Money Distributions in Chaotic Economies

This paper considers the ideal gas-like model of trading markets, where each individual is identified as a gas molecule that interacts with others trading in elastic or money-conservative collisions. Traditionally this model introduces different rules of random selection and exchange between pair agents. Real economic transactions are complex but obviously non-random. Consequently, unlike this traditional model, this work implements chaotic elements in the evolution of an economic system. In particular, we use a chaotic signal that breaks the natural pairing symmetry $(i,j)\Leftrightarrow(j,i)$ of a random gas-like model. As a result of that, it is found that a chaotic market like this can reproduce the referenced wealth distributions observed in real economies (the Gamma, Exponential and Pareto distributions).

A Chaotic Approach to Market Dynamics

Economy is demanding new models, able to understand and predict the evolution of markets. To this respect, Econophysics is offering models of markets as complex systems, such as the gas-like model, able to predict money distributions observed in real economies. However, this model reveals some technical hitches to explain the power law (Pareto) distribution, observed in individuals with high incomes. Here, non linear dynamics is introduced in the gas-like model. The results obtained demonstrate that a chaotic gas-like model can reproduce the two money distributions observed in real economies (Exponential and Pareto). Moreover, it is able to control the transition between them. This may give some insight of the micro-level causes that originate unfair distributions of money in a global society. Ultimately, the chaotic model makes obvious the inherent instability of asymmetric scenarios, where sinks of wealth appear in the market and doom it to complete inequality.

Application of Chaotic Number Generators in Econophysics

Agent-based models have demonstrated their power and flexibility in Econophysics. However their major challenge is still to devise more realistic simulation scenarios. The complexity of Economy makes appealing the idea of introducing chaotic number generators as simulation engines in these models. Chaos based number generators are easy to use and highly configurable. This makes them just perfect for this application.