Balance Sheet Approach to Agent-Based Computational Economics: The EURACE Project

  • Andrea Teglio
  • Marco Raberto
  • Silvano Cincotti
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 77)

Abstract

Handling carefully monetary and real flows, given by agents’ behaviors and interactions, is a key requirement when dealing with complex economic models populated by a high number of agents. The paper shows how the stock-flows consistency issue has been faced in the EURACE model, by considering a dynamic balance sheet approach for modeling and validation purposes.

Keywords

Agent-based computational economics Balance sheet approach EURACE project 

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References

  1. 1.
    Basu, N., Pryor, R., Quint, T.: ASPEN: a microsimulation model of the economy. Comput. Econ. 12(3), 223–241 (1998)MATHCrossRefGoogle Scholar
  2. 2.
    Bruun, C.: Agent-based Keynesian economics: simulating a monetary production system bottom-up, University of Aalborg, Denmark (1999)Google Scholar
  3. 3.
    Coakley, S., Kiran, M.: EURACE Report D1.1: X-Agent framework and software environment for agent-based models in economics. Department of Computer Science, University of Sheffield, UK (2007)Google Scholar
  4. 4.
    Deissenberg, C., van der Hoog, S., Dawid, H.: EURACE: A Massively Parallel Agent-based Model of the European Economy. Appl. Math. Comput. 204, 541–552 (2008)MATHCrossRefMathSciNetGoogle Scholar
  5. 5.
    Dosi, G., Fagiolo, G., Roventini, A.: Schumpeter Meeting Keynes: A Policy-Friendly Model of Endogenous Growth and Business Cycles. J. Econ. Dynam. Control (in press, 2010)Google Scholar
  6. 6.
    Dosi, G., Fagiolo, G., Roventini, A.: The Microfoundations of Business Cycles: An Evolutionary, Multi-Agent Model. J. Evol. Econ. 18(3-4), 413–432 (2008)CrossRefGoogle Scholar
  7. 7.
    Epstein, J.M.: Agent-Based Computational Models And Generative Social Science. Complexity 4(5), 41–60 (1999)CrossRefMathSciNetGoogle Scholar
  8. 8.
    Epstein, J.M., Axtell, R.L.: Growing Artificial Societies: Social Science from the Bottom Up (Complex Adaptive Systems). MIT Press, Cambridge (1996)Google Scholar
  9. 9.
    EURACE Final Activity Report (2009), http://www.eurace.org/
  10. 10.
    Holcombe, M., Coakley, S., Smallwood, R.: A General Framework for Agent-based Modelling of Complex Systems. EURACE Working paper WP1.1, Department of Computer Science, University of Sheffield, UK (2006)Google Scholar
  11. 11.
    Kutschinski, E., Uthmann, T., Polani, D.: Learning Competitive Pricing Strategies by Multi-Agent Reinforcement Learning. J. Econometrics 27, 2207–2218 (2001)MathSciNetGoogle Scholar
  12. 12.
    LeBaron, B.D.: Agent-based computational finance. In: Tesfatsion, L., Judd, K. (eds.) Handbook of Computational Economics, North Holland, Amsterdam (2006)Google Scholar
  13. 13.
    Sallans, B., Pfister, A., Karatzoglou, A., Dorffner, G.: Simulations and validation of an integrated markets model. J. Artif. Soc. Social Simul. 6(4) (2003), http://jasss.soc.surrey.ac.uk/6/4/2.html
  14. 14.
    Tassier, T.: Emerging small-world referral networks in evolutionary labor markets. IEEE Trans. Evol. Comput. 5(5), 482–492 (2001)CrossRefGoogle Scholar
  15. 15.
    Tesfatsion, L., Judd, K.: Agent-Based Computational Economics. North Holland, Amsterdam (2006)Google Scholar
  16. 16.
    Tesfatsion, L.: Structure, behaviour, and market power in an evolutionary labour market with adaptive search. J. Econom. Dynam. Control 25, 419–457 (2001)MATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Andrea Teglio
    • 1
  • Marco Raberto
    • 2
  • Silvano Cincotti
    • 3
  1. 1.Departament d’EconomiaUniversitat Jaume I, Castellón de la PlanaSpain
  2. 2.School of Science and EngineeringReykjavik UniversityReykjavikIceland
  3. 3.DIBE-CINEFUniversità di GenovaGenovaItaly

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