Abstract
Using computer simulations in examining, explaining and predicting social processes and relationships as well as measuring the possible impact of policies has become an important part of policy-making. This chapter presents a comparative analysis of simulation models utilised in the field of policy-making. Different models and modelling theories and approaches are examined and compared to each other with respect to their role in public decision-making processes. The analysis has shown that none of the theories alone is able to address all aspects of complex policy interactions, which indicates the need for the development of hybrid simulation models consisting of a combinatory set of models built on different modelling theories. Building such hybrid simulation models will also demand the development of new and more comprehensive simulation modelling platforms.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
eGovPoliNet—Building a global multidisciplinary digital governance and policy modelling research.and practice community. See http://www.policy-community.eu/ (last access: 28th July 2014).
- 2.
MoPoS—A monetary Policy Simulation Game (Lengwiler 2004).
- 3.
GAIM—Gestione Accoglienza IMmigrati (Sedehi 2006) is used for the training of foreign intercultural mediators in the immigration housing management courses.
- 4.
http://www.anylogic.com/ (last access: 28th July 2014).
- 5.
http://ccl.northwestern.edu/netlogo/ (last access: 28th July 2014).
- 6.
A more detailed overview of tools and technologies supporting policy making is provided in (Kamateri et al. 2014).
- 7.
A comparative analysis of tools and technical frameworks is provided in (Kamateri et al. 2014).
- 8.
The framework is published in Annex I to technical report D 4.2 of eGovPoliNet: Maria A. Wimmer and Dragana Majstorovic (Eds.): Synthesis Report of Knowledge Assets, including Visions (D 4.2). eGovPoliNet consortium, 2014, report available under http://www.policy-community.eu/results/public-deliverables/ (last access: 28th July 2014).
- 9.
A more detailed discussion of stakeholder engagement in policy making is given in (Helbig et al. 2014).
References
Ahrweiler P, Pyka A, Gilbert N (2004) Simulating knowledge dynamics in innovation networks (SKIN). In: Leombruni R, Richiardi M (eds) The agent-based computational approach. World Scientific Press, Singapore, pp 284–296
Ahrweiler P, Gilbert N, Pyka, A (2011a) Agency and structure. A socials simulation of knowledgeintensive industries. Comput Math Org Theor 17(1):59–76
Ahrweiler P, Pyka A, Gilbert N (2011b) A new model for university-industry links in knowledgebased economies. J Prod Innov Manage 28(2):218–235
Anderson RE, Hicks C (2011) Highlights of contemporary microsimulation. Soc Sci Comput Rev 29(1):3–8
Astolfi R, Lorenzoni L, Oderkirk J (2012) A comparative analysis of health forecasting methods. OECD Health Working Papers, No. 59. OECD Publishing. doi:10.1787/18152015
Bayoumi T (2004) GEM: a new international macroeconomic model, Occasional Paper 239. Washington, D.C.: International Monetary Fund. http://213.154.74.164/invenio/record/12437/files/bayoumiop239.pdf. Accessed 29th July 2014
Brouwers L, Cakici B, Camitz M, Tegnell A, Boman M (2009a) Economic consequences to society of pandemic H1N1 influenza 2009– preliminary results for Sweden. Euro Surveillance 14(37):pii= 19333. http://www.eurosurveillance.org/ViewArticle.aspx?ArticleId = 19333 Accessed 29 July 2014
Brouwers L, Camitz M, Cakici B, Mäkilä K, Saretok P (2009b) MicroSim: modeling the Swedish Population. http://arxiv.org/abs/0902.0901. Accessed 29 July 2014
Brown L, Harris A, Picton M, Thurecht L, Yap M, Harding A et al. (2009). Linking micro-simulation and macro-economic models to estimate the economic impact of chronic disease prevention. In: Zaidi A, Harding A, Williamson P (eds) New frontiers in microsimulation modelling. Ashgate, European Centre Vienna, pp 527–556
Butka P, Mach M, Furdik K, Genci J (2011) Design of a system architecture for support of collaborative policy modelling processes. In: Proceedings of SACI 2011, 6th IEEE International Symposium on Applied Computational Intelligence and Informatics. Red Hook, NY, USA, Óbuda University, Hungary, IEEE, Curran Associates, Inc, pp 193–198
