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

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Combining Soft Computing and Statistical Methods in Data Analysis

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.

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References

  1. Basu, N., Pryor, R., Quint, T.: ASPEN: a microsimulation model of the economy. Comput. Econ. 12(3), 223–241 (1998)

    Article  MATH  Google Scholar 

  2. Bruun, C.: Agent-based Keynesian economics: simulating a monetary production system bottom-up, University of Aalborg, Denmark (1999)

    Google Scholar 

  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. 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)

    Article  MATH  MathSciNet  Google Scholar 

  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. 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)

    Article  Google Scholar 

  7. Epstein, J.M.: Agent-Based Computational Models And Generative Social Science. Complexity 4(5), 41–60 (1999)

    Article  MathSciNet  Google Scholar 

  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. EURACE Final Activity Report (2009), http://www.eurace.org/

  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. Kutschinski, E., Uthmann, T., Polani, D.: Learning Competitive Pricing Strategies by Multi-Agent Reinforcement Learning. J. Econometrics 27, 2207–2218 (2001)

    MathSciNet  Google Scholar 

  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. 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. Tassier, T.: Emerging small-world referral networks in evolutionary labor markets. IEEE Trans. Evol. Comput. 5(5), 482–492 (2001)

    Article  Google Scholar 

  15. Tesfatsion, L., Judd, K.: Agent-Based Computational Economics. North Holland, Amsterdam (2006)

    Google Scholar 

  16. Tesfatsion, L.: Structure, behaviour, and market power in an evolutionary labour market with adaptive search. J. Econom. Dynam. Control 25, 419–457 (2001)

    Article  MATH  Google Scholar 

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Teglio, A., Raberto, M., Cincotti, S. (2010). Balance Sheet Approach to Agent-Based Computational Economics: The EURACE Project. In: Borgelt, C., et al. Combining Soft Computing and Statistical Methods in Data Analysis. Advances in Intelligent and Soft Computing, vol 77. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14746-3_74

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  • DOI: https://doi.org/10.1007/978-3-642-14746-3_74

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-14745-6

  • Online ISBN: 978-3-642-14746-3

  • eBook Packages: EngineeringEngineering (R0)

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