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Policy Advice Derived From Simulation Models

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  • Brenner, Thomas
  • Werker, Claudia

Abstract

When advising policy we face the fundamental problem that economic processes are connected with uncertainty and thus policy can err. In this paper we show how the use of simulation models can reduce policy errors. We suggest that policy is best based on so-called abductive simulation models, which help to better understand how policy measures can influence economic processes. We show that abductive simulation models use a combination of theoretical and empirical analysis based on different data sets. This helps inferring empirically reliable and meaningful statements about how policy measures influence economic processes. By way of example we show how research subsidies by the government influence the likelihood that a regional cluster emerges.

Suggested Citation

  • Brenner, Thomas & Werker, Claudia, 2009. "Policy Advice Derived From Simulation Models," MPRA Paper 13134, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:13134
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    References listed on IDEAS

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    1. Gönenç Yücel & Els van Daalen, 2009. "An Objective-Based Perspective on Assessment of Model-Supported Policy Processes," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 12(4), pages 1-3.
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    4. Simon Deichsel & Andreas Pyka, 2009. "A Pragmatic Reading of Friedman's Methodological Essay and What It Tells Us for the Discussion of ABMs," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 12(4), pages 1-6.
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    6. Dirk Fornahl & Thomas Brenner (ed.), 2003. "Cooperation, Networks and Institutions in Regional Innovation Systems," Books, Edward Elgar Publishing, number 2713.
    7. Machlup, Fritz, 1978. "Methodology of Economics and Other Social Sciences," Elsevier Monographs, Elsevier, edition 1, number 9780124645509 edited by Shell, Karl.
    8. Thomas Brenner, 2001. "Simulating the Evolution of Localised Industrial Clusters - an Identification of the Basic Mechanisms," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 4(3), pages 1-4.
    9. Thomas Brenner & Claudia Werker, 2007. "A Taxonomy of Inference in Simulation Models," Computational Economics, Springer;Society for Computational Economics, vol. 30(3), pages 227-244, October.
    10. Braunerhjelm, Pontus & Carlsson, Bo, 1999. "Industry Clusters in Ohio and Sweden, 1975-1995," Small Business Economics, Springer, vol. 12(4), pages 279-293, June.
    11. Falk, Rahel, 2007. "Measuring the effects of public support schemes on firms' innovation activities: Survey evidence from Austria," Research Policy, Elsevier, vol. 36(5), pages 665-679, June.
    12. Rolf Sternberg & Timo Litzenberger, 2004. "Regional clusters in Germany--their geography and their relevance for entrepreneurial activities," European Planning Studies, Taylor & Francis Journals, vol. 12(6), pages 767-791, September.
    13. Thomas Brenner & André Mühlig, 2007. "Factors and Mechanisms Causing the Emergence of Local Industrial Clusters - A Meta-Study of 159 Cases," Papers on Economics and Evolution 2007-23, Philipps University Marburg, Department of Geography.
    14. Schwerin, Joachim & Werker, Claudia, 2003. "Learning innovation policy based on historical experience," Structural Change and Economic Dynamics, Elsevier, vol. 14(4), pages 385-404, December.
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    Cited by:

    1. Nicola Lettieri, 2016. "Computational Social Science, the Evolution of Policy Design and Rule Making in Smart Societies," Future Internet, MDPI, vol. 8(2), pages 1-17, May.
    2. Vermeulen, Ben & Pyka, Andreas, 2016. "Agent-based modeling for decision making in economics under uncertainty," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 10, pages 1-33.
    3. Bruno Bruna & Faggini Marisa, 2020. "Sharing Competition: An Agent-Based Model for the Short-Term Accommodations Market," The B.E. Journal of Economic Analysis & Policy, De Gruyter, vol. 20(2), pages 1-13, April.
    4. Cristina Ponsiglione & Ivana Quinto & Giuseppe Zollo, 2018. "Regional Innovation Systems as Complex Adaptive Systems: The Case of Lagging European Regions," Sustainability, MDPI, vol. 10(8), pages 1-19, August.
    5. Bernardo Alves Furtado & Gustavo Onofre Andre~ao, 2022. "Machine Learning Simulates Agent-Based Model Towards Policy," Papers 2203.02576, arXiv.org, revised Nov 2022.

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    More about this item

    Keywords

    Policy Advice; Simulation Models; Uncertainty; Methodology;
    All these keywords.

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • B52 - Schools of Economic Thought and Methodology - - Current Heterodox Approaches - - - Historical; Institutional; Evolutionary; Modern Monetary Theory;
    • H89 - Public Economics - - Miscellaneous Issues - - - Other
    • B41 - Schools of Economic Thought and Methodology - - Economic Methodology - - - Economic Methodology

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