Skip to main content

System Models for Policy Analysis

  • Chapter
  • First Online:
Public Policy Analysis

Abstract

Although quantitative system models are only one of many tools of a policy analyst, they are an important tool. For the policy analyst, the purpose of building and using models is to estimate things that cannot be observed or measured directly. The prime example is impact assessment—estimating the outcomes of a policy that a decisionmaker may consider adopting. Other uses are diagnosis (estimating what factors have the greatest leverage to change a specified outcome or what is the primary source of a given outcome) and forecasting (estimating how a variable is likely to evolve in the future, usually assuming “present trends”). They also may be used as learning tools (to gain an understanding of how the system works, or may work in the future).

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    Merriam-Webster’s Collegiate Dictionary, 10th edition, 1998, includes the following among its many definitions of model: A description or analogy used to help visualize something (as an atom) that cannot be directly observed.

  2. 2.

    Of course, engineering design models are built for similar purposes.

  3. 3.

    Note that we label the inputs to a model diagram as variables instead of factors to emphasize the distinction between a model diagram and a system diagram.

  4. 4.

    This can be a point of contention between the policy analyst and the academic researcher. The purpose of an academic study, after all, is to find the truth of the matter. Extrapolation is mere speculation, and is generally frowned upon. The purpose of a policy study is to decide what to do next, and the analyst does not have the luxury of waiting until the truth is known with reasonable certainty. Extrapolation is necessary.

  5. 5.

    This theme is developed in Bankes (1993), Hodges (1991), and Pilkey and Pilkey-Jarvis (2007).

References

  • Ackoff RL (1974) The future of operational research is past. J Oper Res Soc 30(2):93–104

    Google Scholar 

  • Agusdinata DB (2008) Exploratory modeling and analysis: a promising method to deal with deep uncertainty. Ph.D. thesis, Delft University of Technology, Delft

    Google Scholar 

  • Armiger G (1995) Handbook of statistical modeling for the social and behavioral sciences. Plenum Press, NY

    Google Scholar 

  • Balci O, Ormsby WF (2007) Conceptual modelling for designing large-scale simulations. J Simul 1:175–186

    Article  Google Scholar 

  • Bankes S (1993) Exploratory modeling for policy analysis. Oper Res 43(3):435–449

    Article  Google Scholar 

  • Barlas Y (1996) Formal aspects of model validity and validation in system dynamics. Syst Dyn Rev 12(3):183–210

    Article  Google Scholar 

  • Carrillo MJ, Hillestad RJ, Twaalfhoven PGJ, Bolten JG, van de Riet OAWT, Walker WE (1996) PACE-FORWARD: policy analytic and computational environment for dutch freight transport, MR-732-EAC/VW, RAND, Santa Monica

    Google Scholar 

  • Caulkins JP, Rydell CP, Schwabe WL, Chiesa J (1997) Mandatory minimum drug sentences: throwing away the key or the taxpayers’ money?, MR-923-RWJ, RAND, Santa Monica

    Google Scholar 

  • Davis PK, Bigelow JH (1998) Experiments in multiresolution modeling, MR-1004-DARPA, RAND, Santa Monica

    Google Scholar 

  • Dupuy TN (1987) Understanding war: history and theory of combat. Paragon House Publishers, NY

    Google Scholar 

  • Environmental & Water Resources Institute (EWRI) (2011). Collaborative modeling for decision support in water resources: principles and best practices, Report 2011-R-03, U.S. Army Corps of Engineers, Institute for Water Resources, Alexandria, Virginia

    Google Scholar 

  • Forrester JW (1961) Industrial dynamics. MIT Press, Cambridge

    Google Scholar 

  • Gold MR (ed) (1996) Cost effectiveness in health and medicine. Oxford University Press, Oxford

    Google Scholar 

  • Greenberger M, Crenson MA, Crissey BL (1976) Models in the policy process: public decision making in the computer era. Russell Sage Foundation, NY

    Google Scholar 

  • Hillestad RJ, Walker WE, Carrillo MJ, Bolten JG, Twaalfhoven PGJ, van de Riet OAWT (1996) FORWARD—freight options for road, water, and rail for the dutch: final report, MR-736-EAC/VW, RAND, Santa Monica

