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Statistical Mechanics of Competitive Resource Allocation using Agent-based Models

Author

Listed:
  • Anirban Chakraborti
  • Damien Challet
  • Arnab Chatterjee
  • Matteo Marsili
  • Yi-Cheng Zhang
  • Bikas K. Chakrabarti

Abstract

Demand outstrips available resources in most situations, which gives rise to competition, interaction and learning. In this article, we review a broad spectrum of multi-agent models of competition (El Farol Bar problem, Minority Game, Kolkata Paise Restaurant problem, Stable marriage problem, Parking space problem and others) and the methods used to understand them analytically. We emphasize the power of concepts and tools from statistical mechanics to understand and explain fully collective phenomena such as phase transitions and long memory, and the mapping between agent heterogeneity and physical disorder. As these methods can be applied to any large-scale model of competitive resource allocation made up of heterogeneous adaptive agent with non-linear interaction, they provide a prospective unifying paradigm for many scientific disciplines.

Suggested Citation

  • Anirban Chakraborti & Damien Challet & Arnab Chatterjee & Matteo Marsili & Yi-Cheng Zhang & Bikas K. Chakrabarti, 2013. "Statistical Mechanics of Competitive Resource Allocation using Agent-based Models," Papers 1305.2121, arXiv.org, revised Sep 2014.
    • Handle: RePEc:arx:papers:1305.2121
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    References listed on IDEAS

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    1. Fudenberg, Drew & Levine, David, 1998. "Learning in games," European Economic Review, Elsevier, vol. 42(3-5), pages 631-639, May.
    2. J. Doyne Farmer, 2002. "Market force, ecology and evolution," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 11(5), pages 895-953, November.
    3. Daniel L. McFadden, 2013. "The New Science of Pleasure," NBER Working Papers 18687, National Bureau of Economic Research, Inc.
    4. Frédéric Abergel & Anirban Chakraborti & B.K. Chakrabarti & Asim Ghosh, 2013. "Econophysics of systemic risk and network dynamics," Post-Print hal-00872397, HAL.
    5. Mantegna,Rosario N. & Stanley,H. Eugene, 2007. "Introduction to Econophysics," Cambridge Books, Cambridge University Press, number 9780521039871, January.
    6. Drew Fudenberg & David K. Levine, 1998. "The Theory of Learning in Games," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262061945, December.
    7. Chakrabarti,Bikas K. & Chakraborti,Anirban & Chakravarty,Satya R. & Chatterjee,Arnab, 2013. "Econophysics of Income and Wealth Distributions," Cambridge Books, Cambridge University Press, number 9781107013445, January.
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