Statistical mechanics of competitive resource allocation using agent-based models

Anirban Chakraborti, Damien Challet, Arnab Chatterjee, Matteo Marsili, Yi-Cheng Zhang and Bikas K. Chakrabarti
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Anirban Chakraborti: MAS - Mathématiques Appliquées aux Systèmes - EA 4037 - Ecole Centrale Paris
Matteo Marsili: ICTP - Abdus Salam International Centre for Theoretical Physics [Trieste]

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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.

Date: 2015-01-25
Note: View the original document on HAL open archive server: https://hal.science/hal-00834380v1
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Published in Physics Reports, 2015, 552, pp.1-25. ⟨10.1016/j.physrep.2014.09.006⟩

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Related works:
Working Paper: Statistical Mechanics of Competitive Resource Allocation using Agent-based Models (2014)
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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-00834380

DOI: 10.1016/j.physrep.2014.09.006

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