    Google Scholar 

  • Hillier FS, Lieberman GJ (2005) Introduction to operations research, 8th edn. McGraw-Hill, Boston

    Google Scholar 

  • Hodges JS (1991) Six (or so) things you can do with a bad model. Oper Res 39(3):355–365

    Article  Google Scholar 

  • Hodges JS, Dewar JA (1992) Is it you or your model talking? a framework for model validation, R-4114-AF/A/OSD, RAND, Santa Monica

    Google Scholar 

  • Hopkins A (ed) (1992) Measures of the quality of life, and the uses to which such measures may be put, Royal College of Physicians of London

    Google Scholar 

  • Jones-Lee MW (1976) The value of life: an economic analysis. University of Chicago Press, Chicago

    Google Scholar 

  • Kleijnen JPC (1999) Validation of models: statistical techniques and data availability”. 1999 Winter Simulation Conference Proceedings, Phoenix, AZ, 5-8 Dec 1999, pp 647–654

    Google Scholar 

  • Kwakkel JH, Walker WE, Marchau VAWJ, From predictive modeling to exploratory modeling: how to use non-predictive models for decisionmaking under deep uncertainty. In: Proceedings of the 25th mini-euro conference, University of Coimbra, Portugal, 15-17 April 2010 (ISBN 978-989-95055-3-7)

    Google Scholar 

  • Landsburg SE (2011) Price theory and applications, 8th edn. South-Western Cengage Learning, Mason, Ohio

    Google Scholar 

  • Law AM, Kelton WD (1991) Simulation modeling and analysis, 2nd edn. McGraw-Hill, Boston

    Google Scholar 

  • Mankiw NG (1997) Macroeconomics, 3rd edn. Worth Publishers, NY

    Google Scholar 

  • Morton S, Rolph J (2000) Public policy and statistics: case studies from RAND. Springer, NY

    Google Scholar 

  • Perkins F (1994) Practical cost benefit analysis: basic concepts and applications. Macmillan Education, Australia

    Google Scholar 

  • Pilkey OH, Pilkey-Jarvis L (2007) Useless arithmetic: why environmental scientists can’t predict the future. Columbia University Press, NY

    Google Scholar 

  • Roberts EB (ed) (1978) Managerial applications of system dynamics. MIT Press, Cambridge

    Google Scholar 

  • Robinson S (2004) Simulation: the practice of model development and use. Wiley, UK

    Google Scholar 

  • Quade ES (1989) Analysis for public decisions, 3rd edn. Elsevier, NY

    Google Scholar 

  • Seinfeld JH (1986) Atmospheric chemistry and physics of air pollution. Wiley, NY

    Google Scholar 

  • Sterman JD (2000) Business dynamics: systems thinking and modeling for a complex world. McGraw-Hill, Boston

    Google Scholar 

  • SUMMA Consortium (2005). Final publishable report, report for the European Commission (DG-TREN), Leiden. [http://www.tmleuven.be/project/summa/summa-d8.pdf]

  • Van Horn RL (1971) Validation of simulation results. Manage Sci 17:247–258

    Article  Google Scholar 

  • Walker WE, Lang NA, Keur J, Visser HG, Wijnen RAA, Kohse U, Veldhuis J, De Haan ARC (2003) An organizational decision support system for airport strategic exploration. In: Bui T, Sroka H, Stanek S, Goluchowski J (eds) DSS in the uncertainty of the internet age. Publisher of the Karol Adamiecki University of Economics in Katowice, Katowice, pp 435–452

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Warren E. Walker .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer Science+Business Media New York

About this chapter

Cite this chapter

Walker, W.E., van Daalen, C.E. (2013). System Models for Policy Analysis. In: Thissen, W., Walker, W. (eds) Public Policy Analysis. International Series in Operations Research & Management Science, vol 179. Springer, Boston, MA. https://doi.org/10.1007/978-1-4614-4602-6_7

Download citation

Publish with us

Policies and ethics