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Validation and Functional Complexity

Author

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  • Robert E. Marks

    (School of Economics, Australian School of Business, the University of New South Wales)

Abstract

This paper provides a framework for discussing the validity of computer simulation models of market phenomena. It defines functional complexity and derives measures of this for a well known agent-based simulation model and suggests methods to overcome the obstacle of complexity in validating such models.

Suggested Citation

  • Robert E. Marks, 2013. "Validation and Functional Complexity," Discussion Papers 2013-30, School of Economics, The University of New South Wales.
  • Handle: RePEc:swe:wpaper:2013-30
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    File URL: http://research.economics.unsw.edu.au/RePEc/papers/2013-30.pdf
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    References listed on IDEAS

    as
    1. Matteo Richiardi & Roberto Leombruni & Nicole J. Saam & Michele Sonnessa, 2006. "A Common Protocol for Agent-Based Social Simulation," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 9(1), pages 1-15.
    2. Derek Bunn & Fernando Oliveira, 2003. "Evaluating Individual Market Power in Electricity Markets via Agent-Based Simulation," Annals of Operations Research, Springer, vol. 121(1), pages 57-77, July.
    3. Tesfatsion, Leigh & Judd, Kenneth L., 2006. "Handbook of Computational Economics, Vol. 2: Agent-Based Computational Economics," Staff General Research Papers Archive 10368, Iowa State University, Department of Economics.
    4. Midgley, David & Marks, Robert & Kunchamwar, Dinesh, 2007. "Building and assurance of agent-based models: An example and challenge to the field," Journal of Business Research, Elsevier, vol. 60(8), pages 884-893, August.
    5. Giorgio Fagiolo & Paul Windrum & Alessio Moneta, 2006. "Empirical Validation of Agent Based Models: A Critical Survey," LEM Papers Series 2006/14, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    6. Steven N. Durlauf, 2005. "Complexity and Empirical Economics," Economic Journal, Royal Economic Society, vol. 115(504), pages 225-243, June.
    7. Leigh Tesfatsion & Kenneth L. Judd (ed.), 2006. "Handbook of Computational Economics," Handbook of Computational Economics, Elsevier, edition 1, volume 2, number 2.
    8. Thomas Brenner & Claudia Werker, 2006. "A Practical Guide to Inference in Simulation Models," Papers on Economics and Evolution 2006-02, Philipps University Marburg, Department of Geography.
    9. David F. Midgley & Robert E. Marks & Lee C. Cooper, 1997. "Breeding Competitive Strategies," Management Science, INFORMS, vol. 43(3), pages 257-275, March.
    10. Marks, R E, 1992. "Breeding Hybrid Strategies: Optimal Behaviour for Oligopolists," Journal of Evolutionary Economics, Springer, vol. 2(1), pages 17-38, March.
    11. Vriend, Nicolaas J., 2000. "An illustration of the essential difference between individual and social learning, and its consequences for computational analyses," Journal of Economic Dynamics and Control, Elsevier, vol. 24(1), pages 1-19, January.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    validation of simulation models; agent-based simulations; functional complexity;
    All these keywords.

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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