Agents Games And Evolution

Games, or contexts of strategic interaction, pervade and suffuse our lives and the lives of all organisms. How are we to make sense of and cope with such situations? How should an agent play? When will and when won't cooperation arise and be maintained? Using examples and a careful digestion of the literature, Agents, Games, and Evolution: Strategies at Work and Play addresses these encompassing themes throughout, and is organized into four parts: Part I introduces classical game theory and strategy selection. It compares ideally rational and the "naturalist" approach used by this book, which focuses on how actual agents chose their strategies, and the effects of these strategies on model systems. Part II explores a number of basic games, using models in which agents have fixed strategies. This section draws heavily on the substantial literature associated with the relevant application areas in the social sciences. Part III reviews core results and applications of agent-based models in which strategic interaction is present and for which design issues have genuine practical import. This section draws heavily on the substantial literature associated with the application area to hand. Part IV addresses miscellaneous topics in strategic interaction, including lying in negotiations, reasoning by backward induction, and evolutionary models. Modelled after the authors' Agents, Games, and Evolution course at the University of Pennsylvania, this book keeps mathematics to a minimum, focusing on computational strategies and useful methods for dealing with a variety of situations.

Evolutionary game theory studies the behaviour of large populations of strategically interacting agents & is used by economists to predict in settings where traditional assumptions about the rationality of agents & knowledge may be inapplicable.

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.

Evolving agents to play games is a promising technology. It can provide entertaining opponents for games like Chess or Checkers, matched to a human opponent as an alternative to the perfect and unbeatable opponents embodied by current artifical intelligences. Evolved agents also permit us to explore the strategy space of mathematical games like Prisoner's Dilemma and Rock-Paper-Scissors. This book summarizes, explores, and extends recent work showing that there are many unsuspected factors that must be controlled in order to create a plausible or useful set of agents for modeling cooperation and conflict, deal making, or other social behaviors. The book also provides a proposal for an agent training protocol that is intended as a step toward being able to train humaniform agents—in other words, agents that plausibly model human behavior.

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.

Samir Okasha approaches evolutionary biology from a philosophical perspective in Agents and Goals in Evolution, analysing a mode of thinking in biology called agential thinking. He considers how the paradigm case involves treating an evolved organism as if it were an agent pursuing a goal, such as survival or reproduction, and seeing its phenotypic traits as strategies for achieving that goal or furthering its biological interests. As agential thinking deliberately transposes a set of concepts--goals, interests, strategies--from rational human agents and to the biological world more generally, Okasha's enquiry firstly looks at the justification for this: is it mere anthropomorphism, or does it play a genuine intellectual role in the science? From this central question, key points are considered such as: how do we identify the 'goal' that evolved organisms will behave as if they are trying to achieve? Can agential thinking ever be applied to groups rather than to individual organisms? And how does agential thinking relate to the controversies over fitness-maximization in evolutionary biology? In addition, Okasha examines the relation between the adaptive and the rational by considering whether organisms can validly be treated as agent-like. Should we expect their evolved behaviour to correspond with that of rational agents as codified in the theory of rational choice? If so, does this mean that the fitness-maximizing paradigm of the evolutionary biologist can be mapped directly to the utility-maximizing paradigm of the rational choice theorist? All of these important questions are engagingly raised and discussed at length.

Every form of behaviour is shaped by trial and error. Such stepwise adaptation can occur through individual learning or through natural selection, the basis of evolution. Since the work of Maynard Smith and others, it has been realised how game theory can model this process. Evolutionary game theory replaces the static solutions of classical game theory by a dynamical approach centred not on the concept of rational players but on the population dynamics of behavioural programmes. In this book the authors investigate the nonlinear dynamics of the self-regulation of social and economic behaviour, and of the closely related interactions between species in ecological communities. Replicator equations describe how successful strategies spread and thereby create new conditions which can alter the basis of their success, i.e. to enable us to understand the strategic and genetic foundations of the endless chronicle of invasions and extinctions which punctuate evolution. In short, evolutionary game theory describes when to escalate a conflict, how to elicit cooperation, why to expect a balance of the sexes, and how to understand natural selection in mathematical terms.

This 1982 book is an account of an alternative way of thinking about evolution and the theory of games.

In almost every human society some people get more and others get less. Why is inequity the rule in these societies? In The Origins of Unfairness, philosopher Cailin O'Connor firstly considers how groups are divided into social categories, like gender, race, and religion, to address this question. She uses the formal frameworks of game theory and evolutionary game theory to explore the cultural evolution of the conventions which piggyback on these seemingly irrelevant social categories. These frameworks elucidate a variety of topics from the innateness of gender differences, to collaboration in academia, to household bargaining, to minority disadvantage, to homophily. They help to show how inequity can emerge from simple processes of cultural change in groups with gender and racial categories, and under a wide array of situations. The process of learning conventions of coordination and resource division is such that some groups will tend to get more and others less. O'Connor offers solutions to such problems of coordination and resource division and also shows why we need to think of inequity as part of an ever evolving process. Surprisingly minimal conditions are needed to robustly produce phenomena related to inequity and, once inequity emerges in these models, it takes very little for it to persist indefinitely. Thus, those concerned with social justice must remain vigilant against the dynamic forces that push towards inequity.

Since its original publication in 2000, Game Theory Evolving has been considered the best textbook on evolutionary game theory. This completely revised and updated second edition of Game Theory Evolving contains new material and shows students how to apply game theory to model human behavior in ways that reflect the special nature of sociality and individuality. The textbook continues its in-depth look at cooperation in teams, agent-based simulations, experimental economics, the evolution and diffusion of preferences, and the connection between biology and economics. Recognizing that students learn by doing, the textbook introduces principles through practice. Herbert Gintis exposes students to the techniques and applications of game theory through a wealth of sophisticated and surprisingly fun-to-solve problems involving human and animal behavior. The second edition includes solutions to the problems presented and information related to agent-based modeling. In addition, the textbook incorporates instruction in using mathematical software to solve complex problems. Game Theory Evolving is perfect for graduate and upper-level undergraduate economics students, and is a terrific introduction for ambitious do-it-yourselfers throughout the behavioral sciences. Revised and updated edition relevant for courses across disciplines Perfect for graduate and upper-level undergraduate economics courses Solutions to problems presented throughout Incorporates instruction in using computational software for complex problem solving Includes in-depth discussions of agent-based modeling