteen years ago, however, a revolutionary discovery changed everything. A new golden age' for optimization started, which is continuing up to the current time. What is the cause of the excitement? Techniques of linear programming formed previously an isolated body of knowledge. Then suddenly a tunnel was built linking it with a rich and promising land, part of which was already cultivated, part of which was completely unexplored. These revolutionary new techniques are now applied to solve conic linear problems. This makes it possible to model and solve large classes of essentially nonlinear optimization problems as efficiently as LP problems. This volume gives an overview of the latest developments of such High Performance Optimization Techniques'. The first part is a thorough treatment of interior point methods for semidefinite programming problems. The second part reviews today's most exciting research topics and results in the area of convex optimization. Audience: This volume is for graduate students and researchers who are interested in modern optimization techniques.

The scientific and technical development of any kind of germplasm is regulated by a vast network of treaties, conventions, international agreements, and national and regional legislation. These regulations govern biotechnological innovations in plants and microorganisms, access to and use of plant genetic resources, and biosafety. This complex mix has made it difficult to arrive at global interpretations, due to overlaps, gaps, ambiguities, contradictions, and lack of consistency. The big picture is even more complex, as a series of scientific developments - gene editing in particular - have in some cases rendered these international regulatory frameworks obsolete. This book puts forward an innovative approach: a "Comprehensive Plant Germplasm System". The System is a cooperative game theory-based proposal for a binding international convention which would supersede all other conventions, treaties, national and regional legislation covering native varieties and traditional developments, heterogeneous plant varieties, microorganisms, biotechnological inventions, plant genetic resources, and biosafety regulation. In short, it offers a comprehensive framework regarding intellectual property, biosafety, and business regulation and covers all types of germplasm. If applied, the system is expected to yield higher productivity rates in crops and improved food biodiversity, as well as a new paradigm based on the promotion of innovation for "Agriculture 4.0."

This volume offers a wealth of interdisciplinary approaches to artificial intelligence, machine learning and optimization tools, which contribute to the optimization of urban features towards forming smart, sustainable, and livable future cities. Special features include: New research on the design of city elements and smart systems with respect to new technologies and scientific thinking Discussions on the theoretical background that lead to smart cities for the future New technologies and principles of research that can promote ideas of artificial intelligence and machine learning in optimized urban environments The book engages students and researchers in the subjects of artificial intelligence, machine learning, and optimization tools in smart sustainable cities as eminent international experts contribute their research results and thinking in its chapters. Overall, its audience can benefit from a variety of disciplines including, architecture, engineering, physics, mathematics, computer science, and related fields.

Based on the "Fourth International Conference on Dynamics of Disasters" (Kalamata, Greece, July 2019), this volume includes contributions from experts who share their latest discoveries on natural and unnatural disasters. Authors provide overviews of the tactical points involved in disaster relief, outlines of hurdles from mitigation and preparedness to response and recovery, and uses for mathematical models to describe natural and man-made disasters. Topics covered include economics, optimization, machine learning, government, management, business, humanities, engineering, medicine, mathematics, computer science, behavioral studies, emergency services, and environmental studies will engage readers from a wide variety of fields and backgrounds.

This book engages in an ongoing topic, such as the implementation of nature-inspired metaheuristic algorithms, with a main concentration on optimization problems in different fields of engineering optimization applications. The chapters of the book provide concise overviews of various nature-inspired metaheuristic algorithms, defining their profits in obtaining the optimal solutions of tiresome engineering design problems that cannot be efficiently resolved via conventional mathematical-based techniques. Thus, the chapters report on advanced studies on the applications of not only the traditional, but also the contemporary certain nature-inspired metaheuristic algorithms to specific engineering optimization problems with single and multi-objectives. Harmony search, artificial bee colony, teaching learning-based optimization, electrostatic discharge, grasshopper, backtracking search, and interactive search are just some of the methods exhibited and consulted step by step in application contexts. The book is a perfect guide for graduate students, researchers, academicians, and professionals willing to use metaheuristic algorithms in engineering optimization applications.

This book contains international perspectives that unifies the themes of strategic management, decision theory, and data science. It contains thought-provoking presentations of case studies backed by adequate analysis adding significance to the discussions. Most of the decision-making models in use do take due advantage of collection and processing of relevant data using appropriate analytics oriented to provide inputs into effective decision-making. The book showcases applications in diverse fields including banking and insurance, portfolio management, inventory analysis, performance assessment of comparable economic agents, managing utilities in a health-care facility, reducing traffic snarls on highways, monitoring achievement of some of the sustainable development goals in a country or state, and similar other areas that showcase policy implications. It holds immense value for researchers as well as professionals responsible for organizational decisions.

There has been a great deal of excitement over the last few years concerning the emergence of new mathematical techniques for the analysis and control of nonlinear systems: witness the emergence of a set of simplified tools for the analysis of bifurcations, chaos and other simplified tools for the analysis of bifurcations, chaos and other complicated dynamical behaviour and the development of a comprehensive theory of nonlinear control. Coupled with this set of analytic advances has been the vast increase in computational power available both for the simulation of nonlinear systems as well as for the implementation in real time of sophisticated, real-time nonlinear control laws. Thus, technological advances have bolstered the impact of analytic advances and produced a tremendous variety of new problems and applications which are nonlinear in an essential way. This book lays out in a concise mathematical framework the tools and methods of analysis which underlie this diversity of applications. The material presented in this book is culled from different 1st year graduate courses that the author has taught at MIT and at Berkeley.