54 research outputs found

    Evolutionary Tournament-Based Comparison of Learning and Non-Learning Algorithms for Iterated Games

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    Evolutionary tournaments have been used effectively as a tool for comparing game-playing algorithms. For instance, in the late 1970's, Axelrod organized tournaments to compare algorithms for playing the iterated prisoner's dilemma (PD) game. These tournaments capture the dynamics in a population of agents that periodically adopt relatively successful algorithms in the environment. While these tournaments have provided us with a better understanding of the relative merits of algorithms for iterated PD, our understanding is less clear about algorithms for playing iterated versions of arbitrary single-stage games in an environment of heterogeneous agents. While the Nash equilibrium solution concept has been used to recommend using Nash equilibrium strategies for rational players playing general-sum games, learning algorithms like fictitious play may be preferred for playing against sub-rational players. In this paper, we study the relative performance of learning and non-learning algorithms in an evolutionary tournament where agents periodically adopt relatively successful algorithms in the population. The tournament is played over a testbed composed of all possible structurally distinct 2×2 conflicted games with ordinal payoffs: a baseline, neutral testbed for comparing algorithms. Before analyzing results from the evolutionary tournament, we discuss the testbed, our choice of representative learning and non-learning algorithms and relative rankings of these algorithms in a round-robin competition. The results from the tournament highlight the advantage of learning algorithms over players using static equilibrium strategies for repeated plays of arbitrary single-stage games. The results are likely to be of more benefit compared to work on static analysis of equilibrium strategies for choosing decision procedures for open, adapting agent society consisting of a variety of competitors.Repeated Games, Evolution, Simulation

    Analyzing Social Network Structures in the Iterated Prisoner's Dilemma with Choice and Refusal

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    The Iterated Prisoner's Dilemma with Choice and Refusal (IPD/CR) is an extension of the Iterated Prisoner's Dilemma with evolution that allows players to choose and to refuse their game partners. From individual behaviors, behavioral population structures emerge. In this report, we examine one particular IPD/CR environment and document the social network methods used to identify population behaviors found within this complex adaptive system. In contrast to the standard homogeneous population of nice cooperators, we have also found metastable populations of mixed strategies within this environment. In particular, the social networks of interesting populations and their evolution are examined.Comment: 37 pages, uuencoded gzip'd Postscript (1.1Mb when gunzip'd) also available via WWW at http://www.cs.wisc.edu/~smucker/ipd-cr/ipd-cr.htm

    Simulating Evolutionary Games: A Python-Based Introduction

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    This paper is an introduction to agent-based simulation using the Python programming language. The core objective of the paper is to enable students, teachers, and researchers immediately to begin social-science simulation projects in a general purpose programming language. This objective is facilitated by design features of the Python programming language, which we very briefly discuss. The paper has a 'tutorial' component, in that it is enablement-focused and therefore strongly application-oriented. As our illustrative application, we choose a classic agent-based simulation model: the evolutionary iterated prisoner's dilemma. We show how to simulate the iterated prisoner's dilemma with code that is simple and readable yet flexible and easily extensible. Despite the simplicity of the code, it constitutes a useful and easily extended simulation toolkit. We offer three examples of this extensibility: we explore the classic result that topology matters for evolutionary outcomes, we show how player type evolution is affected by payoff cardinality, and we show that strategy evaluation procedures can affect strategy persistence. Social science students and instructors should find that this paper provides adequate background to immediately begin their own simulation projects. Social science researchers will additionally be able to compare the simplicity, readability, and extensibility of the Python code with comparable simulations in other languages.Agent-Based Simulation, Python, Prisoner's Dilemma

    Individual versus group success in round robin tournament of an ability-based two-player game

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    This thesis studies the development of cooperation and measures of success among self-interested agents in a defined ability-based two-player asymmetric game that is structured as a round-robin tournament. Our research is motivated by the notion that in many systems cooperative behaviour depends on some parameters that are usually not considered in existing research. These include: the balance between individual activity and interaction with others; the impact of agents' ability levels; and the need to maintain balance between individual and group performance. In this thesis, we examine all these issues by using a defined game-theoretic modelling and simulation framework. Our simulation experiments on six agent group compositions establish some patterns of how an agent's ability and strategy impact its individual and overall group performance. The results demonstrate that the design framework supports methodical comparative studies of strategy profiles with respect to specific individual and group performance measures.cooperative behaviourtwo-player asymmetric gametheoretic modelling and simulation framewor

    A Spatial Agent-Based Model of N-Person Prisoner's Dilemma Cooperation in a Socio-Geographic Community

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    The purpose of this paper is to present a spatial agent-based model of N-person prisoner's dilemma that is designed to simulate the collective communication and cooperation within a socio-geographic community. Based on a tight coupling of REPAST and a vector Geographic Information System, the model simulates the emergence of cooperation from the mobility behaviors and interaction strategies of citizen agents. To approximate human behavior, the agents are set as stochastic learning automata with Pavlovian personalities and attitudes. A review of the theory of the standard prisoner's dilemma, the iterated prisoner's dilemma, and the N-person prisoner's dilemma is given as well as an overview of the generic architecture of the agent-based model. The capabilities of the spatial N-person prisoner's dilemma component are demonstrated with several scenario simulation runs for varied initial cooperation percentages and mobility dynamics. Experimental results revealed that agent mobility and context preservation bring qualitatively different effects to the evolution of cooperative behavior in an analyzed spatial environment.Agent Based Modeling, Cooperation, Prisoners Dilemma, Spatial Interaction Model, Spatially Structured Social Dilemma, Geographic Information Systems

    Evolution of Reciprocal Cooperation in the Avatamsaka Game

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    The Avatamsaka game is investigated both analytically and using computer simulations. The Avatamsaka game is a dependent game in which each agent’s payoff depends completely not on her own decision but on the other players’. Consequently, any combination of mixed strategies is a Nash equilibrium.Analysis and evolutionary simulations show that the socially optimal state becomes evolutionarily stable by a Pavlovian strategy in the repeated Avatamsaka game, and also in any kind of dependent game. The mechanism of the evolutionary process is investigated from the viewpoint of the agent’s memory and mutation of strategies.Book SeriesLecture Notes in Economics and Mathematical SystemsThis BookThe Complex Networks of Economic InteractionsEssays in Agent-Based Economics and Econophysic
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