Skip to main content

On the Spot and Map: Interactive Model-Based Policy Support Under Deep Uncertainty

  • Chapter
  • First Online:
Policy Analytics, Modelling, and Informatics

Part of the book series: Public Administration and Information Technology ((PAIT,volume 25))

  • 857 Accesses

Abstract

In this chapter, we discuss and demonstrate the use of ‘on the spot’ and ‘on the map’ scenario exploration and policy-support in workshop settings. First we justify the need for exploratory model-based policy workshops. Then we present some methods and techniques needed for these workshops. Special attention is paid to new techniques we believe are crucially needed for this kind of interactive workshop if time is of the essence, namely (1) techniques to quickly generate small but diverse ensembles of alternative scenarios, and (2) techniques to visualize whole-system dynamics on maps by means of geospatial animations. We subsequently describe a workshop related to the 2015–2016 European refugee crisis for which this approach and these techniques were developed and used. Finally, we discuss shortcomings and improvements to deal with these shortcomings and conclude.

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

Access this chapter

Institutional subscriptions

Notes

  1. 1.

    See http://europa.eu/rapid/press-release_MEMO-16-963_en.htm.

  2. 2.

    See for example http://www.spiegel.de/international/europe/the-refugee-deal-between-the-eu-and-turkey-is-failing-a-1094339.html.

  3. 3.

    See: http://ramiro.org/notebook/basemap-choropleth/.

  4. 4.

    See: http://matplotlib.org/basemap/index.html.

  5. 5.

    The scripts can be obtained from the authors.

References

  • Agusdinata DB (2008) Exploratory modeling and analysis. A promising method to deal with deep uncertainty. PhD thesis, Next Generation Infrastructures Foundation, Delft

    Google Scholar 

  • Auping WL, Pruyt E, Kwakkel JH (2015) Societal ageing in the Netherlands: a robust system dynamics approach. Syst Res Behav Sci 32(4):485–501. doi:10.1002/sres.2340

    Article  Google Scholar 

  • Bankes SC (1993) Exploratory modeling for policy analysis. Oper Res 41(3):435–449

    Article  Google Scholar 

  • Bergot T (2001) Influence of the assimilation scheme on the efficiency of adaptive observations. Q J R Meteorol Soc 127(572):635–660

    Article  Google Scholar 

  • Bishop CH, Etherton BJ, Majumdar SJ (2001) Behavior space sampling with the ensemble transform Kalman filter. Part I: theoretical aspects. Mon Weather Rev 129(3):420–436

    Article  Google Scholar 

  • Bossel H (2007) Systems and models: complexity, dynamics, evolution, sustainability. Books on Demand GmbH, Norderstedt

    Google Scholar 

  • Bryant BP, Lempert RJ (2010) Thinking inside the box: a participatory, computer-assisted approach to scenario discovery. Technol Forecast Soc Chang 77(1):34–49

    Article  Google Scholar 

  • Bucher CG (1988) Behavior space sampling—an iterative fast Monte Carlo procedure. Struct Saf 5(2):119–126

    Article  Google Scholar 

  • Fiddaman T 1997 Feedback complexity in integrated climate-economy models. Ph.D. Thesis, MIT Sloan School of Management

    Google Scholar 

  • Ford A (2009) Modeling the environment, 2nd edn. Island Press, Washington, D.C

    Google Scholar 

  • Forrester JW (1958) Industrial dynamics. A major breakthrough for decision makers. Harv Bus Rev 36(4):37–66

    Google Scholar 

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

    Google Scholar 

  • Forrester JW (1968) Principles of systems. Wright-Allen Press, Inc., Cambridge

    Google Scholar 

  • Forrester JW (1969) Urban dynamics. Productivity Press, Cambridge

    Google Scholar 

  • Forrester JW (1971) World dynamics. Wright-Allen Press, Inc., Cambridge

    Google Scholar 

  • Friedman JH, Fisher NI (1999) Bump-hunting in high-dimensional data. Stat Comput 9:123–143

    Article  Google Scholar 

  • Groves D, Lempert RL (2007) A new analytic method for finding policy-relevant scenarios. Glob Environ Chang 17:73–85

