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Industrial dynamics, fiscal policy and R&D: Evidence from a computational experiment

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  • Russo, Alberto
  • Catalano, Michele
  • Gaffeo, Edoardo
  • Gallegati, Mauro
  • Napoletano, Mauro

Abstract

We present an agent-based computational model in which bounded rational firms and workers trade on fully decentralized markets for final goods and labor by means of random matching protocols. The model replicates several macroeconomic phenomena regularly observed in the data, with aggregate features emerging from the localized interactions of individual entities. The model is then used as a computational laboratory to run an experiment on the role of fiscal policy in increasing macroeconomic performance.

Suggested Citation

  • Russo, Alberto & Catalano, Michele & Gaffeo, Edoardo & Gallegati, Mauro & Napoletano, Mauro, 2007. "Industrial dynamics, fiscal policy and R&D: Evidence from a computational experiment," Journal of Economic Behavior & Organization, Elsevier, vol. 64(3-4), pages 426-447.
    • Handle: RePEc:eee:jeborg:v:64:y:2007:i:3-4:p:426-447
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    1. G. Fagiolo & G. Dosi & R. Gabriele, 2004. "Matching, Bargaining, And Wage Setting In An Evolutionary Model Of Labor Market And Output Dynamics," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 7(02), pages 157-186.
    2. Tesfatsion, Leigh S., 2002. "Agent-Based Computational Economics: Growing Economies from the Bottom Up," Staff General Research Papers Archive 5075, Iowa State University, Department of Economics.
    3. Bottazzi, Giulio & Dosi, Giovanni & Lippi, Marco & Pammolli, Fabio & Riccaboni, Massimo, 2001. "Innovation and corporate growth in the evolution of the drug industry," International Journal of Industrial Organization, Elsevier, vol. 19(7), pages 1161-1187, July.
    4. Mauro Gallegati & Alan Kirman (ed.), 1999. "Beyond the Representative Agent," Books, Edward Elgar Publishing, number 1375.
    5. Gode, Dhananjay K & Sunder, Shyam, 1993. "Allocative Efficiency of Markets with Zero-Intelligence Traders: Market as a Partial Substitute for Individual Rationality," Journal of Political Economy, University of Chicago Press, vol. 101(1), pages 119-137, February.
    6. L. A. N. Amaral & S. V. Buldyrev & S. Havlin & H. Leschhorn & P. Maass & M. A. Salinger & H. E. Stanley & M. H. R. Stanley, 1997. "Scaling behavior in economics: I. Empirical results for company growth," Papers cond-mat/9702082, arXiv.org.
    7. Joshua M. Epstein & Robert L. Axtell, 1996. "Growing Artificial Societies: Social Science from the Bottom Up," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262550253, April.
    8. Stiglitz, Joseph E., 1989. "Imperfect information in the product market," Handbook of Industrial Organization, in: R. Schmalensee & R. Willig (ed.), Handbook of Industrial Organization, edition 1, volume 1, chapter 13, pages 769-847, Elsevier.
    9. Lane, David A, 1993. "Artificial Worlds and Economics, Part I," Journal of Evolutionary Economics, Springer, vol. 3(2), pages 89-107, May.
    10. Stanley, Michael H. R. & Buldyrev, Sergey V. & Havlin, Shlomo & Mantegna, Rosario N. & Salinger, Michael A. & Eugene Stanley, H., 1995. "Zipf plots and the size distribution of firms," Economics Letters, Elsevier, vol. 49(4), pages 453-457, October.
    11. Alan P. Kirman, 1992. "Whom or What Does the Representative Individual Represent?," Journal of Economic Perspectives, American Economic Association, vol. 6(2), pages 117-136, Spring.
    12. Lane, David A, 1993. "Artificial Worlds and Economics, Part II," Journal of Evolutionary Economics, Springer, vol. 3(3), pages 177-197, August.
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