By Bikas K. Chakrabarti, Anirban Chakraborti, Satya R. Chakravarty
The distribution of wealth and source of revenue isn't uniform, and philosophers and economists have attempted for years to appreciate the explanations and formulate treatments for such inequalities. This publication introduces the stylish and exciting kinetic trade types that physicists have constructed to take on those concerns. this is often the 1st monograph in econophysics focussed at the analyses and modelling of those distributions, and is perfect for physicists and economists. it's written in easy, lucid language, with lots of illustrations and in-depth analyses, making it appropriate for researchers new to this box in addition to really good readers. It explores the starting place of monetary inequality and examines the medical steps that may be taken to lessen this inequality sooner or later.
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Extra resources for Econophysics of Income and Wealth Distributions
14 The income distribution of Argentina in May 2002. Reproduced from Ferrero (2005). In such cases, the fitting using interpolating functions, such as above, would not be useful. One must also mention that many studies have found special features: (1) Ferrero (2005) studied the income distribution in Argentina during the economic crisis, which showed a time-dependent bimodal shape with two peaks (Fig. 14). (2) Coelho et al. (2008) examined wealth distributions and found the existence of two distinct power law regimes: the Pareto exponents of the super-rich (identified, for example, in rich lists such as that provided by Forbes) are smaller than the Pareto exponents obtained for top earners in income data sets.
1 Lotka–Volterra models The generalized Lotka-Volterra model for wealth distribution (Levy and Solomon 1997; Richmond and Solomon 2001; Solomon and Richmond 2002) is based on the redistribution of wealth of a total number N of agents in a society. 1) mi,t+1 = (1 + ξt )mi,t + N j j combines a multiplicative random process with an autocatalytic process. Here, mi,t represents the amount of money assigned to an agent i at time t. The random numbers ξt are chosen from a positive set which has a variance V .
2003) for the Gini coefficient G, and the open circles show the theoretical formula G = (1 + f )/2. Reproduced from Silva and Yakovenko (2005). 8) as shown by the upper curve in Fig. 21, which reasonably agrees with the 1983 data. 9) where the last term in Eq. 9) represents the vertical jump of the Lorenz curve at x = 1. At this point, a small percentage of population in the upper class actually accounts for a substantial fraction f of the total income. The lower curve in Fig. 21 shows that Eq.