​This book both summarizes the basic theory of evolutionary games and explains their developing applications, giving special attention to the 2-player, 2-strategy game. This game, usually termed a "2×2 game” in the jargon, has been deemed most important because it makes it possible to posit an archetype framework that can be extended to various applications for engineering, the social sciences, and even pure science fields spanning theoretical biology, physics, economics, politics, and information science. The 2×2 game is in fact one of the hottest issues in the field of statistical physics. The book first shows how the fundamental theory of the 2×2 game, based on so-called replicator dynamics, highlights its potential relation with nonlinear dynamical systems. This analytical approach implies that there is a gap between theoretical and reality-based prognoses observed in social systems of humans as well as in those of animal species. The book explains that this perceived gap is the result of an underlying reciprocity mechanism called social viscosity. As a second major point, the book puts a sharp focus on network reciprocity, one of the five fundamental mechanisms for adding social viscosity to a system and one that has been a great concern for study by statistical physicists in the past decade. The book explains how network reciprocity works for emerging cooperation, and readers can clearly understand the existence of substantial mechanics when the term "network reciprocity" is used. In the latter part of the book, readers will find several interesting examples in which evolutionary game theory is applied. One such example is traffic flow analysis. Traffic flow is one of the subjects that fluid dynamics can deal with, although flowing objects do not comprise a pure fluid but, rather, are a set of many particles. Applying the framework of evolutionary games to realistic traffic flows, the book reveals that social dilemma structures lie behind traffic flow.

Game Theory and Experimental Games: The Study of Strategic Interaction focuses on the development of game theory, taking into consideration empirical research, theoretical formulations, and research procedures involved. The book proceeds with a discussion on the theory of one-person games. The individual decision that a player makes in these kinds of games is noted as influential as to the outcome of these games. This discussion is followed by a presentation of pure coordination games and minimal situation. The ability of players to anticipate the choices of others to achieve a mutually beneficial outcome is emphasized. A favorable social situation is also influential in these kinds of games. The text moves forward by presenting studies on various kinds of competitive games. The research studies presented are coupled with empirical evidence and discussion designed to support the claims that are pointed out. The book also discusses several kinds of approaches in the study of games. Voting as a way to resolve multi-person games is also emphasized, including voting procedures, the preferences of voters, and voting strategies. The book is a valuable source of data for readers and scholars who are interested in the exploration of game theories.

Recent applications of evolutionary game theory in the merging fields of the mathematical and social sciences are brilliantly portrayed in this book, which highlights social physics and shows how the approach can help to quantitatively model complex human–environmental–social systems. First, readers are introduced to the fundamentals of evolutionary game theory. The two-player, two-strategy game, or the 2 × 2 game, is presented as an archetype to help understand the difficulty of cooperating for survival against defection in common social contexts. Subsequently, the book explains the theoretical background of the multi-player, two-strategy game, which may be more widely applicable than the 2 × 2 game for social dilemmas. The latest applications of 2 × 2 games are also discussed to explore how integrated reciprocity mechanisms can solve social dilemmas. In turn, the book describes two practical areas in which evolutionary game theory has been applied. The first concerns traffic flow analysis. In conventional interpretations, traffic flow can be understood by means of fluid dynamics, in which the flow of vehicles is evaluated as a continuum body. Such a simple idea, however, does not work well in reality, particularly if a driver’s decision-making process is considered. Various dilemmas involve complex structures that depend primarily on traffic density, a revelation that should help establish a practical solution for reducing traffic congestion. Second, the book provides keen insights into how powerful evolutionary game theory can be in the context of epidemiology. Both approaches, quasi-analytical and multi-agent simulation, can clarify how an infectious disease such as seasonal influenza spreads across a complex social network, which is significantly affected by the public attitude toward vaccination. A methodology is proposed for the optimum design of a public vaccination policy incorporating subsidies to efficiently increase vaccination coverage while minimizing the social cost.

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 × 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.

The outstanding feature of this book is that it provides a unified account of three types of decision problem. It covers the basic ideas of decision theory, classical game theory, and evolutionary game theory in one volume. No background knowledge of economics or biology is required as examples have been carefully selected for their accessibility. Detailed solutions to the numerous exercises are provided at the back of the book, making it ideal for self-study. This introduction to game theory is intended as a first course for undergraduate students of mathematics, but it will also interest advanced students or researchers in biology and economics.

