2014
DOI: 10.1103/physreve.90.042808
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Intergroup information exchange drives cooperation in the public goods game

Abstract: In this manuscript we explore the onset of cooperative traits in the Public Goods game. This well-known game involves N -agent interactions and thus reproduces a large number of social scenarios in which cooperation appears to be essential. Many studies have recently addressed how the structure of the interaction patterns influences the emergence of cooperation. Here we study how information about the payoffs collected by each individual in the different groups it participates in, influences the decisions made… Show more

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Cited by 21 publications
(19 citation statements)
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References 35 publications
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“…The game can be implemented on structured populations, where players are placed on the nodes of a graph and interact through their links. The results usually show that, while in well-mixed populations cooperators extinguish quickly, repeated local interactions among the same players allow the formation of clusters of cooperators which are able to survive [70][71][72][73][74][75].…”
Section: Public Goods Game In the Multiplexmentioning
confidence: 99%
“…The game can be implemented on structured populations, where players are placed on the nodes of a graph and interact through their links. The results usually show that, while in well-mixed populations cooperators extinguish quickly, repeated local interactions among the same players allow the formation of clusters of cooperators which are able to survive [70][71][72][73][74][75].…”
Section: Public Goods Game In the Multiplexmentioning
confidence: 99%
“…On the practical side, the study of this topic contributes to the understanding of behaviors of epidemics, while on the theoretical side, it provides a simple dynamical framework to demonstrate rich phase diagrams for analysis. Several mathematical models have been proposed to describe common infectious diseases, including two-state SIS model and threestate SIR model with S standing for susceptible, I for infected, and R for recovery in the epidemiological terminology [3,4]. A variety of methods have been developed to analyze the epidemic spreading processes, including generating function [5,6], pair-approximation [7], heterogeneous mean field theory [8][9][10], probability generating function [11,12], and branching process approximation [13,14].…”
Section: Introductionmentioning
confidence: 99%
“…Many models, such as DeGroot model [4], Sznajd model [5], discrete CODA model [6], Hegselmann-Krause model [7], generalized Glauber models [8], Deffuant model [9], have been developed to simulate interaction mechanisms of opinions. In recent years, game theory, which provides a useful framework to build foundational models to mimick the interactions among agents with conflict of interests in many disciplines [10][11][12][13][14][15][16][17][18][19], has also been used in these fields to help understand opinion formation and evolution. With respect to opinion formation, a lot of game models have been proposed or used in previous studies [20][21][22][23][24][25].…”
Section: Introductionmentioning
confidence: 99%