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

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

Evolutionary instability of Zero Determinant strategies demonstrates that winning isn't everything

Zero Determinant (ZD) strategies are a new class of probabilistic and conditional strategies that are able to unilaterally set the expected payoff of an opponent in iterated plays of the Prisoner's Dilemma irrespective of the opponent's strategy, or else to set the ratio between a ZD player's and their opponent's expected payoff. Here we show that while ZD strategies are weakly dominant, they are not evolutionarily stable and will instead evolve into less coercive strategies. We show that ZD strategies with an informational advantage over other players that allows them to recognize other ZD strategies can be evolutionarily stable (and able to exploit other players). However, such an advantage is bound to be short-lived as opposing strategies evolve to counteract the recognition.Comment: 14 pages, 4 figures. Change in title (again!) to comply with Nature Communications requirements. To appear in Nature Communication

Evolutionary Markovian Strategies in 2 x 2 Spatial Games

Evolutionary spatial 2 x 2 games between heterogeneous agents are analyzed using different variants of cellular automata (CA). Agents play repeatedly against their nearest neighbors 2 x 2 games specified by a rescaled payoff matrix with two parameteres. Each agent is governed by a binary Markovian strategy (BMS) specified by 4 conditional probabilities [p_R, p_S, p_T, p_P] that take values 0 or 1. The initial configuration consists in a random assignment of "strategists" among the 2^4= 16 possible BMS. The system then evolves within strategy space according to the simple standard rule: each agent copies the strategy of the neighbor who got the highest payoff. Besides on the payoff matrix, the dominant strategy -and the degree of cooperation- depend on i) the type of the neighborhood (von Neumann or Moore); ii) the way the cooperation state is actualized (deterministically or stochastichally); and iii) the amount of noise measured by a parameter epsilon. However a robust winner strategy is [1,0,1,1].Comment: 18 pages, 8 figures (7 of these figures contain 4 encapsulapted poscript files each

Probabilistic memory-one strategies to dominate the iterated prisoner’s dilemma over networks

Financiado para publicación en acceso aberto: Universidade de Vigo/CISUGThe Iterated Prisoner’s Dilemma (IPD) has been a classical game theoretical scenario used to model behaviour interactions among agents. From the famous Axelrod’s tournament, and the successful results obtained by the Tit for Tat strategy, to the introduction of the zerodeterminant strategies in the last decade, the game theory community has been exploring the performance of multiple strategies for years. This article grounds on such previous work, studying probabilistic memory-one strategies (PMO) and using evolutionary game theory, to analyse the criteria to find the most successful set of strategies in networked topologies. The results are nearly deterministic in discrete PMO scenarios. However, results become much more complex when moving to continuous ones, and there is no optimal strategy for a given scenario. Finally, this article describes how, using machine learning and evolutionary techniques; a cluster of agents, playing synchronously and adaptively, is able to dominate the rest of the populatio

Iterated Prisoner\u27s Dilemma for Species

The Iterated Prisoner\u27s Dilemma (IPD) is widely used to study the evolution of cooperation between self-interested agents. Existing work asks how genes that code for cooperation arise and spread through a single-species population of IPD playing agents. In this paper, we focus on competition between different species of agents. Making this distinction allows us to separate and examine macroevolutionary phenomena. We illustrate with some species-level simulation experiments with agents that use well-known strategies, and with species of agents that use team strategies

Cooperation in the iterated prisoner's dilemma is learned by operant conditioning mechanisms

The prisoner's dilemma (PD) is the leading metaphor for the evolution of cooperative behavior in populations of selfish agents. Although cooperation in the iterated prisoner's dilemma (IPD) has been studied for over twenty years, most of this research has been focused on strategies that involve nonlearned behavior. Another approach is to suppose that players' selection of the preferred reply might he enforced in the same way as feeding animals track the best way to feed in changing nonstationary environments. Learning mechanisms such as operant conditioning enable animals to acquire relevant characteristics of their environment in order to get reinforcements and to avoid punishments. In this study, the role of operant conditioning in the learning of cooperation was evaluated in the PD. We found that operant mechanisms allow the learning of IPD play against other strategies. When random moves are allowed in the game, the operant learning model showed low sensitivity. On the basis of this evidence, it is suggested that operant learning might be involved in reciprocal altruism.Fil: Gutnisky, D. A.. Universidad de Buenos Aires. Facultad de Ingenieria. Instituto de Ingeniería Biomédica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Biología y Medicina Experimental. Fundación de Instituto de Biología y Medicina Experimental. Instituto de Biología y Medicina Experimental; ArgentinaFil: Zanutto, Bonifacio Silvano. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Biología y Medicina Experimental. Fundación de Instituto de Biología y Medicina Experimental. Instituto de Biología y Medicina Experimental; Argentina. Universidad de Buenos Aires. Facultad de Ingenieria. Instituto de Ingeniería Biomédica; Argentin

Agent Based Modeling of Electronic Markets To Analyze the Sustainability of Mutual Cooperation

According to game theory, dominant strategy of Prisoner’s Dilemma game is defecting. Since online trading between two strangers falls in the realm of a Prisoner’s Dilemma, failure should be the only logical conclusion of such electronic commerce situation. Traders are less likely to cooperate since they are not required to deal with the same buyer again given the enormous population of online traders. Thus one could argue that markets like eBay should never exist. Then what is the reason behind resounding success of such electronic markets? The answer lies in the reputation system that they established. Google page rankings and reader reviews on various sites like Amazon.com and Epinions.com are some other examples of the reputation system pointing towards the enormous influence that they wield in our everyday life. This research thus attempts to analyze importance of reputation system in an online trading using Agent Based Simulation. The effect of reputation system on the sustainability of mutual cooperation between online traders is analyzed by taking into account key factors like level of gullibility of online traders and the weight of influence given to their past behavior. Results indicate that reputation system is certainly capable of sustaining cooperation among online traders. However, weightage given to the past behavior of agents should be decided only after taking into account the level of gullibility of agents. Use of agent based modeling for studying dynamics of online trading is a relatively new concept. Sustaining cooperation among online traders is often perceived to be difficult task. The reputation system proposed in this research is expected to make contribution towards this goal

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

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

Game theoretic modeling and analysis : A co-evolutionary, agent-based approach

Ph.DDOCTOR OF PHILOSOPH