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Statistical mechanics of competitive resource allocation using agent-based models

Author

Listed:
  • Anirban Chakraborti

    (MAS - Mathématiques Appliquées aux Systèmes - EA 4037 - Ecole Centrale Paris)

  • Damien Challet

    (MAS - Mathématiques Appliquées aux Systèmes - EA 4037 - Ecole Centrale Paris)

  • Arnab Chatterjee
  • Matteo Marsili

    (ICTP - Abdus Salam International Centre for Theoretical Physics [Trieste])

  • 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 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 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, 2015. "Statistical mechanics of competitive resource allocation using agent-based models," Post-Print hal-00834380, HAL.
    • Handle: RePEc:hal:journl:hal-00834380
    DOI: 10.1016/j.physrep.2014.09.006
    Note: View the original document on HAL open archive server: https://hal.science/hal-00834380v1
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    References listed on IDEAS

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