40 research outputs found

    Individual variation evades the Prisoner's Dilemma

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    BACKGROUND: The Prisoner's Dilemma (PD) is a widely used paradigm to study cooperation in evolutionary biology, as well as in fields as diverse as moral philosophy, sociology, economics and politics. Players are typically assumed to have fixed payoffs for adopting certain strategies, which depend only on the strategy played by the opponent. However, fixed payoffs are not realistic in nature. Utility functions and the associated payoffs from pursuing certain strategies vary among members of a population with numerous factors. In biology such factors include size, age, social status and expected life span; in economics they include socio-economic status, personal preference and past experience; and in politics they include ideology, political interests and public support. Thus, no outcome is identical for any two different players. RESULTS: We show that relaxing the assumption of fixed payoffs leads to frequent violations of the payoff structure required for a Prisoner's Dilemma. With variance twice the payoff interval in a linear PD matrix, for example, only 16% of matrices are valid. CONCLUSIONS: A single player lacking a valid PD matrix destroys the conditions for a Prisoner's Dilemma, so between any two players, PD games themselves are fewer still (3% in this case). This may explain why the Prisoner's Dilemma has hardly been found in nature, despite the fact that it has served as a ubiquitous (and still instructive) model in studies of the evolution of cooperation

    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

    Learning and innovative elements of strategy adoption rules expand cooperative network topologies

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    Cooperation plays a key role in the evolution of complex systems. However, the level of cooperation extensively varies with the topology of agent networks in the widely used models of repeated games. Here we show that cooperation remains rather stable by applying the reinforcement learning strategy adoption rule, Q-learning on a variety of random, regular, small-word, scale-free and modular network models in repeated, multi-agent Prisoners Dilemma and Hawk-Dove games. Furthermore, we found that using the above model systems other long-term learning strategy adoption rules also promote cooperation, while introducing a low level of noise (as a model of innovation) to the strategy adoption rules makes the level of cooperation less dependent on the actual network topology. Our results demonstrate that long-term learning and random elements in the strategy adoption rules, when acting together, extend the range of network topologies enabling the development of cooperation at a wider range of costs and temptations. These results suggest that a balanced duo of learning and innovation may help to preserve cooperation during the re-organization of real-world networks, and may play a prominent role in the evolution of self-organizing, complex systems.Comment: 14 pages, 3 Figures + a Supplementary Material with 25 pages, 3 Tables, 12 Figures and 116 reference

    Exploring Hopes And Fears From Supply Chain Innovations: An Analysis Of Antecedents And Consequences Of Supply Chain Knowledge Exchanges

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    This dissertation sheds light on severalhopes and fears from supply chain innovation in three distinct papers. Paper one introduces the concept of Process Innovation Propagation as an appropriation technique helping to extract the most returns out of a process innovation by exporting to supply chain partners. Paper two devises and empirically tests knowledge properties that best lead to radical and incremental supply chain innovative capabilities. Lastly, paper three conducts an exploratory study that introduces factors affecting a firm’s optimum supply chain innovation strategy. The dissertation makes a strong argument that supply chain innovation is most prominently governed by power asymmetry that may either help or hurt innovative performance

