The paper presented a manifesto of the new field, arguing that “behavior of large numbers of humans (as measured, e.g., by economic indices) might conform to analogs of the scaling laws that have proved useful in describing systems composed of large numbers of inanimate objects” (Stanley et al., 1996).
Econophysics does not literally apply the laws of physics, such as Newton’s laws or quantum mechanics, to humans. It uses mathematical methods developed in statistical physics to study statistical properties of complex economic systems consisting of a
large number of humans. As such, it may be considered
as a branch of applied theory of probabilities. However,
statistical physics is distinctly different from mathematical statistics in its focus, methods, and results.
Some physicists and economists attempted to connect
the two disciplines during the twentieth century. Frederick
Soddy (1933), the Nobel Prize winner in chemistry for his work on radioactivity, published the book Wealth,
Virtual Wealth and Debt, where he argued that the real
wealth is derived from the energy use in transforming raw
materials into goods and services, and not from monetary
transactions.
He also warned about dangers of excessive
debt and related “virtual wealth”, thus anticipating
the Great Depression. His ideas were largely ignored at
the time, but resonate today (Defilla, 2007).
The statistical physicist Elliott Montroll coauthored
the book Introduction to Quantitative Aspects
of Social Phenomena (Montroll and Badger, 1974). Several economists (Blume, 1993; Durlauf, 1997; Foley, 1994; Follmer, 1974) applied statistical physics to economic problems. The mathematicians Farjoun and Machover (1983) argued that many paradoxes in classical political economy can be resolved if one adopts a probabilistic approach.
When modern econophysics started in the middle of
1990s, its attention was primarily focused on analysis of
financial markets. Soon after, another direction, closer
to economics than finance, has emerged. It studies the
probability distributions of money, wealth, and income
in a society and overlaps with the long-standing line of
research in economics studying inequality in a society
Econophysics does not literally apply the laws of physics, such as Newton’s laws or quantum mechanics, to humans. It uses mathematical methods developed in statistical physics to study statistical properties of complex economic systems consisting of a
large number of humans. As such, it may be considered
as a branch of applied theory of probabilities. However,
statistical physics is distinctly different from mathematical statistics in its focus, methods, and results.
Some physicists and economists attempted to connect
the two disciplines during the twentieth century. Frederick
Soddy (1933), the Nobel Prize winner in chemistry for his work on radioactivity, published the book Wealth,
Virtual Wealth and Debt, where he argued that the real
wealth is derived from the energy use in transforming raw
materials into goods and services, and not from monetary
transactions.
He also warned about dangers of excessive
debt and related “virtual wealth”, thus anticipating
the Great Depression. His ideas were largely ignored at
the time, but resonate today (Defilla, 2007).
The statistical physicist Elliott Montroll coauthored
the book Introduction to Quantitative Aspects
of Social Phenomena (Montroll and Badger, 1974). Several economists (Blume, 1993; Durlauf, 1997; Foley, 1994; Follmer, 1974) applied statistical physics to economic problems. The mathematicians Farjoun and Machover (1983) argued that many paradoxes in classical political economy can be resolved if one adopts a probabilistic approach.
When modern econophysics started in the middle of
1990s, its attention was primarily focused on analysis of
financial markets. Soon after, another direction, closer
to economics than finance, has emerged. It studies the
probability distributions of money, wealth, and income
in a society and overlaps with the long-standing line of
research in economics studying inequality in a society
- Model 1: video file animation-1.avi, 1.4 MB
In each transaction, Dm is selected to be a random fraction of $1000, which is the average amount of money per agent in the system. The simulation is performed with 5000 agents. The histogram shown in the animation is obtained by averaging the histograms of 10 runs of simulations to produce a smoother distribution. - Model 2: video file animation-2.avi, 2.6 MB
In this model, Dm=$1 has the same value for all transactions. To speed up convergence, each agent is initially given the smaller amount $10. The simulations are performed with 500 agents and repeated 1000 times. The histogram shown in the animation is obtained by adding the histograms of all runs. Because Dm is small, the money distribution evolves in a diffusive manner. The initial distribution first broadens into a symmetric, Gaussian curve. Then, probability starts to accumulate around m=0, which acts as the impenetrable boundary, because money balances of agents cannot go below zero. As a result, the probability distribution acquires a skewed (asymmetric) exponential shape.
Source: Colloquium: Statistical mechanics of money, wealth, and income Victor M. Yakovenko
Department of Physics, University of Maryland, College Park, Maryland 20742-4111, USA J. Barkley Rosser, Jr.
Department of Economics, James Madison University, Harrisonburg, Virginia 22807, USA (Dated: 24 December 2009)
Department of Physics, University of Maryland, College Park, Maryland 20742-4111, USA J. Barkley Rosser, Jr.
Department of Economics, James Madison University, Harrisonburg, Virginia 22807, USA (Dated: 24 December 2009)