IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v367y2006icp388-414.html
   My bibliography  Save this article

The Inequality Process as a wealth maximizing process

Author

Listed:
  • Angle, John

Abstract

The One Parameter Inequality Process (OPIP) long predates the Saved Wealth Model (SWM) to which it is isomorphic up to the different choice of stochastic driver of wealth exchange. Both are stochastic interacting particle systems intended to model wealth and income distribution. The OPIP and other versions of the Inequality Process explain many aspects of wealth and income distribution but have gone undiscussed in econophysics. The OPIP is a jump process with a discrete 0,1 uniform random variate driving the exchange of wealth between two particles, while the SWM, as an extension of the stochastic version of the ideal gas model, is driven by a continuous uniform random variate with support at [0.0,1.0]. The OPIP's stationary distribution is a Lévy stable distribution attracted to the Pareto pdf near the (hot) upper bound of the OPIP's parameter, ω, and attracted to the normal (Gaussian) pdf toward the (cool) lower bound of ω. A gamma pdf model approximating the OPIP's stationary distribution is heuristically derived from the solution of the OPIP. The approximation works for ω<.5, better as ω→0. The gamma pdf model has parameters in terms of ω. The Inequality Process with Distributed Omega (IPDO) is a generalization of the OPIP. In the IPDO each particle can have a unique value of its parameter, i.e., particle i has ωi. The meta-model of the Inequality Process implies that smaller ω is associated with higher skill level among workers. This hypothesis is confirmed in a test of the IPDO. Particle wealth gain or loss in the OPIP and IPDO is more clearly asymmetric than in the SWM (λ≠0). Time-reversal asymmetry follows from asymmetry of gain and loss. While the IPDO scatters wealth, it also transfers wealth from particles with larger ω to those with smaller ω, particles that according to the IPDO's meta-model are more productive of wealth, nourishing wealth production. The smaller the harmonic mean of the ωi's in the IPDO population of particles, the more wealth is concentrated in particles with smaller ω, the less noise and the more ω signal there is in particle wealth, and the deeper the time horizon of the process. The IPDO wealth concentration mechanism is simpler than Maxwell's Demon.