Carrat F, Vergu E, Ferguson N, Lemaitre M, Cauchemez S, Leach S, Valleron A (2008) Time lines of infection and disease in human influenza: a review of volunteer challenge studies. Am J Epidemiol 167(7):775–785
Collins A, Joseph D, Bielaczyc K (2004) Design research: theoretical and methodological issues. J Learn Sci 13(1):15–42
Davis P, Lay-Yee R, Pearson J (2010) Using micro-simulation to create a synthesized data set and test policy options: the case of health service effects under demographic ageing. Health Policy 97:267–274
Farmer JD, Foley D (2009) The economy needs agent-based modelling. Nature 460:685–686
Fasth T, Ihlar M, Brouwers L (2010) VirSim—a model to support pandemic policy making. PLOS Currents Influenza. 2010 Sep 22, 2: RRN1181. doi:10.1371/currents.RRN1181
Fergusson D, Horwood L, Shannon F, Lawton J (1989) The Christchurch child development study: a review of epidemiological findings. Paediatr Perinat Epidemiol 3:278–301
Forrester JW (1961) Industrial dynamics. MIT Press, Cambridge
Fraser C, Donnelly CSC, WP H, MD VK, TD H et al. (2009). WHO rapid pandemic assessment collaboration. Pandemic potential of a strain of influenza A (H1N1): early findings. Science 324(5934). PubMed PMID: 19433588, pp. 1557–1561
Gilbert N (2007) Agent-based models. Sage, London
Gilbert N, Troitzsch K (2005) Simulation for the social scientist. Open University Press, Maidenhead
Gilbert N, Pyka A, Ahrweiler P (2001) Innovation networks—a simulation approach. J Artif Soc Soc Simul 4(3)
Gilbert N, Ahrweiler P, Pyka A (2007) Learning in innovation networks—some simulation experiments. Physica A: Stat Mech Appl 374(1):100–109
Gilbert N, Ahrweiler P, Pyka A (2014) Simulating knowledge dynamics in innovation networks. Springer, Heidelberg
Greenwood DJ, Levin, M. (2006) Introduction to action research: social research for social change. Sage, Thousand Oaks
Harrison RJ, Lin Z, Carroll GR, Carley KM (2007) Simulation modeling in organizational and management research. Acad Manage Rev 32(4)1229–1245
Helbig N, Dawes SS, Dzhusupova Z, Klievink B, Mkude CG (2014) Stakeholder engagement in policy development: observations and lessons from international experience. In: Janssen M, Wimmer MA, Deljoo A (eds) Policy practice and digital science—integrating complex systems, social simulation and public administration in policy research. Springer Science, Berlin
Jameson A (1996) Numerical uncertainty management in user and student modeling: an overview of systems and issues. User modeling and user adapted interaction, 5, special issue on numerical uncertainty management in user and student modeling, pp 193–251
Kamateri E, Panopoulou E, Tambouris E, Tarabanis K, Ojo A, Lee D, Price D (2014) A comparative analysis of tools and technologies for policy making. In: Janssen M, Wimmer MA, Deljoo A (eds) Policy practice and digital science—integrating complex systems, social simulation and public administration in policy research. Springer Science, Berlin
Lay-Yee R, Milne B, Davis P, Pearson J, McLay J (2014) Determinants and disparities: a simulation approach to the case of child health care. Under Review
Lengwiler Y (2004) A monetary policy simulation game. J Econ Educ 35(2):175–183
Lotzmann U, Meyer R (2011) DRAMS—a declarative rule-based agent modelling system. In: Proceedings of 25th European Conference on Modelling and Simulation (pp 77–83)
Lotzmann U, Wimmer MA (2013) Evidence traces for multi-agent declarative rule-based policy simulation. In Proceedings of the 17th IEEE/ACM International Symposium on Distributed Simulation and Real Time Applications (DS-RT 2013). IEEE Computer Society, pp115–122
Mannion O, Lay-Yee R, Wrapson W, Davis P, Pearson J (2012) JAMSIM: a microsimulation modelling policy tool. J Artif Soc Soc Simul 15(1):8
Maria A (1997) Introduction to modelling and simulation. Proceedings of the 29th Conference on Winter Simulation, pp 7–13
