Exchanges in complex networks: income and wealth distributions

TL;DR

This paper studies how wealth distribution among agents is influenced by the network structure of their interactions, revealing that scale-free networks produce power-law wealth distributions similar to real-world income data.

Contribution

It demonstrates the impact of network topology on wealth distribution, especially how scale-free networks lead to power-law tails, supported by simulations and empirical data comparison.

Findings

Scale-free networks generate power-law wealth distributions.

Wealth distribution aligns with empirical income data from Australia.

Network properties directly influence wealth inequality patterns.

Abstract

We investigate the wealth evolution in a system of agents that exchange wealth through a disordered network in presence of an additive stochastic Gaussian noise. We show that the resulting wealth distribution is shaped by the degree distribution of the underlying network and in particular we verify that scale free networks generate distributions with power-law tails in the high-income region. Numerical simulations of wealth exchanges performed on two different kind of networks show the inner relation between the wealth distribution and the network properties and confirm the agreement with a self-consistent solution. We show that empirical data for the income distribution in Australia are qualitatively well described by our theoretical predictions.