Managing Market Complexity pp 15-26

Part of the Lecture Notes in Economics and Mathematical Systems book series (LNE, volume 662)

Transformation Networks: A study of how technological complexity impacts economic performance

  • Christopher D. Hollander
  • Ivan Garibay
  • Thomas O’Neal


Under a resource-based view of the firm, economic agents transform resources from one form into another. These transformations can be viewed as the application of technology. The relationships between the technologies present in an economy can be modeled by a transformation network. The size and structure of these networks can describe the “economic complexity” of a society. In this paper, we use an agent-based computational economics model to investigate how the density of a transformation network affects the economic performance of its underlying artificial economy, as measured by the GDP. Our results show that the mean and median GDP of this economy increases as the density of its transformation network increases; furthermore, the cause of this increase is related to the number and type of cycles and sinks in the network. Our results suggest that economies with a high degree of economic complexity perform better than simpler economies with lower economic complexity.


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  1. 1.
    Robert L. Axtel. Multi-Agent systems macro: A prospectus. In David Colander, editor, Post-Walrasian Macroeconomics: Beyond the Dynamic Stochastic General Equilibrium Model. Cambridge, 2006.Google Scholar
  2. 2.
    Erik Brouwer, Alfred Kleinknecht, and Jeroen O. N. Reijnen. Employment growth and innovation at the firm level. Journal of Evolutionary Economics, 3(2):153-159, June 1993. ISSN 0936-9937.Google Scholar
  3. 3.
    Manuel Cartier. An Agent-Based model of innovation emergence in organizations: Renault and ford through the lens of evolutionism. Computational & Mathematical Organization Theory, 10(2):147-153, July 2004. ISSN 1381-298X. doi: 10.1023/B:CMOT.0000039167.91320.df. URL
  4. 4.
    Elena Cefis and Orietta Marsili. Survivor: The role of innovation in firms' survival. Research Policy, 35(5):626-641, June 2006. ISSN 0048-7333.Google Scholar
  5. 5.
    By David Colander, Peter Howitt, Alan Kirman, Axel Leijonhufvud, and Perry Mehrling. Beyond DSGE Models : Toward an Empirically Based Macroeconomics. pages 236-240, 2008.Google Scholar
  6. 6.
    Herbert Dawid. Agent-based models of innovation and technological change. volume 2 of Handbook of Computational Economics, pages 1235-1272. Elsevier, 2006.Google Scholar
  7. 7.
    Domenico Delli Gatti, Edoardo Gaffeo, Mauro Gallegati, Gianfranco Giulioni, Alan Kirman, Antonio Palestrini, and Alberto Russo. Complex Dynamics, Financial Fragility and Stylized Facts. In Philippe Mathieu, Bruno Beaufils, and Olivier Brandouy, editors, Artificial Economics, volume 564 of Lecture Notes in Economics and Mathematical Systems, pages 127-135. Springer Berlin Heidelberg, 2006. ISBN 978-3-540-28547-2.Google Scholar
  8. 8.
    Giorgio Gallo, Giustino Longo, Stefano Pallottino, and Sang Nguyen. Directed hypergraphs and applications. Discrete Applied Mathematics, 42(2-3):177-201, April 1993. ISSN 0166218X.Google Scholar
  9. 9.
    Domenico Delli Gatti, Edoardo Gaffeo, and Mauro Gallegati. Complex agent-based macroeconomics: a manifesto for a new paradigm. Journal of Economic Interaction and Coordination, 5(2):111-135, June 2010. ISSN 1860-711X.Google Scholar
  10. 10.
    Nigel Gilbert, Andreas Pyka, and Petra Ahrweiler. Innovation networks - a simulation approach. Journal of Artificial Societies and Social Simulation, 4(3), 2001. URL
  11. 11.
    Cesar A. Hidalgo, Sebastian Bustos, Michele Coscia, Sarah Chung, Juan Jimenez, Alexander Simoes, Muhammed A. Yildirim, Harvard University, John F. Kennedy School of Government, Harvard University, and Center for International Development. The atlas of economic complexity:. Harvard University Center for International Development,, [s.l.] :, 2011. URL Available online.Google Scholar
  12. 12.
    Michael König and Stefano Battiston. From Graph Theory to Models of Economic Networks. A Tutorial. Lecture Notes in Economics and Mathematical Systems, 613(Spring):23-63, 2009. ISSN 00758442.Google Scholar
  13. 13.
    By Blake Lebaron and Leigh Tesfatsion. Modeling Macroeconomies as Open-Ended Dynamic Systems of Interacting Agents. American Economic Review, pages 246-250, 2008.Google Scholar
  14. 14.
    Jack A. Nickerson and Todd R. Zenger. A Knowledge-Based Theory of the Firm - The Problem-Solving Perspective. Organization Science, 15(6):617-632, November 2004. ISSN 1047-7039.Google Scholar
  15. 15.
    Scott E. Page. Diversity and Complexity. Princeton University Press, 1 edition, November 2010. ISBN 0691137676.Google Scholar
  16. 16.
    Frank Schweitzer, Giorgio Fagiolo, Didier Sornette, Fernando Vega-Redondo, Alessandro Vespignani, and Douglas R White. Economic networks: the new challenges. Science (New York, N.Y.), 325(5939):422-5, July 2009. ISSN 1095-9203.Google Scholar
  17. 17.
    Gregory Tassey. Annotated bibliography of technologySs impacts on economic growth. Technical report, National Institute of Standards and Technology, 2009. URL Scholar
  18. 18.
    Leigh Tesfatsion and L. Tesfatsion and K.L. Judd. Agent-Based Computational Economics: A Constructive Approach to Economic Theory. In Journal of Economic Dynamics and Control, volume Volume 2, pages 831-880. Elsevier, April 2006. ISBN 1574-0021.Google Scholar
  19. 19.
    Martin L. Weitzman. Recombinant Growth. Quarterly Journal of Economics, 113(2):331-360, 1998. ISSN 0033-5533.Google Scholar
  20. 20.
    Birger Wernerfelt. A Resource-Based View of the Firm. Strategic Management Journal, 5(2):171-180, January 1984. ISSN 1545-0864.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Christopher D. Hollander
    • 1
  • Ivan Garibay
    • 1
  • Thomas O’Neal
    • 1
  1. 1.University of Central FloridaOrlandoUSA

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