Victor M. Yakovenko
posted by Matúš Medo
(3 May 2012)
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(246 views, 174 downloads, 1 comments )
This Chapter is written for the Festschrift celebrating the 70th birthday of
the distinguished economist Duncan Foley from the New School for Social
Research in New York. This Chapter reviews applications of statistical physics
methods, such as the principle of entropy maximization, to the probability
distributions of money, income, and global energy consumption per capita. The
exponential probability distribution of wages, predicted by the statistical
equilibrium theory of a labor market developed by Foley in 1996, is supported
by empirical data on income distribution in the USA for the majority (about
97%) of population. In addition, the upper tail of income distribution (about
3% of population) follows a power law and expands dramatically during financial
bubbles, which results in a significant increase of the overall income
inequality. A mathematical analysis of the empirical data clearly demonstrates
the two-class structure of a society, as pointed out Karl Marx and recently
highlighted by the Occupy Movement. Empirical data for the energy consumption
per capita around the world are close to an exponential distribution, which can
be also explained by the entropy maximization principle.
The Econophysics Forum
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I might add that the "entropic origin" of almost all economic variables predicated by uncertainty is rooted in Gibbs measure. That was proven in a paper entitled "The Source of Uncertainty for Probabilistic Preferences Over Gambles" posted on this Forum and made available at http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1971954 More recent, another paper posted on this Forum entitled "A Confidence Representation Theorem for Ambiguity Aversion with Applications to Financial Markets and Trade Algorithm" used a behavioural random integral operator constructed there, and Karhunen-Loeve (KL)type expansion (the probabilistic nature of the random integral equation required modifications to the standard proof for KL expansion) , to show that confidence is ergodic. To the extent that confidence "controls" response to economic or any decision variable for that matter, is the extent to which we see the Gibbs measure as an underlying theme. The probability distribution is generated by people's prior belief and the configurations about the future they formulate in their mind.