Policy Advice Derived From Simulation Models
AbstractWhen 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.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoPaper provided by University Library of Munich, Germany in its series MPRA Paper with number 13134.
Date of creation: 01 Jan 2009
Date of revision:
Policy Advice; Simulation Models; Uncertainty; Methodology;
Other versions of this item:
- Thomas Brenner & Claudia Werker, 2009. "Policy Advice Derived from Simulation Models," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 12(4), pages 2.
- 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 - - - Institutional; Evolutionary
- H89 - Public Economics - - Miscellaneous Issues - - - Other
- B41 - Schools of Economic Thought and Methodology - - Economic Methodology - - - Economic Methodology
This paper has been announced in the following NEP Reports:
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- 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.
- Thomas Brenner & AndrÃ© MÃ¼hlig, 2007. "Factors and Mechanisms Causing the Emergence of Local Industrial Clusters - A Meta-Study of 159 Cases," Papers on Economics and Evolution 2007-23, Max Planck Institute of Economics, Evolutionary Economics Group.
- Andrew Brown & Gary Slater & David A. Spencer, 2002. "Driven to abstraction? Critical realism and the search for the 'inner connection' of social phenomena," Cambridge Journal of Economics, Oxford University Press, vol. 26(6), pages 773-788, November.
- Thomas Brenner & Claudia Werker, 2007. "A Taxonomy of Inference in Simulation Models," Computational Economics, Society for Computational Economics, vol. 30(3), pages 227-244, October.
- 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 4.
- Pavitt, Keith, 1984. "Sectoral patterns of technical change: Towards a taxonomy and a theory," Research Policy, Elsevier, vol. 13(6), pages 343-373, December.
- 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.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Ekkehart Schlicht).
If references are entirely missing, you can add them using this form.