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On the mechanism of phase transitions in a minimal agent-based macroeconomic model

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  • Sun, Ye
  • Chen, Yu

Abstract

We conduct a theoretical analysis for Mark0, which is a minimal macroeconomic agent-based model, to understand the emergence of phase transitions in the modeled economy. We identify directly the essential components and processes relating to specific mechanisms of all phase transitions in the system. The primary finding is that though the entire economy seems to reach equilibrium as the firms seem to be randomly distributed, some subgroups of firms are undergoing irreversible non-equilibrium processes. In particular, the irreversibility is rooted either in the undiminished demand–supply gap among a group of oversupplying firms, or in the diminishing debt ratio gap among a group of indebted firms. Whereas the undiminished oversupplying firms drive the economy to extreme states overtly, the indebted firms evolve into a cluster inside the economy covertly due to the diminishing debt ratio gap in this subgroup. In the latter case, it is further shown that the self-organized subgroup is steadily driven to the phase boundary, at which the random factors embedded in the model trigger a cascade of default events and cause the crash of system.

Suggested Citation

  • Sun, Ye & Chen, Yu, 2018. "On the mechanism of phase transitions in a minimal agent-based macroeconomic model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 613-624.
  • Handle: RePEc:eee:phsmap:v:506:y:2018:i:c:p:613-624
    DOI: 10.1016/j.physa.2018.04.019
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    References listed on IDEAS

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    1. Gualdi, Stanislao & Tarzia, Marco & Zamponi, Francesco & Bouchaud, Jean-Philippe, 2015. "Tipping points in macroeconomic agent-based models," Journal of Economic Dynamics and Control, Elsevier, vol. 50(C), pages 29-61.
    2. Hwang, Keunho & Kang, Jangkoo & Ryu, Doojin, 2010. "Phase-transition behavior in the emerging market: Evidence from the KOSPI200 futures market," International Review of Financial Analysis, Elsevier, vol. 19(1), pages 35-46, January.
    3. V. Alfi & M. Cristelli & L. Pietronero & A. Zaccaria, 2009. "Minimal agent based model for financial markets II," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 67(3), pages 399-417, February.
    4. Leigh Tesfatsion & Kenneth L. Judd (ed.), 2006. "Handbook of Computational Economics," Handbook of Computational Economics, Elsevier, edition 1, volume 2, number 2.
    5. Lengnick, Matthias, 2013. "Agent-based macroeconomics: A baseline model," Journal of Economic Behavior & Organization, Elsevier, vol. 86(C), pages 102-120.
    6. 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.
    7. J. Doyne Farmer & Duncan Foley, 2009. "The economy needs agent-based modelling," Nature, Nature, vol. 460(7256), pages 685-686, August.
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    Cited by:

    1. Wu, Xifeng & Xu, Yuechao & Lou, Yuting & Chen, Yu, 2018. "Low carbon transition in a distributed energy system regulated by localized energy markets," Energy Policy, Elsevier, vol. 122(C), pages 474-485.
    2. Davis, Natalie & Jarvis, Andrew & Polhill, J. Gareth, 2022. "Co-evolution of network structure and consumer inequality in a spatially explicit model of energetic resource acquisition," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 608(P1).

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