Adaptive networks of trading agents.
Multiagent models have been used in many contexts to study generic collective behavior. Similarly, complex networks have become very popular because of the diversity of growth rules giving rise to scale-free behavior. Here we study adaptive networks where the agents trade "wealth" when they are linked together while links can appear and disappear according to the wealth of the corresponding agents; thus the agents influence the network dynamics and vice versa. Our framework generalizes a multiagent model of Bouchaud and Mézard [Physica A 282, 536 (2000)], and leads to a steady state with fluctuating connectivities. The system spontaneously self-organizes into a critical state where the wealth distribution has a fat tail and the network is scale free; in addition, network heterogeneities lead to enhanced wealth condensation.
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Marian Smoluchowski Institute of Physics, Jagellonian University, Reymonta 4, t30-059 Krakow, Poland.
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Martin
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D000465:Algorithms; D001703:Biophysics / Q000379:methods; D003198:Computer Simulation; D008954:Models, Biological; D015233:Models, Statistical; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D013598:Systems Theory; D013997:Time Factors