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Finding the Impact of Market Visibility and Monopoly on Wealth Distribution and Poverty Using Computational Economics

Author

Listed:
  • Kashif Zia

    (Sohar University)

  • Umar Farooq

    (University of Science and Technology Bannu)

  • Sakeena Al Ajmi

    (Sohar University)

Abstract

The complexity science, with the help of, agent based modeling has recently claimed that the free market economy is producing wealth inequality—a totally opposite perception, which is in practice for more than 10 decades. It is capable of investigating the distribution of money in the market economy and finding solution for unfair gap between the wealth and, thus, overcoming poverty. This paper is an attempt to investigate this claim. This work is inspired from Gooding’s work and it, therefore, reproduces his toy trader model, which claims to offer a fair trading environment. This model is extended towards a more human-oriented economy by introducing two possible biases: the variation of market visibility between the traders (by introducing the social networks of traders) and monopoly. The basic aim was to find out the impact of market visibility and monopoly on wealth distribution and poverty. It was learnt through simulations performed, in NetLogo, that the accessibility of traders, when taken as an important factor of a free market economy, positively influences wealth disparity, wealth gap and controls poverty. However, more human-oriented economy, in fact, widens the rich-poor divide and increases poverty. These results suggest that the authorities must control the market and everyone involved in trading should be given equal opportunities. This would also help in controlling the possible monopoly of the traders. The intrinsic physics of the free market would, otherwise, will always result in an unequal distribution of wealth.

Suggested Citation

  • Kashif Zia & Umar Farooq & Sakeena Al Ajmi, 2023. "Finding the Impact of Market Visibility and Monopoly on Wealth Distribution and Poverty Using Computational Economics," Computational Economics, Springer;Society for Computational Economics, vol. 61(1), pages 113-137, January.
  • Handle: RePEc:kap:compec:v:61:y:2023:i:1:d:10.1007_s10614-021-10201-x
    DOI: 10.1007/s10614-021-10201-x
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    References listed on IDEAS

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    1. Rand, William & Rust, Roland T., 2011. "Agent-based modeling in marketing: Guidelines for rigor," International Journal of Research in Marketing, Elsevier, vol. 28(3), pages 181-193.
    2. Michael Joffe, 2017. "Causal theories, models and evidence in economics—some reflections from the natural sciences," Cogent Economics & Finance, Taylor & Francis Journals, vol. 5(1), pages 1280983-128, January.
    3. Tesfatsion, Leigh & Judd, Kenneth L., 2006. "Handbook of Computational Economics, Vol. 2: Agent-Based Computational Economics," Staff General Research Papers Archive 10368, Iowa State University, Department of Economics.
    4. Claudius Gräbner & Catherine S. E. Bale & Bernardo Alves Furtado & Brais Alvarez-Pereira & James E. Gentile & Heath Henderson & Francesca Lipari, 2019. "Getting the Best of Both Worlds? Developing Complementary Equation-Based and Agent-Based Models," Computational Economics, Springer;Society for Computational Economics, vol. 53(2), pages 763-782, February.
    5. Oded Galor & Joseph Zeira, 1993. "Income Distribution and Macroeconomics," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 60(1), pages 35-52.
    6. Adrian Dragulescu & Victor M. Yakovenko, 2000. "Statistical mechanics of money," Papers cond-mat/0001432, arXiv.org, revised Aug 2000.
    7. Tesfatsion, Leigh, 2006. "Agent-Based Computational Economics: A Constructive Approach to Economic Theory," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 16, pages 831-880, Elsevier.
    8. Tesfatsion, Leigh S., 2002. "Agent-Based Computational Economics: Growing Economies from the Bottom Up," Staff General Research Papers Archive 5075, Iowa State University, Department of Economics.
    9. Thomas Piketty, 2015. "About Capital in the Twenty-First Century," American Economic Review, American Economic Association, vol. 105(5), pages 48-53, May.
    10. George A. Akerlof, 2003. "Behavioral Macroeconomics and Macroeconomic Behavior," The American Economist, Sage Publications, vol. 47(1), pages 25-47, March.
    11. Leigh Tesfatsion & Kenneth L. Judd (ed.), 2006. "Handbook of Computational Economics," Handbook of Computational Economics, Elsevier, edition 1, volume 2, number 2.
    12. Joseph E Stiglitz, 2018. "Where modern macroeconomics went wrong," Oxford Review of Economic Policy, Oxford University Press and Oxford Review of Economic Policy Limited, vol. 34(1-2), pages 70-106.
    13. Davies, James B. & Shorrocks, Anthony F., 2000. "The distribution of wealth," Handbook of Income Distribution, in: A.B. Atkinson & F. Bourguignon (ed.), Handbook of Income Distribution, edition 1, volume 1, chapter 11, pages 605-675, Elsevier.
    14. Mario J. Miranda & Paul L. Fackler, 2004. "Applied Computational Economics and Finance," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262633094, December.
    15. Timothy A. Kohler & Michael E. Smith & Amy Bogaard & Gary M. Feinman & Christian E. Peterson & Alleen Betzenhauser & Matthew Pailes & Elizabeth C. Stone & Anna Marie Prentiss & Timothy J. Dennehy & La, 2017. "Greater post-Neolithic wealth disparities in Eurasia than in North America and Mesoamerica," Nature, Nature, vol. 551(7682), pages 619-622, November.
    16. Raphaele Chappe & Willi Semmler, 2019. "Financial Market as Driver for Disparity in Wealth Accumulation—A Receding Horizon Approach," Computational Economics, Springer;Society for Computational Economics, vol. 54(3), pages 1231-1261, October.
    17. Tim Gooding, 2019. "Economics for a Fairer Society," Springer Books, Springer, number 978-3-030-17020-2, September.
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