This Colloquium reviews statistical models for money, wealth, and income
distributions developed in the econophysics literature since the late 1990s. By
analogy with the Boltzmann-Gibbs distribution of energy in physics, it is shown
that the probability distribution of money is exponential for certain classes
of models with interacting economic agents. Alternative scenarios are also
reviewed. Data analysis of the empirical distributions of wealth and income
reveals a two-class distribution. The majority of the population belongs to the
lower class, characterized by the exponential ("thermal") distribution, whereas
a small fraction of the population in the upper class is characterized by the
power-law ("superthermal") distribution. The lower part is very stable,
stationary in time, whereas the upper part is highly dynamical and out of
equilibrium.Comment: 24 pages, 13 figures; v.2 - minor stylistic changes and updates of
references corresponding to the published versio
Complex economic nonlinear dynamics endogenously do not converge to a point, a limit cycle, or an explosion. Their study developed out of earlier studies of cybernetic, catastrophic, and chaotic systems. Complexity analysis stresses interactions among dispersed agents without a global controller, tangled hierarchies, adaptive learning, evolution, and novelty, and out-of-equilibrium dynamics. Complexity methods include interacting particle systems, self-organized criticality, and evolutionary game theory, to simulate artificial stock markets and other phenomena. Theoretically, bounded rationality replaces rational expectations. Complexity theory influences empirical methods and restructures policy debates.
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