Suggested Citation

  • Angle, John, 2006. "The Inequality Process as a wealth maximizing process," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 367(C), pages 388-414.
    • Handle: RePEc:eee:phsmap:v:367:y:2006:i:c:p:388-414
    DOI: 10.1016/j.physa.2005.11.017
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437105011933
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2005.11.017?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    1. Salem, A B Z & Mount, T D, 1974. "A Convenient Descriptive Model of Income Distribution: The Gamma Density," Econometrica, Econometric Society, vol. 42(6), pages 1115-1127, November.
    2. Arnab Chatterjee & Bikas K. Chakrabarti & Robin B. Stinchcombe, 2005. "Master equation for a kinetic model of trading market and its analytic solution," Papers cond-mat/0501413, arXiv.org, revised Aug 2005.
    3. A. F. Shorrocks, 1975. "On Stochastic Models of Size Distributions," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 42(4), pages 631-641.
    4. Daniel H. Weinberg, 1999. "Fifty Years of U.S. Income Data from the Current Population Survey: Alternatives, Trends, and Quality," American Economic Review, American Economic Association, vol. 89(2), pages 18-22, May.
    5. Anirban Chakraborti & Bikas K. Chakrabarti, 2000. "Statistical mechanics of money: How saving propensity affects its distribution," Papers cond-mat/0004256, arXiv.org, revised Jun 2000.
    6. Chatterjee, Arnab & K. Chakrabarti, Bikas & Manna, S.S, 2004. "Pareto law in a kinetic model of market with random saving propensity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 335(1), pages 155-163.
    7. Marco Patriarca & Anirban Chakraborti & Kimmo Kaski & Guido Germano, 2005. "Kinetic theory models for the distribution of wealth: power law from overlap of exponentials," Papers physics/0504153, arXiv.org, revised May 2005.
    8. Arnab Chatterjee & Bikas K. Chakrabarti & S. S. Manna, 2003. "Pareto Law in a Kinetic Model of Market with Random Saving Propensity," Papers cond-mat/0301289, arXiv.org, revised Jan 2004.
    9. A. Chakraborti & B.K. Chakrabarti, 2000. "Statistical mechanics of money: how saving propensity affects its distribution," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 17(1), pages 167-170, September.
    10. Arnab Chatterjee & Bikas K. Chakrabarti & S. S. Manna, 2003. "Money in Gas-Like Markets: Gibbs and Pareto Laws," Papers cond-mat/0311227, arXiv.org.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Adams Vallejos & Ignacio Ormazabal & Felix A. Borotto & Hernan F. Astudillo, 2018. "A new $\kappa$-deformed parametric model for the size distribution of wealth," Papers 1805.06929, arXiv.org.
    2. Vallejos, Adams & Ormazábal, Ignacio & Borotto, Félix A. & Astudillo, Hernán F., 2019. "A new κ-deformed parametric model for the size distribution of wealth," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 514(C), pages 819-829.
    3. Patriarca, Marco & Chakraborti, Anirban & Germano, Guido, 2006. "Influence of saving propensity on the power-law tail of the wealth distribution," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 369(2), pages 723-736.
    4. John Angle, 2007. "The Macro Model of the Inequality Process and The Surging Relative Frequency of Large Wage Incomes," Papers 0705.3430, arXiv.org.
    5. Chakrabarti, Anindya S. & Chakrabarti, Bikas K., 2010. "Statistical theories of income and wealth distribution," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 4, pages 1-31.
    6. Aydiner, Ekrem & Cherstvy, Andrey G. & Metzler, Ralf, 2018. "Wealth distribution, Pareto law, and stretched exponential decay of money: Computer simulations analysis of agent-based models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 278-288.
    7. Victor M. Yakovenko & J. Barkley Rosser, 2009. "Colloquium: Statistical mechanics of money, wealth, and income," Papers 0905.1518, arXiv.org, revised Dec 2009.
    8. Max Greenberg & H. Oliver Gao, 2024. "Twenty-five years of random asset exchange modeling," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 97(6), pages 1-27, June.
    9. Düring, Bertram & Matthes, Daniel & Toscani, Giuseppe, 2008. "A Boltzmann-type approach to the formation of wealth distribution curves," CoFE Discussion Papers 08/05, University of Konstanz, Center of Finance and Econometrics (CoFE).
    10. Shu-Heng Chen & Sai-Ping Li, 2011. "Econophysics: Bridges over a Turbulent Current," Papers 1107.5373, arXiv.org.
    11. Chakrabarti, Anindya S. & Chakrabarti, Bikas K., 2009. "Microeconomics of the ideal gas like market models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(19), pages 4151-4158.
    12. Marcel Ausloos, 2013. "Econophysics: Comments on a Few Applications, Successes, Methods and Models," IIM Kozhikode Society & Management Review, , vol. 2(2), pages 101-115, July.
    13. Costas Efthimiou & Adam Wearne, 2016. "Household Income Distribution in the USA," Papers 1602.06234, arXiv.org.
    14. Campolieti, Michele, 2018. "Heavy-tailed distributions and the distribution of wealth: Evidence from rich lists in Canada, 1999–2017," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 263-272.
    15. Yougui Wang & Ning Ding, 2005. "Dynamic Process of Money Transfer Models," Papers physics/0507162, arXiv.org.
    16. Fei Cao & Sebastien Motsch, 2021. "Derivation of wealth distributions from biased exchange of money," Papers 2105.07341, arXiv.org.
    17. Brugna, Carlo & Toscani, Giuseppe, 2018. "Kinetic models for goods exchange in a multi-agent market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 499(C), pages 362-375.
    18. Chakrabarti, Anindya S., 2011. "An almost linear stochastic map related to the particle system models of social sciences," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(23), pages 4370-4378.
    19. Ghosh, Asim & Chatterjee, Arnab & Inoue, Jun-ichi & Chakrabarti, Bikas K., 2016. "Inequality measures in kinetic exchange models of wealth distributions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 451(C), pages 465-474.
    20. Juan Pablo Pinasco & Mauro Rodríguez Cartabia & Nicolas Saintier, 2018. "A Game Theoretic Model of Wealth Distribution," Dynamic Games and Applications, Springer, vol. 8(4), pages 874-890, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:367:y:2006:i:c:p:388-414. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.