This book offers a self-sufficient treatment of a key tool, game theory and mechanism design, to model, analyze, and solve centralized as well as decentralized design problems involving multiple autonomous agents that interact strategically in a rational and intelligent way. The contents of the book provide a sound foundation of game theory and mechanism design theory which clearly represent the “science” behind traditional as well as emerging economic applications for the society.The importance of the discipline of game theory has been recognized through numerous Nobel prizes in economic sciences being awarded to game theorists, including the 2005, 2007, and 2012 prizes. The book distills the marvelous contributions of these and other celebrated game theorists and presents it in a way that can be easily understood even by senior undergraduate students.A unique feature of the book is its detailed coverage of mechanism design which is the art of designing a game among strategic agents so that a social goal is realized in an equilibrium of the induced game. Another feature is a large number of illustrative examples that are representative of both classical and modern applications of game theory and mechanism design. The book also includes informative biographical sketches of game theory legends, and is specially customized to a general engineering audience.After a thorough reading of this book, readers would be able to apply game theory and mechanism design in a principled and mature way to solve relevant problems in computer science (esp, artificial intelligence/machine learning), computer engineering, operations research, industrial engineering and microeconomics.

This book explores how AI and mechanism design can provide a new framework for international politics. The international political system is all manners in which countries, governments and people relate. Mechanism design in international politics relates to identifying rules that define relationships between people and countries that achieve a particular outcome, e.g., peace or more trade or democracy or economic development. Artificial intelligence is technique of making machines intelligent. This book explores mechanism design and artificial intelligence in international politics and applies these technologies to politics, economy and society. This book will be of interest to scholars of international relations, politics, sustainable development, and artificial intelligence.

This monograph focuses on exploring game theoretic modeling and mechanism design for problem solving in Internet and network economics. For the first time, the main theoretical issues and applications of mechanism design are bound together in a single text.

'Mechanism Design for Total Quality Management' is clearly written in a logical manner and points are supported by real life case studies. Dr. Ogland demonstrates how a Total Quality Management strategy articulated through the use of bootstrap algorithms can be used to achieve world-class performance in challenging environments such as complex organisations saturated with power struggles and internal politics. The book features insights on critical systems thinking, game theory, quality management systems, the EFQM Business Excellence Model, self-assessment, and the implementation of TQM. Case studies provide practical insights from twenty years of empirical research on how to bootstrap TQM and Business Excellence in complex environments. The ideas developed in the book have been acknowledged as a major contribution to the theory of TQM, and the book itself is an indispensable resource for practitioners trying to implement TQM in environments where traditional implementation methods are bound to fail.

This book provides a critical, selective review of concepts from game theory and their applications in public policy, and further suggests some modifications for some of the models (chiefly in cooperative game theory) to improve their applicability to economics and public policy.

'Using the Bootstrap Algorithm for Changing the Control Game' is clearly written and points are supported by real life case studies. Dr. Ogland demonstrates how a Total Quality Management strategy articulated through the use of bootstrap algorithms can be used to achieve world-class performance in challenging environments such as complex organisations saturated with power struggles and internal politics. The book features insights on critical systems thinking, game theory, quality management systems, the Efqm Business Excellence Model, self-assessment, and the implementation of Tqm. Case studies provide practical insights from twenty years of empirical research on how to bootstrap Tqm and Business Excellence in complex environments. The ideas developed in the book have been acknowledged as a major contribution to the theory of Tqm, and the book itself is an indispensable resource for practitioners trying to implement Tqm in environments where traditional implementation methods are bound to fail.

Game Theory And Decision Theory In Agent-Based Systems is a collection of papers from international leading researchers, that offers a broad view of the many ways game theory and decision theory can be applied in agent-based systems, from standard applications of the core elements of the theory to more cutting edge developments. The range of topics discussed in this book provide the reader with the first comprehensive volume that reflects both the depth and breadth of work in applying techniques from game theory and decision theory to design agent-based systems. Chapters include: Selecting Partners; Evolution of Agents with Moral Sentiments in an IPD Exercise; Dynamic Desires; Emotions and Personality; Decision-Theoretic Approach to Game Theory; Shopbot Economics; Finding the Best Way to Join in; Shopbots and Pricebots in Electronic Service Markets; Polynomial Time Mechanisms; Multi-Agent Q-learning and Regression Trees; Satisficing Equilibria; Investigating Commitment Flexibility in Multi-agent Contracts; Pricing in Agent Economies using Multi-agent Q-learning; Using Hypergames to Increase Planned Payoff and Reduce Risk; Bilateral Negotiation with Incomplete and Uncertain Information; Robust Combinatorial Auction Protocol against False-name Bids.

As with the previous editions, this fourth edition relies on teaching by example and the Karplus Learning Cycle to convey the ideas of game theory in a way that is approachable, intuitive, and interdisciplinary. Noncooperative equilibrium concepts such as Nash equilibrium, mixed strategy equilibria, and subgame perfect equilibrium are systematically introduced in the first half of the book. Bayesian Nash equilibrium is briefly introduced. The subsequent chapters discuss cooperative solutions with and without side payments, rationalizable strategies and correlated equilibria, and applications to elections, social mechanism design, and larger-scale games. New examples include panic buying, supply-chain shifts in the pandemic, and global warming.

A critical challenge in deep learning is the vulnerability of deep learning networks to security attacks from intelligent cyber adversaries. Even innocuous perturbations to the training data can be used to manipulate the behaviour of deep networks in unintended ways. In this book, we review the latest developments in adversarial attack technologies in computer vision; natural language processing; and cybersecurity with regard to multidimensional, textual and image data, sequence data, and temporal data. In turn, we assess the robustness properties of deep learning networks to produce a taxonomy of adversarial examples that characterises the security of learning systems using game theoretical adversarial deep learning algorithms. The state-of-the-art in adversarial perturbation-based privacy protection mechanisms is also reviewed. We propose new adversary types for game theoretical objectives in non-stationary computational learning environments. Proper quantification of the hypothesis set in the decision problems of our research leads to various functional problems, oracular problems, sampling tasks, and optimization problems. We also address the defence mechanisms currently available for deep learning models deployed in real-world environments. The learning theories used in these defence mechanisms concern data representations, feature manipulations, misclassifications costs, sensitivity landscapes, distributional robustness, and complexity classes of the adversarial deep learning algorithms and their applications. In closing, we propose future research directions in adversarial deep learning applications for resilient learning system design and review formalized learning assumptions concerning the attack surfaces and robustness characteristics of artificial intelligence applications so as to deconstruct the contemporary adversarial deep learning designs. Given its scope, the book will be of interest to Adversarial Machine Learning practitioners and Adversarial Artificial Intelligence researchers whose work involves the design and application of Adversarial Deep Learning.

This book constitutes the refereed proceedings of the Second International Symposium on Algorithmic Game Theory, SAGT 2009, held in Paphos, Cyprus, in October 2009. The 29 revised full papes presented together with 3 invited lectures were carefully reviewed and selected from 55 submissions. The papers are intended to cover all important areas such as solution concepts, game classes, computation of equilibria and market equilibria, algorithmic mechanism design, automated mechanism design, convergence and learning in games, complexity classes in game theory, algorithmic aspects of fixed-point theorems, mechanisms, incentives and coalitions, cost-sharing algorithms, computational problems in economics, finance, decision theory and pricing, computational social choice, auction algorithms, price of anarchy and its relatives, representations of games and their complexity, economic aspects of distributed computing and the internet, congestion, routing and network design and formation games and game-theoretic approaches to networking problems.