Power-law exponent of the Bouchaud-Mézard model on regular random network

Abstract

We study the Bouchaud-Mézard model on a regular random network. By assuming adiabaticity and independency, and utilizing the generalized central limit theorem and the Tauberian theorem, we derive an equation that determines the exponent of the probability distribution function of the wealth as $x\to \infty$. The analysis shows that the exponent can be smaller than 2, while a mean-field analysis always gives the exponent as being larger than 2. The results of our analysis are shown to be good agreement with those of the numerical simulations.

To be pubished in Phys. Rev. E

References in corpus (4)

• Distribution Of Wealth In A Network Model Of The Economy
• Bouchaud-Mézard model on a random network
• Wealth distribution on complex networks
• Breakdown of the mean-field approximation in a wealth distribution model

Cited by in corpus (2)

• On growth-optimal tax rates and the issue of wealth inequalities
• Anomaly, class division, and decoupling in income dynamics