The Handbook of Experimental Game Theory offers a comprehensive analysis of the field, discussing foundational topics that are at the core of applied game theory. It highlights the nuances that scientific experiments have delivered to our understanding of strategic interactions among decision makers. Leading experts explore methodological considerations and games of complete and incomplete information to offer new directions for research in experimental game theory. Chapters demonstrate transformative behavioral research focused on classic topics in game theory such as cooperation and coordination games. Taking a scientific approach to the study of game theory, this innovative Handbook provides an insight into laboratory and field experiments that test game theoretic propositions and suggests new ways of modeling strategic behavior. It takes a forward-thinking position, addressing the challenges inherent in innovations surrounding the measurement of strategic behavior using experimental methods. This Handbook will prove to be a valuable resource for scholars and students who are looking to gain a broader understanding of experimental game theory and how to contribute to its advancement. It will also be of particular interest to researchers in experimental and behavioral economics.

Economic theory and philosophy have discussed concepts of fairness, but the criteria of fairness are in each case absolute: a situation is either fair or it is not. This book draws on these literatures to propose two criteria of relative fairness, and a hierarchical rule for the priority of application of these criteria, with a view to comparison of practicable alternatives in public policy. A veil-of-ignorance device of representation of rational fairness is used to argue that these criteria are normatively relevant. Applications to intergenerational fairness, fairness among regions in the context of migration, externalities and Pigovian taxes, to fair prices and wages, and to relative fairness in the status of racial and caste groups are sketched. The book is designed with real world public policy practice. Scholars with an interest in the economic evaluation of public policy will find this compelling book essential reading.

Visual novels (VNs), a ludic video game genre that pairs textual fiction stories with anime-like images and varying degrees of interactivity, have increased in popularity among Western audiences in recent years. Despite originating in Japan, these stories have made their way into global culture as a genre accessible for both play and creation with wide-ranging themes from horror and loneliness to sexuality. The History and Allure of Interactive Visual Novels begins with a comprehensive overview of the visual novel genre and the cultural evolution that led to its rise, then explains the tropes and appeal of subgenres like bishojo (cute girl games), detective games, horror, and eroge (erotic games). Finally, the book explores the future of the genre in both user-generated games and games from other genres that liberally borrow both narrative and ludological themes from visual novels. Whether you’re a long-standing fan of the genre or a newcomer looking for a fresh experience, The History and Allure of Interactive Visual Novels will provide an accessible and critically engaging overview of a genre that is rich in storytelling yet often overlooked.

This book addresses the mathematical and the practical aspects of motion implied by advanced control theory. The richness and power of the theory are demonstrated by separate analyses of single-model and multi-modal repertoires, consisting of verities of estimation and control facets. Starting with purely mathematical concepts, specifically, abstract probability and information theories, model control theory is gradually revealed as a rather amazing domain. The mathematical equations, taking essentially simple forms, are exposed as powerful generators of motion. Moreover, seemingly obvious applications of the theory, such as high-performance aircraft control make room for unexpected virtual reality feedback in control of motion for the neurologically impaired.Following the presentation of some historical milestones and mathematical preliminaries, the book is divided into four parts. The first deals with minimal-order models of state estimation and control. The second addresses multi-modal estimation and control, which facilitates the operation of high-performance aircraft in large flight envelopes. The third presents the transition from naturally nonlinear control of movement in obstacle avoidance and object targeting to virtually linear control of movement in the neurologically impaired. The fourth and final part of the book addresses the application of virtual sensory feedback in walking with specific neurological impairment. While the clinical studies reported were all based on a single-model paradigm, a later reflection reveals that, given the variety of neurological symptoms associated with the relevant disorders, a multi-modal approach, as that addressed in the control of high-performance aircraft in a large flight envelope, would be similarly applicable in the treatment of neurological disorders.

Many applications, including computer vision, computer arithmetic, deep learning, entanglement in quantum information, graph theory and energy networks, can be successfully tackled within the framework of polynomial optimization, an emerging field with growing research efforts in the last two decades. One key advantage of these techniques is their ability to model a wide range of problems using optimization formulations. Polynomial optimization heavily relies on the moment-sums of squares (moment-SOS) approach proposed by Lasserre, which provides certificates for positive polynomials. On the practical side, however, there is 'no free lunch' and such optimization methods usually encompass severe scalability issues. Fortunately, for many applications, including the ones formerly mentioned, we can look at the problem in the eyes and exploit the inherent data structure arising from the cost and constraints describing the problem.This book presents several research efforts to resolve this scientific challenge with important computational implications. It provides the development of alternative optimization schemes that scale well in terms of computational complexity, at least in some identified class of problems. It also features a unified modeling framework to handle a wide range of applications involving both commutative and noncommutative variables, and to solve concretely large-scale instances. Readers will find a practical section dedicated to the use of available open-source software libraries.This interdisciplinary monograph is essential reading for students, researchers and professionals interested in solving optimization problems with polynomial input data.

