Properties of wealth distribution in multi-agent systems of a complex network (original) (raw)
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Wealth distribution on a dynamic complex network
arXiv (Cornell University), 2023
We present an agent-based model of microscopic wealth exchange in a dynamic network to study the topological features associated with economic inequality. The model evolves through two alternating processes, the conservative exchange of wealth between connected agents and the rewiring of connections, which depends on the wealth of the agents. The two dynamics are interrelated; from the dynamics of wealth a complex network emerges and the network in turn dictates who interacts with whom. We study the time evolution and the economic and topological asymptotic characteristics of the model for different values of a social protection factor f , which favors the poorest agent in each wealth transaction. In the case of f = 0, our results show condensation of wealth and connections in a few agents, in accordance with the mean field models with respect to wealth. Low values of f favor agents from the middle and upper classes, leading to the formation of hubs in the network. As f increases, the network restriction on exchanges gives rise to an egalitarian society different from the results outside the midfield network.
Exchanges in complex networks: income and wealth distributions
2003
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.
2017
Wealth distribution studies have been reported for almost 200 years using different models to explain the dynamics involved. Also, many kinds of approaches have arisen to fit the registered data. Pareto ́s distribution emerged as one of the best empirical model showing a good fitting with real data sets of wealth distribution all over the world and over different time ages. Theoretical models validate their assessments through this distribution. Souma asserted that wealth distribution interaction between agents could be modeled in a small-world network with different rules of wealth exchange. Garlaschelli, found that long-term shape of the empirical distribution strongly depends on the topology of the transaction networks among economic units. In the present work, an evolutionary game theory method was used to establish wealth exchange between economical agents structured in a small world network. The present project constructs a model based on México ́s population income data sets ...
A model of distribution of the wealth in a society based on the properties of complex networks has been proposed. The wealth is interpreted as a consequence of communication possibilities and proportional to the number of connections possessed by a person (as a vertex of the social network). Numerical simulation of wealth distribution shows a transition from the Pareto law to distribution with a gap demonstrating the absence of the middle class. Such a transition has been described as a second-order phase transition, the order parameter has been introduced and the value of the critical exponent has been found.
Wealth distribution on complex networks
Physical Review E, 2012
We study the wealth distribution of the Bouchaud-Mézard model on complex networks. It is known from numerical simulations that this distribution depends on the topology of the network; however, no one has succeeded in explaining it. Using "adiabatic" and "independent" assumptions along with the central-limit theorem, we derive equations that determine the probability distribution function. The results are compared to those of simulations for various networks. We find good agreement between our theory and the simulations, except for the case of Watts-Strogatz networks with a low rewiring rate due to the breakdown of independent assumption.
A family-network model for wealth distribution in societies
Physica A-statistical Mechanics and Its Applications, 2005
A model based on first-degree family relations network is used to describe the wealth distribution in societies. The network structure is not a priori introduced in the model, it is generated in parallel with the wealth values through simple and realistic dynamical rules. The model has two main parameters, governing the wealth exchange in the network. Choosing their values realistically, leads to wealth distributions in good agreement with measured data. The cumulative wealth distribution function has an exponential behavior in the low and medium wealth limit, and shows the Pareto-like power-law tail for the upper 5% of the society. The obtained Pareto indexes are in good agreement with the measured ones. The generated family networks also converge to a statistically stable topology with a simple Poissonian degree distribution. On this family network many interesting correlations are studied, and the main factors leading to wealth diversification and the formation of the Pareto law are identified. r
Socioeconomic networks with long-range interactions
Physical Review E, 2008
We study a modified version of a model previously proposed by Jackson and Wolinsky to account for communication of information and allocation of goods in socioeconomic networks. In the model, the utility function of each node is given by a weighted sum of contributions from all accessible nodes. The weights, parametrized by the variable ␦, decrease with distance. We introduce a growth mechanism where new nodes attach to the existing network preferentially by utility. By increasing ␦, the network structure evolves from a power-law to an exponential degree distribution, passing through a regime characterized by shorter average path length, lower degree assortativity, and higher central point dominance. In the second part of the paper we compare different network structures in terms of the average utility received by each node. We show that power-law networks provide higher average utility than Poisson random networks. This provides a possible justification for the ubiquitousness of scale-free networks in the real world.
Network Structures and Poverty Traps
Dynamic Games and Applications, 2018
We build an evolutionary network game of economic agents that choose actions of being either a high-profile or a low-profile economic agent. Those economic agents reside in the vertices of an undirected graph or network given by their types, and their strategic interaction is driven by imitative behavior. Then, the share of types of economic agents forms networks described by a mean field formalism which depends on agents' payoff functions, as well as on the current state of the economic network. We show the fact that, in this context of networks, a neighbor is imitated if her strategy outperformed the focal individual's in the previous iterations. The main result is that there are three equilibria (each with a nondegenerate basin of attraction), one completely made up of high-profile individuals, one made up of low-profile individuals (i.e., the poverty trap), and a mixture. The main parameters from being in one or the other equilibrium are: (i) the degree of node, (ii) cost of being high-profile, and (iii) initial distribution of types. We conclude with simple numerical examples to show that outcome depends on network structures and on both the education costs, c, and the value of β which is the incentive to choose the high-profile action.
2020
Societies rely on individual contributions to sustain public goods that benefit the entire community. Several mechanisms, that specify how individuals change their decisions based on past experiences, have been proposed to explain how altruists are not outcompeted by selfish counterparts. A key aspect of such strategy updates involves a comparison of an individual's latest payoff with that of a random neighbour. In reality, both the economic and social milieu often shapes cooperative behaviour. We propose a new decision heuristic, where the propensity of an individual to cooperate depends on the local strategy environment in which she is embedded as well as her wealth relative to that of her neighbours. Our decision-making model allows cooperation to be sustained and also explains the results of recent experiments on social dilemmas in dynamic networks. Final cooperation levels depend only on the extent to which the strategy environment influences altruistic behaviour but are la...