179 research outputs found

    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

    Intellectual Property as a Carrot for Innovators Using Game Theory to Show the Limits of the Argument

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    Policymakers all over the world claim: no innovation without protection. For more than a century, critics have objected that the case for intellectual property is far from clear. This paper uses a game theoretic model to organise the debate. It is possible to model innovation as a prisoner's dilemma between potential innovators, and to interpret intellectual property as a tool for making cooperation the equilibrium. However, this model rests on assumptions about cost and benefit that are unlikely to hold, or have even been shown to be wrong, in many empirically relevant situations. Moreover, even if the problem is indeed a prisoner's dilemma, in many situations intellectual property is an inappropriate cure. It sets incentives to race to be the first, or the last, to innovate, as the case may be. In equilibrium, the firms would have to randomise between investment and non-investment, which is unlikely to work out in practice. Frequently, firms would have to invent cooperatively, which proves difficult in larger industries.intellectual property, game theory

    Transitions to Democratic Constitutions in Ethnic Conflicts

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    Excerpt This article discusses the preconditions for settling ethnic conflict through a constitutional compromise: democracy. The focus is on the conditions for transition to democracy amidst intense ethnic strife. What factors facilitate transition to democracy and what factors are obstacles? It is assumed that the attitude of social groups to democracy is determined by their leaders\u27 rational calculations of the prospects of social, economical and political benefits. In other words, social groups have the capacity to formulate collective interests and act strategically to further them, and their leaders choose the alternative path of action with the highest expected benefits among those available. To extend the argument, I will first draw on some recent analysis in the rational choice literature on institutions. Second, I will analyse two very different contexts in which transitions to democracy were attempted, the events in Angola 1974-75 and in Zimbabwe in 1979-80. Rational choice theorists try to discover the meaning of rationality in different contexts, and the study of strategic choices and interaction of the six political elite groups in Angola and Zimbabwe, each with a core ethnic constituency, makes empirical probing and refining of the propositions of rational choice theory possible

    Fairness Emergence in Reputation Systems

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    Reputation systems have been used to support users in making decisions under uncertainty or risk that is due to the autonomous behavior of others. Research results support the conclusion that reputation systems can protect against exploitation by unfair users, and that they have an impact on the prices and income of users. This observation leads to another question: can reputation systems be used to assure or increase the fairness of resource distribution? This question has a high relevance in social situations where, due to the absence of established authorities or institutions, agents need to rely on mutual trust relations in order to increase fairness of distribution. This question can be formulated as a hypothesis: in reputation (or trust management) systems, fairness should be an emergent property. The notion of fairness can be precisely defined and investigated based on the theory of equity. In this paper, we investigate the Fairness Emergence hypothesis in reputation systems and prove that , under certain conditions, the hypothesis is valid for open and closed systems, even in unstable system states and in the presence of adversaries. Moreover, we investigate the sensitivity of Fairness Emergence and show that an improvement of the reputation system strengthens the emergence of fairness. Our results are confirmed using a trace-driven simulation from a large Internet auction site.Trust, Simulation, Fairness, Equity, Emergence, Reputation System

    Resistance to learning and the evolution of cooperation

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    In many evolutionary algorithms, crossover is the main operator used in generating new individuals from old ones. However, the usual mechanism for generating offsprings in spatially structured evolutionary games has to date been clonation. Here we study the effect of incorporating crossover on these models. Our framework is the spatial Continuous Prisoner's Dilemma. For this evolutionary game, it has been reported that occasional errors (mutations) in the clonal process can explain the emergence of cooperation from a non-cooperative initial state. First, we show that this only occurs for particular regimes of low costs of cooperation. Then, we display how crossover gets greater the range of scenarios where cooperative mutants can invade selfish populations. In a social context, where crossover involves a general rule of gradual learning, our results show that the less that is learnt in a single step, the larger the degree of global cooperation finally attained. In general, the effect of step-by-step learning can be more efficient for the evolution of cooperation than a full blast one.Evolutionary games, Continuous prisoner's dilemma, Spatially structured, Crossover, Learning

    Cooperation in Networked Populations of Selfish Adaptive Agents: Sensitivity to Learning Speed

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    This paper investigates the evolution of cooperation in iterated Prisoner's Dilemma (IPD) games with individually learning agents, subject to the structure of the interaction network. In particular, we study how Tit-for-Tat or All-Defection comes to dominate the population on Watts-Strogatz networks, under varying learning speeds and average network path lengths. We find that the presence of a cooperative regime (where almost the entire population plays Tit-for-Tat) is dependent on the quickness of information spreading across the network. More precisely, cooperation hinges on the relation between individual adaptation speed and average path length in the interaction topology. Our results are in good agreement with previous works both on discrete choice dynamics on networks and in the evolution of cooperation literature

    Resistance to learning and the evolution of cooperation

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    In many evolutionary algorithms, crossover is the main operator used in generating new individuals from old ones. However, the usual mechanism for generating offsprings in spatially structured evolutionary games has to date been clonation. Here we study the effect of incorporating crossover on these models. Our framework is the spatial Continuous Prisoner's Dilemma. For this evolutionary game, it has been reported that occasional errors (mutations) in the clonal process can explain the emergence of cooperation from a non-cooperative initial state. First, we show that this only occurs for particular regimes of low costs of cooperation. Then, we display how crossover gets greater the range of scenarios where cooperative mutants can invade selfish populations. In a social context, where crossover involves a general rule of gradual learning, our results show that the less that is learnt in a single step, the larger the degree of global cooperation finally attained. In general, the effect of step-by-step learning can be more efficient for the evolution of cooperation than a full blast one

    Slow Emergence of Cooperation for Win-Stay Lose-Shift on Trees

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    We consider a group of agents on a graph who repeatedly play the prisoner’s dilemma game against their neighbors. The players adapt their actions to the past behavior of their opponents by applying the win-stay lose-shift strategy. On a finite connected graph, it is easy to see that the system learns to cooperate by converging to the all-cooperate state in a finite time. We analyze the rate of convergence in terms of the size and structure of the graph. Dyer et al. (2002) showed that the system converges rapidly on the cycle, but that it takes a time exponential in the size of the graph to converge to cooperation on the complete graph. We show that the emergence of cooperation is exponentially slow in some expander graphs. More surprisingly, we show that it is also exponentially slow in bounded-degree trees, where many other dynamics are known to converge rapidly
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