This book covers an introduction to convex optimization, one of the powerful and tractable optimization problems that can be efficiently solved on a computer. The goal of the book is tohelp develop a sense of what convex optimization is, and how it can be used in a widening array of practical contexts with a particular emphasis on machine learning.The first part of the book covers core concepts of convex sets, convex functions, and related basic definitions that serve understanding convex optimization and its corresponding models. The second part deals with one very useful theory, called duality, which enables us to: (1) gain algorithmic insights; and (2) obtain an approximate solution to non-convex optimization problems which are often difficult to solve. The last part focuses on modern applications in machine learning and deep learning.A defining feature of this book is that it succinctly relates the "story" of how convex optimization plays a role, via historical examples and trending machine learning applications. Another key feature is that it includes programming implementation of a variety of machine learning algorithms inspired by optimization fundamentals, together with a brief tutorial of the used programming tools. The implementation is based on Python, CVXPY, and TensorFlow. This book does not follow a traditional textbook-style organization, but is streamlined via a series of lecture notes that are intimately related, centered around coherent themes and concepts. It serves as a textbook mainly for a senior-level undergraduate course, yet is also suitable for a first-year graduate course. Readers benefit from having a good background in linear algebra, some exposure to probability, and basic familiarity with Python.

Why do some games seem to be universal while others have a particular connection to the culture of the people playing them? Around the World in 80 Games is about the mathematics of chance, game theory, gamification, gaming strategies and computer games. Traversing the globe, Marcus du Sautoy looks at the genesis of games new and old, explores how to invent a good game and explains the fascination of a popular lockdown game. From the secrets of whist to hopscotch, Scrabble to Wordle. The most simple games endure: board games, card games and dice games have captivated us for centuries and the acclaimed mathematician and author of The Creativity Code (among many others) will once again bring mathematics to the fore with insight and aplomb in Around the World in 80 Games.

The book addresses optimization in the petroleum industry from a practical, large-scale-application-oriented point of view. The models and techniques presented help to optimize the limited resources in the industry in order to maximize economic benefits, ensure operational safety, and reduce environmental impact. The book discusses several important real-life applications of optimization in the petroleum industry, ranging from the scheduling of personnel time to the blending of gasoline. It covers a wide spectrum of relevant activities, including drilling, producing, maintenance, and distribution. The text begins with an introductory overview of the petroleum industry and then of optimization models and techniques. The main body of the book details a variety of applications of optimization models and techniques within the petroleum industry. Applied  Optimization in the Petroleum Industry helps readers to find effective optimization-based solutions to their own practical problems in a large and important industrial sector, still the main source of the world’s energy and the source of raw materials for a wide variety of industrial and consumer products.

This book is a scientific basis for understanding the urgent need for a Great Transformation to a third step in social evolution. Already being a community of common destiny, humanity can form an actual unity through diversity to avoid extinction. Social actors can recognise informational imperatives for cognition, communication and co-operation to achieve such a unity. By doing so, they apply a logic that underlies the structuration of any agency, which is a real logic of self-organising systems from the physical to the social. This logic is the Logic of the Third - the Third is a meta-structure that emerges in a leap. The agents interact and when they co-act they are likely to form a real meta-structure of organisational relations. Informational agents anticipate this by generating requisite information in their attempt to cope with complex challenges. Such an information is a meta-structure too. The Third helps achieve synergy effects.This book discusses considerations from philosophy, systems theory, the study of information, social systems, social information, ecology and technology. It addresses ethical issues connected with the long-forgotten arms race in an atomic age, the global warming not yet under control, the pandemic misunderstood, the social question still unanswered.