Introduces current evolutionary game theory--where ideas from evolutionary biology and rationalistic economics meet--emphasizing the links between static and dynamic approaches and noncooperative game theory. This text introduces current evolutionary game theory--where ideas from evolutionary biology and rationalistic economics meet--emphasizing the links between static and dynamic approaches and noncooperative game theory. Much of the text is devoted to the key concepts of evolutionary stability and replicator dynamics. The former highlights the role of mutations and the latter the mechanisms of selection. Moreover, set-valued static and dynamic stability concepts, as well as processes of social evolution, are discussed. Separate background chapters are devoted to noncooperative game theory and the theory of ordinary differential equations. There are examples throughout as well as individual chapter summaries. Because evolutionary game theory is a fast-moving field that is itself branching out and rapidly evolving, Jörgen Weibull has judiciously focused on clarifying and explaining core elements of the theory in an up-to-date, comprehensive, and self-contained treatment. The result is a text for second-year graduate students in economic theory, other social sciences, and evolutionary biology. The book goes beyond filling the gap between texts by Maynard-Smith and Hofbauer and Sigmund that are currently being used in the field. Evolutionary Game Theory will also serve as an introduction for those embarking on research in this area as well as a reference for those already familiar with the field. Weibull provides an overview of the developments that have taken place in this branch of game theory, discusses the mathematical tools needed to understand the area, describes both the motivation and intuition for the concepts involved, and explains why and how it is relevant to economics.

This book presents a comprehensive new, multi-objective and integrative view on traditional game and control theories. Consisting of 15 chapters, it is divided into three parts covering noncooperative games; mixtures of simultaneous and sequential multi-objective games; and multi-agent control of Pareto-Nash-Stackelberg-type games respectively. Can multicriteria optimization, game theory and optimal control be integrated into a unique theory? Are there mathematical models and solution concepts that could constitute the basis of a new paradigm? Is there a common approach and method to solve emerging problems? The book addresses these and other related questions and problems to create the foundation for the Pareto-Nash-Stackelberg Game and Control Theory. It considers a series of simultaneous/Nash and sequential/Stackelberg games, single-criterion and multicriteria/Pareto games, combining Nash and Stackelberg game concepts and Pareto optimization, as well as a range of notions related to system control. In addition, it considers the problems of finding and representing the entire set of solutions. Intended for researches, professors, specialists, and students in the areas of game theory, operational research, applied mathematics, economics, computer science and engineering, it also serves as a textbook for various courses in these fields.

Game Theory through Examples is a thorough introduction to elementary game theory, covering finite games with complete information. The core philosophy underlying this volume is that abstract concepts are best learned when encountered first (and repeatedly) in concrete settings. Thus, the essential ideas of game theory are here presented in the context of actual games, real games much more complex and rich than the typical toy examples. All the fundamental ideas are here: Nash equilibria, backward induction, elementary probability, imperfect information, extensive and normal form, mixed and behavioral strategies. The active-learning, example-driven approach makes the text suitable for a course taught through problem solving. Students will be thoroughly engaged by the extensive classroom exercises, compelling homework problems, and nearly sixty projects in the text. Also available are approximately eighty Java applets and three dozen Excel spreadsheets in which students can play games and organize information in order to acquire a gut feeling to help in the analysis of the games. Mathematical exploration is a deep form of play; that maxim is embodied in this book. Game Theory through Examples is a lively introduction to this appealing theory. Assuming only high school prerequisites makes the volume especially suitable for a liberal arts or general education spirit-of-mathematics course. It could also serve as the active-learning supplement to a more abstract text in an upper-division game theory course.

This volume is based on lectures delivered at the 2011 AMS Short Course on Evolutionary Game Dynamics, held January 4-5, 2011 in New Orleans, Louisiana. Evolutionary game theory studies basic types of social interactions in populations of players. It combines the strategic viewpoint of classical game theory (independent rational players trying to outguess each other) with population dynamics (successful strategies increase their frequencies). A substantial part of the appeal of evolutionary game theory comes from its highly diverse applications such as social dilemmas, the evolution of language, or mating behaviour in animals. Moreover, its methods are becoming increasingly popular in computer science, engineering, and control theory. They help to design and control multi-agent systems, often with a large number of agents (for instance, when routing drivers over highway networks or data packets over the Internet). While these fields have traditionally used a top down approach by directly controlling the behaviour of each agent in the system, attention has recently turned to an indirect approach allowing the agents to function independently while providing incentives that lead them to behave in the desired way. Instead of the traditional assumption of equilibrium behaviour, researchers opt increasingly for the evolutionary paradigm and consider the dynamics of behaviour in populations of agents employing simple, myopic decision rules.