Wealth distribution models with regulations: Dynamics and equilibria

Abstract

Simple agent based exchange models are a commonplace in the study of wealth distribution in an artificial economy. Generally, in a system that is composed of many agents characterized by their wealth and risk-aversion factor, two agents are selected sequentially and randomly to exchange wealth, allowing for its redistribution. Here we analyze how the effect of a social protection policy, which favors agents of lower wealth during the exchange, influences stability and some relevant economic indicators of the system. On the other hand, we study how periods of interruption of these policies produce, in the short and long term, changes in the system. In most cases, a steady state is reached, but with varying relaxation times. We conclude that regulations may improve economic mobility and reduce inequality. Moreover, our results indicate that the removal of social protection entails a high cost associated with the hysteresis of the distribution of wealth. Economic inequalities increase during a period without social protection, but also they remain high for an even longer time and, in some extreme cases, inequality may be irreversible, indicating that the withdrawal of social protection yields a high cost associated with the hysteresis of the distribution of wealth.

Lattice–Gas–Automaton Modeling of Income Distribution

“…In fact, the Boltzmann–Gibbs function is usually reasonable for the low and middle ranges of income distribution [ 14 ], while the Pareto function provides a good fit to the high range [ 15 ]. This is because the process of lower wealth accumulation is additive, causing a Gaussian-like distribution, while the wealth in the high class grows in a multiplicative way, generating a power-law tail [ 54 ]. To obtain more satisfactory simulation results, further research will be conducted.…”

Wealth Distribution in Villages. Transition From Socialism to Capitalism in View of Exhaustive Wealth Data and a Master Equation Approach

“…Modeling the experimental data is done by many different methods, ranging from simple mean-field type models, to more complex models involving network-science approach or agent based computational simulations [11,[15][16][17][18][19][20][21][22][23][24][25][26][27][28][29].…”

Equal opportunities lead to maximum wealth inequality

“…However, an efficient market prevents the arbitrage opportunities, i.e., the expected growth rate of individual wealth is the same for all agentes. Therefore, there exists a positive constant α t such that [4][5][6][7][8][9][10][11][12][13][14][15]…”

“…First, restricting exchanges between agents linked by a static [14] or dynamic [13,15] network. Second, introducing agent specific parameters λ i [12,17] such that…”