Martini AP, Trivellato U (1997) The role of survey data in microsimulation models for social policy analysis. Labour 02/1997, 11(1):83–112. (doi:10.1111/1467–9914.00030)
Mayring P (2011) Qualitative Inhaltsanalyse: Grundlagen und Techniken. Beltz, Weinheim und Basel
McLay J, Lay-Yee R, Milne B et al (2014) Statistical modelling techniques for dynamic microsimulation: An empirical performance assessment. Under review
Milne BJ, Lay-Yee R, McLay J, Tobias MTP, Armstrong A, Lynn R et al (2014) A collaborative approach to bridging the research-policy gap through the development of policy advice software. Evid Policy 10(1):127–136
Orcutt G (1957) A new type of socio-economic system. Rev Econ Stat 39(2):116–23
Pyka A, Gilbert N, Ahrweiler P (2007) Simulating knowledge-generation and—distribution processes. Innov Collab Netw, Cybern Syst 38:667–693
Ronald F, Halpern JY (1991) Uncertainty, belief and probability. Computational Intell 7:160–173
Scherer S, Wimmer MA, Markisic S (2013) Bridging narrative scenario texts and formal policy modeling through conceptual policy modeling. Artif Intell Law 21(4):455–484
Schindler J (2013) About the uncertainties in model design and their effects: an illustration with a land-use model. J Artif Soc Soc Simul 16(4):6
Scholz R, Nokkala T, Ahrweiler P, Pyka A, Gilbert N (2010) The agent-based NEMO model (SKEIN): simulating European framework programmes. In: Ahrweiler P (ed) Innovation in complex social systems. Routledge Studies in Global Competition (pp 300–314)
Sedehi H (2006) GAIM (Gestione Accoglienza IMmigrati): a System Dynamics Model for Immigration “housing” Management. In: Proceedings of International Conference of the System Dynamics Society. Nijmegen, The Netherlands (pp 3359–3367)
Solar O, Irwin A (2010) A conceptual framework for action on the social determinants of health. Social Determinants of Health Discussion, Paper 2: Policy and Practice. World Health Organization, Geneva
Spielauer M (2011) What is social science microsimulation. Soc Sci Comput Rev 29(1):9–20
Srbljanovic A, Skunca O (2003) An introduction to agent-based modelling and simulation of social processes. Interdiscip Descr Complex Syst 1(1–2):1–8
Statistics Sweden (Statistiska centralbyrån, SCB). (2009). Sweden’s population by sex and age on 31/12/2008. http://www.scb.se/Pages/TableAndChart____262460.aspx. Accessed 28 July 2014
The National Board of Health and Welfare. (2011). A(H1N1) 2009– An evaluation of Sweden’s preparations for and management of the pandemic. Evaluation Report number 2011–8–4
van Egmond S., Zeiss R (2010) Modeling for policy science-based models as performative boundary objects for Dutch policy making. Sci Stud 23(1):58–78
Wallinga JTP (2006) Using data on social contacts to estimate age-specific transmission parameters for respiratory-spread infectious agents. Am J Epidemiol 164(10):936–944
Wimmer MA (2011) Open government in policy development: from collaborative scenario texts to formal policy models. Proceedings of 7th International Conference on Distributed Computing and Internet Technologies (ICDCIT—2011) (pp 76–91). Berlin, Springer
Wimmer MA, Scherer S, Moss S, Bicking M (2012) Method and tools to support stakeholder engagement in policy development. The OCOPOMO project. Int J Electron Gov Res 8(3):98–119
Zaidi A, Harding A, Williamson P (2009) New frontiers in microsimulation modeling (Public policy and social welfare. Ashgate Publishing Ltd, England
Zeigler B (1976) Theory of modeling and simulation.Wiley,NewYork
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Majstorovic, D., Wimmer, M., Lay-Yee, R., Davis, P., Ahrweiler, P. (2015). Features and Added Value of Simulation Models Using Different Modelling Approaches Supporting Policy-Making: A Comparative Analysis. In: Janssen, M., Wimmer, M., Deljoo, A. (eds) Policy Practice and Digital Science. Public Administration and Information Technology, vol 10. Springer, Cham. https://doi.org/10.1007/978-3-319-12784-2_6
Download citation
DOI: https://doi.org/10.1007/978-3-319-12784-2_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-12783-5
Online ISBN: 978-3-319-12784-2
eBook Packages: Business and EconomicsEconomics and Finance (R0)