Data-driven discovery is revolutionizing how we model, predict, and control complex systems. Now with Python and MATLAB (R), this textbook trains mathematical scientists and engineers for the next generation of scientific discovery by offering a broad overview of the growing intersection of data-driven methods, machine learning, applied optimization, and classical fields of engineering mathematics and mathematical physics. With a focus on integrating dynamical systems modeling and control with modern methods in applied machine learning, this text includes methods that were chosen for their relevance, simplicity, and generality. Topics range from introductory to research-level material, making it accessible to advanced undergraduate and beginning graduate students from the engineering and physical sciences. The second edition features new chapters on reinforcement learning and physics-informed machine learning, significant new sections throughout, and chapter exercises. Online supplementary material - including lecture videos per section, homeworks, data, and code in MATLAB (R), Python, Julia, and R - available on databookuw.com.

Developments in the use of game theory have impacted multiple fields and created opportunities for new applications. With the ubiquity of these developments, there is an increase in the overall utilization of this approach. Game Theory: Breakthroughs in Research and Practice contains a compendium of the latest academic material on the usage, strategies, and applications for implementing game theory across a variety of industries and fields. Including innovative studies on economics, military strategy, and political science, this multi-volume book is an ideal source for professionals, practitioners, graduate students, academics, and researchers interested in the applications of game theory.

Through analysis of three case study videogames - Left 4 Dead 2, DayZ and Minecraft - and their online player communities, Digital Zombies, Undead Stories develops a framework for understanding how collective gameplay generates experiences of narrative, as well as the narrative dimensions of players' creative activity on social media platforms. Narrative emergence is addressed as a powerful form of player experience in multiplayer games, one which makes individual games' boundaries and meanings fluid and negotiable by players. The phenomenon is also shown to be recursive in nature, shaping individual and collective understandings of videogame texts over time. Digital Zombies, Undead Stories focuses on games featuring zombies as central antagonists. The recurrent figure of the videogame zombie, which mediates between chaos and rule-driven predictability, serves as both metaphor and mascot for narrative emergence. This book argues that in the zombie genre, emergent experiences are at the heart of narrative experiences for players, and more broadly demonstrates the potential for the phenomenon to be understood as a fundamental part of everyday play experiences across genres.

The book systematically introduces smart power system design and its infrastructure, platform and operating standards. It focuses on multi-objective optimization and illustrates where the intelligence of the system lies. With abundant project data, this book is a practical guideline for engineers and researchers in electrical engineering, as well as power network designers and managers in administration.

Optimization techniques have developed into a modern-day solution for real-world problems in various industries. As a way to improve performance and handle issues of uncertainty, optimization research becomes a topic of special interest across disciplines. Problem Solving and Uncertainty Modeling through Optimization and Soft Computing Applications presents the latest research trends and developments in the area of applied optimization methodologies and soft computing techniques for solving complex problems. Taking a multi-disciplinary approach, this critical publication is an essential reference source for engineers, managers, researchers, and post-graduate students.

Optimization is the act of obtaining the "best" result under given circumstances. In design, construction, and maintenance of any engineering system, engineers must make technological and managerial decisions to minimize either the effort or cost required or to maximize benefits. There is no single method available for solving all optimization problems efficiently. Several optimization methods have been developed for different types of problems. The optimum-seeking methods are mathematical programming techniques (specifically, nonlinear programming techniques). Nonlinear Optimization: Models and Applications presents the concepts in several ways to foster understanding. Geometric interpretation: is used to re-enforce the concepts and to foster understanding of the mathematical procedures. The student sees that many problems can be analyzed, and approximate solutions found before analytical solutions techniques are applied. Numerical approximations: early on, the student is exposed to numerical techniques. These numerical procedures are algorithmic and iterative. Worksheets are provided in Excel, MATLAB(R), and Maple(TM) to facilitate the procedure. Algorithms: all algorithms are provided with a step-by-step format. Examples follow the summary to illustrate its use and application. Nonlinear Optimization: Models and Applications: Emphasizes process and interpretation throughout Presents a general classification of optimization problems Addresses situations that lead to models illustrating many types of optimization problems Emphasizes model formulations Addresses a special class of problems that can be solved using only elementary calculus Emphasizes model solution and model sensitivity analysis About the author: William P. Fox is an emeritus professor in the Department of Defense Analysis at the Naval Postgraduate School. He received his Ph.D. at Clemson University and has taught at the United States Military Academy and at Francis Marion University where he was the chair of mathematics. He has written many publications, including over 20 books and over 150 journal articles. Currently, he is an adjunct professor in the Department of Mathematics at the College of William and Mary. He is the emeritus director of both the High School Mathematical Contest in Modeling and the Mathematical Contest in Modeling.