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.

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.

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.

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.

Extremum Seeking through Delays and PDEs, the first book on the topic, expands the scope of applicability of the extremum seeking method, from static and finite-dimensional systems to infinite-dimensional systems. Readers will find: Numerous algorithms for model-free real-time optimization are developed and their convergence guaranteed. Extensions from single-player optimization to noncooperative games, under delays and pdes, are provided. The delays and pdes are compensated in the control designs using the pde backstepping approach, and stability is ensured using infinite-dimensional versions of averaging theory. Accessible and powerful tools for analysis. This book is intended for control engineers in all disciplines (electrical, mechanical, aerospace, chemical), mathematicians, physicists, biologists, and economists. It is appropriate for graduate students, researchers, and industrial users.

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.

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.

This book presents a short introduction to continuous-time financial models. An overview of the basics of stochastic analysis precedes a focus on the Black-Scholes and interest rate models. Other topics covered include self-financing strategies, option pricing, exotic options and risk-neutral probabilities. Vasicek, Cox-Ingersoll-Ross, and Heath-Jarrow-Morton interest rate models are also explored. The author presents practitioners with a basic introduction, with more rigorous information provided for mathematicians. The reader is assumed to be familiar with the basics of probability theory. Some basic knowledge of stochastic integration and differential equations theory is preferable, although all preliminary information is given in the first part of the book. Some relatively simple theoretical exercises are also provided.

Nature-Inspired Optimization Algorithms provides a systematic introduction to all major nature-inspired algorithms for optimization. The book's unified approach, balancing algorithm introduction, theoretical background and practical implementation, complements extensive literature with well-chosen case studies to illustrate how these algorithms work. Topics include particle swarm optimization, ant and bee algorithms, simulated annealing, cuckoo search, firefly algorithm, bat algorithm, flower algorithm, harmony search, algorithm analysis, constraint handling, hybrid methods, parameter tuning and control, as well as multi-objective optimization. This book can serve as an introductory book for graduates, doctoral students and lecturers in computer science, engineering and natural sciences. It can also serve a source of inspiration for new applications. Researchers and engineers as well as experienced experts will also find it a handy reference.

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.

This book presents a crisis scenario generator with black swans, black butterflies and worst case scenarios. It is the most useful scenario generator that can be used to manage assets in a crisis-prone period, offering more reliable values for Value at Risk (VaR), Conditional Value at Risk (CVaR) and Tail Value at Risk (TVaR). Hazardous Forecasts and Crisis Scenario Generator questions how to manage assets when crisis probability increases, enabling you to adopt a process for using generators in order to be well prepared for handling crises.

It has widely been recognized that submodular functions play essential roles in efficiently solvable combinatorial optimization problems. Since the publication of the 1st edition of this book fifteen years ago, submodular functions have been showing further increasing importance in optimization, combinatorics, discrete mathematics, algorithmic computer science, and algorithmic economics, and there have been made remarkable developments of theory and algorithms in submodular functions. The 2nd edition of the book supplements the 1st edition with a lot of remarks and with new two chapters: "Submodular Function Minimization" and "Discrete Convex Analysis." The present 2nd edition is still a unique book on submodular functions, which is essential to students and researchers interested in combinatorial optimization, discrete mathematics, and discrete algorithms in the fields of mathematics, operations research, computer science, and economics.

Key features:

- Self-contained exposition of the theory of submodular functions.
- Selected up-to-date materials substantial to future developments.
- Polyhedral description of Discrete Convex Analysis.
- Full description of submodular function minimization algorithms.
- Effective insertion of figures.
- Useful in applied mathematics, operations research, computer science, and economics.
- Self-contained exposition of the theory of submodular functions.
- Selected up-to-date materials substantial to future developments.
- Polyhedral description of Discrete Convex Analysis.
- Full description of submodular function minimization algorithms.
- Effective insertion of figures.
- Useful in applied mathematics, operations research, computer science, and economics.

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.