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The US business cycle: power law scaling for interacting units with complex internal structure

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  • Ormerod, Paul

Abstract

In the social sciences, there is increasing evidence of the existence of power law distributions. The distribution of recessions in capitalist economies has recently been shown to follow such a distribution. The preferred explanation for this is self-organised criticality. Gene Stanley and colleagues propose an alternative, namely that power law scaling can arise from the interplay between random multiplicative growth and the complex structure of the units composing the system. This paper offers a parsimonious model of the US business cycle based on similar principles. The business cycle, along with long-term growth, is one of the two features which distinguishes capitalism from all previously existing societies. Yet, economics lacks a satisfactory theory of the cycle. The source of cycles is posited in economic theory to be a series of random shocks which are external to the system. In this model, the cycle is an internal feature of the system, arising from the level of industrial concentration of the agents and the interactions between them. The model—in contrast to existing economic theories of the cycle—accounts for the key features of output growth in the US business cycle in the 20th century.

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  • Ormerod, Paul, 2002. "The US business cycle: power law scaling for interacting units with complex internal structure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 314(1), pages 774-785.
  • Handle: RePEc:eee:phsmap:v:314:y:2002:i:1:p:774-785
    DOI: 10.1016/S0378-4371(02)01056-7
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    Cited by:

    1. Wright, Ian, 2005. "The social architecture of capitalism," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 346(3), pages 589-620.
    2. Ormerod, Paul, 2015. "The economics of radical uncertainty," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 9, pages 1-20.
    3. Noell, Christian, 2006. "Self-Organization in Agricultural Sectors and the Relevance of Complex Systems Approaches for Applied Economics," 2006 Annual Meeting, August 12-18, 2006, Queensland, Australia 25516, International Association of Agricultural Economists.
    4. Bell, William Paul, 2009. "Adaptive interactive expectations: dynamically modelling profit expectations," MPRA Paper 38260, University Library of Munich, Germany, revised 09 Feb 2010.
    5. William Martin & Robert Rowthorn, 2004. "Will Stability Last?," CESifo Working Paper Series 1324, CESifo.
    6. Wright, Ian, 2009. "Implicit Microfoundations for Macroeconomics," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 3, pages 1-27.
    7. Chu, Zhuang & Yang, Biao & Ha, Chang Yong & Ahn, Kwangwon, 2018. "Modeling GDP fluctuations with agent-based model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 572-581.
    8. Chris Noell, 2007. "A look into the nature of complex systems and beyond “Stonehenge” economics: coping with complexity or ignoring it in applied economics?," Agricultural Economics, International Association of Agricultural Economists, vol. 37(2‐3), pages 219-235, September.

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