Simple model for the dynamics of correlations in the evolution of economic entities under varying economic conditions

Ausloos, Marcel; Clippe, P.; Pękalski, Andrzej

doi:10.1016/s0378-4371(02)01844-7

Cited by 21 publications

(51 citation statements)

References 8 publications

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“…where x i t ≥ 0 gives the state of the agent i at discrete time t, and it may denote the wealth of this agent; the factor r i x i t expresses the self-growth capacity of agent i, characterized by a parameter r i ; Ψ i t represents the local field acting at the site i at time t; and a i measures the coupling of agent i with its neighborhood; it can also be interpreted as the local environmental pressure exerted on agent i [13]. The negative exponential function acts as a control factor that limits this growth with respect to the local field.…”

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confidence: 99%

Pareto and Boltzmann–Gibbs behaviors in a deterministic multi-agent system

Gonzalez-Estevez

Cosenza

López‐Ruiz

et al. 2008

Physica A: Statistical Mechanics and its Applications

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A deterministic system of interacting agents is considered as a model for economic dynamics. The dynamics of the system is described by a coupled map lattice with near neighbor interactions. The evolution of each agent results from the competition between two factors: the agent's own tendency to grow and the environmental influence that moderates this growth. Depending on the values of the parameters that control these factors, the system can display Pareto or Boltzmann-Gibbs statistical behaviors in its asymptotic dynamical regime. The regions where these behaviors appear are calculated on the space of parameters of the system. Other statistical properties, such as the mean wealth, the standard deviation, and the Gini coefficient characterizing the degree of equity in the wealth distribution are also calculated on the space of parameters of the system.

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confidence: 99%

Pareto and Boltzmann–Gibbs behaviors in a deterministic multi-agent system

Gonzalez-Estevez

Cosenza

López‐Ruiz

et al. 2008

Physica A: Statistical Mechanics and its Applications

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“…The parameter r i represents the capacity of the agent to become richer and the parameter a i describes the local selection pressure [1]. This means that the largest possibility of growth for the agent is obtained when x i ≃ a i Ψ t i , i.e., when the agent has reached some kind of adaptation to the local environment.…”

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confidence: 99%

A model of coupled maps for economic dynamics

Sánchez

Gonzalez-Estevez

López‐Ruiz

et al. 2007

Eur. Phys. J. Spec. Top.

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An array system of coupled maps is proposed as a model for economy evolution. The local dynamics of each map or agent is controlled by two parameters. One of them represents the growth capacity of the agent and the other one is a control term representing the local environmental pressure which avoids an exponential growth. The asymptotic state of the system evolution displays a complex behavior. The distribution of the maps values in this final regime is of power law type. In the model, inequality emerges as a result of the dynamical processes taking place in the microscopic scales.Key words: coupled-maps models, non-equilibrium systems, power law scaling Inequality in the richness distribution is a fact in each economic activity. The origin of such behavior seems to be caused by the interaction of the macro with the microeconomy. Here we propose a simple spatio-temporal model for economy evolution where inequality emerges as a result of the dynamical processes taking only place on the microscopic scale. That is, the microeconomy fully determines the macroeconomic characteristics of the system. The model is composed by N interacting agents representing a company, country or other economic entity. Each agent, identified by an index i = 1 · · · N, is characterized by a real, scalar degree of freedom, x i ∈ [0, ∞] denoting the strength, wealth or richness of the agent. The system evolves in time t synchronously. Each agent updates its x t i value according to its present state and

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“…The model was set in the form of a Monte Carlo simulation. Notice that the ACP model [3,4,5] contains among its variants an adaptation of the Bak -Sneppen model and was built in order to answer economy questions 3 . The model consists of 1. space -a square symmetry lattice, 2. companies, which are initially randomly placed on the lattice, in an 3. environment characterised by a real field F ∈ [0, 1] and a selection pressure sel, 4. each company (i) is characterised by one real parameter f i ∈ [0, 1] (so called its fitness).…”

Section: Acp Modelmentioning

confidence: 99%

Influence of Information Flow in the Formation of Economic Cycles

Miśkiewicz

Ausloos

Understanding Complex Systems

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Simple model for the dynamics of correlations in the evolution of economic entities under varying economic conditions

Cited by 21 publications

References 8 publications

Pareto and Boltzmann–Gibbs behaviors in a deterministic multi-agent system

Pareto and Boltzmann–Gibbs behaviors in a deterministic multi-agent system

A model of coupled maps for economic dynamics

Influence of Information Flow in the Formation of Economic Cycles

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