    Synthesis of Strategies for Non-Zero-Sum Repeated Games

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    There are numerous applications that involve two or more self-interested autonomous agents that repeatedly interact with each other in order to achieve a goal or maximize their utilities. This dissertation focuses on the problem of how to identify and exploit useful structures in agents' behavior for the construction of good strategies for agents in multi-agent environments, particularly non-zero-sum repeated games. This dissertation makes four contributions to the study of this problem. First, this thesis describes a way to take a set of interaction traces produced by different pairs of players in a two-player repeated game, and then find the best way to combine them into a strategy. The strategy can then be incorporated into an existing agent, as an enhancement of the agent's original strategy. In cross-validated experiments involving 126 agents for the Iterated Prisoner's Dilemma, Iterated Chicken Game, and Iterated Battle of the Sexes, my technique was able to make improvement to the performance of nearly all of the agents. Second, this thesis investigates the issue of uncertainty about goals when a goal-based agent situated in a nondeterministic environment. The results of this investigation include the necessary and sufficiency conditions for such guarantee, and an algorithm for synthesizing a strategy from interaction traces that maximizes the probability of success of an agent even when no strategy can assure the success of the agent. Third, this thesis introduces a technique, Symbolic Noise Detection (SND), for detecting noise (i.e., mistakes or miscommunications) among agents in repeated games. The idea is that if we can build a model of the other agent's behavior, we can use this model to detect and correct actions that have been affected by noise. In the 20th Anniversary Iterated Prisoner's Dilemma competition, the SND agent placed third in the "noise" category, and was the best performer among programs that had no "slave" programs feeding points to them. Fourth, the thesis presents a generalization of SND that can be wrapped around any existing strategy. Finally, the thesis includes a general framework for synthesizing strategies from experience for repeated games in both noisy and noisy-free environments

    Noncooperative game theory for industrial organization : an introduction and overview

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    Essays on Bounded Rationality and Strategic Behavior in Experimental and Computational Economics.

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    Chapter 1 evaluates coordination among agents in environments with congestion effects. This paper discusses how people implicitly learn to coordinate their actions when such coordination is beneficial but difficult. During a series of experiments involving human subjects and simulated agents, subjects repeatedly update their strategies during play of the El Farol Bar Game. A subject is able to partially observe her opponents’ previous strategies and payoffs before setting her strategy for the next round of play. Play did not converge to the stage game’s pure strategy Nash equilibrium. Also, subjects routinely imitated the most successful strategies. This flocking behavior led to socially inefficient outcomes. Economic agents often face situations in which they must simultaneously interact in a variety of strategic environments, and yet they have only limited cognitive resources to compete in these varied settings. Chapters 2 through 4 consider how boundedly rational agents allocate scarce cognitive resources in strategic environments characterized by multiple simultaneously played games. Chapter 2 builds a framework that encapsulates a complex adaptive system defined by finite automaton strategies. Chapter 3 considers the evolution of strategies in the presence of cognitive costs in both single-game and multiple-game settings. When facing costs, a player’s strategy population quickly converges to a largely homogenous pool of rather simplified strategies that utilize only 14 percent of their cognitive power. There is evidence of both positive and negative strategic complementarities in the two game environments. Strategies perform better in each game within the two-game {Prisoner’s Dilemma, Stag Hunt} setting than they do when playing each game individually. Conversely, performance is impaired in each game of the two-game {Stag Hunt, Chicken} environment relative to the single game settings. Chapter 4 uses the evolved strategies to evaluate the impact of experience in multiple game environments. Experience in Prisoner’s Dilemma translates well into other games in two-game environments, while experience in Stag Hunt handicaps performance in other games. In multiple game settings, since a strategy’s actions are applied in different games, the context of actions is important. Chapters 3 and 4 address this issue by comparing the natural outcome context to the cooperate/defect context.Ph.D.EconomicsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/86359/1/leady_1.pd

    The Expressive Power of Adjudication

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    This article provides a causal explanation of adjudicative compliance that is distinct from both the court\u27s threat of sanctions and its institutional legitimacy. The new mechanism for compliance is the power of adjudicative expression. The theory of expressive adjudication arises from a previously neglected synergy among three expressive concepts in game theory -correlated equilibria, focal points, and signals. The article identifies the circumstances in which adjudicative expression can, by itself, influence the behavior of existing disputants and of future potential disputants. In each case, ambiguity in the relevant facts or the concepts underlying intentional and spontaneous order can cause a conflict that clarifying expression resolves. This expressive power explains otherwise puzzling instances of compliance with tribunals that lack the power of sanctions, and unifies theories of third-party norm enforcement with a theory of legal sanctions. Finally, the article examines certain normative implications of the expressive theory, including a novel function of adjudicative impartiality, a new justification for the system of public adjudication, and a trade-off between dispute resolution and dispute avoidance
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