    Article  Google Scholar 

  • Hamarat C, Kwakkel JH, Pruyt E (2013) Adaptive robust design under deep uncertainty. Technol Forecast Soc Chang 80(3):408–418

    Article  Google Scholar 

  • Hamarat C, Kwakkel JH, Pruyt E, Loonen ET (2014) An exploratory approach for adaptive policymaking by using multi-objective robust optimization. Simul Model Pract Theory 46:25–39

    Article  Google Scholar 

  • Islam T, Pruyt E (2016) Scenario generation using adaptive sampling: The case of resource scarcity. Environ Model Softw 79:285–299. doi:10.1016/j.envsoft.2015.09.014

    Article  Google Scholar 

  • Kwakkel JH, Pruyt E (2013) Exploratory modeling and analysis, an approach for model-based foresight under deep uncertainty. Technol Forecast Soc Chang 80(3):419–431. doi:10.1016/j.techfore.2012.10.005

    Article  Google Scholar 

  • Kwakkel JH, Pruyt E (2015) Using system dynamics for grand challenges: the ESDMA approach. Syst Res Behav Sci 32:358–375. doi:10.1002/sres.2225

    Article  Google Scholar 

  • Kwakkel JH, Cunningham SW, Pruyt E (2014) Improving scenario discovery by bagging random boxes. In Proceedings of the 2014 Portland international conference on management of engineering and technology conference

    Google Scholar 

  • Lempert RJ, Collins MT (2007) Managing the risk of uncertain threshold response: comparison of robust, optimum, and precautionary approaches. Risk Anal 27(4):1009–1026

    Article  Google Scholar 

  • Lempert RJ, Popper SW, Bankes SC (2003) Shaping the next one hundred years: New methods for quantitative, long-term policy analysis. RAND report MR-1626, The RAND Pardee Center, Santa Monica, CA

    Google Scholar 

  • McKay MD, Beckman RJ, Conover WJ (1979) Comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics 21(2):239–245

    Google Scholar 

  • Petitjean FO, Ketterlin A, Gancarski P (2011) A global averaging method for dynamic time warping, with applications to clustering. Pattern Recog 44(3):678–693

    Article  Google Scholar 

  • Pruyt E (2013) Small system dynamics models for big issues: triple jump towards real-world complexity. TU Delft Library, Delft. Available from http://simulation.tbm.tudelft.nl/smallSDmodels/Intro.html

  • Pruyt E (2015) From building a model to adaptive robust decision making using systems modeling. In: Janssen M, Wimmer MA, Deljoo A (eds) Policy practice and digital science: integrating complex systems, social simulation and public administration in policy research. Volume 10. Series: Public administration and information technology. Springer International Publishing, Switzerland, pp 75–93

    Google Scholar 

  • Pruyt E (2016) Integrating systems modelling and data science: the joint future of simulation and ‘big data’ science. Int J Syst Dyn Appl 5(1):1–16. doi:10.4018/IJSDA.201601010

    Google Scholar 

  • Pruyt E, Islam T (2015) On generating and exploring the behavior space of complex models. Syst Dyn Rev 31(4):220–249. doi:10.1002/sdr.1544

    Article  Google Scholar 

  • Pruyt E, Auping WL, Kwakkel JH (2015) Ebola in West Africa: model-based exploration of social psychological effects and interventions. Syst Res Behav Sci 32:2–14. doi:10.1002/sres.2329

    Article  Google Scholar 

  • Rahmandad H, Oliva R, Osgood NA (eds) (2015) Analytical methods for dynamic modelers. MIT Press, Cambridge

    Google Scholar 

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

    Google Scholar 

  • Thompson K, Tebbens RJ (2007) Eradication versus control for poliomyelitis: an economic analysis. Lancet 369(9570):1363–1371

    Article  Google Scholar 

  • Vennix JAM (1996) Group model building: facilitating team learning using system dynamics. Wiley, Chichester

    Google Scholar 

  • Yücel G (2012) A novel way to measure (dis)similarity between model behaviors based on dynamic pattern features. In: Proceedings of the 30th international conference of the system dynamics society, St.-Gallen, CH, 22–26 July 2012

    Google Scholar 

  • Yücel G, Barlas Y (2011) Automated parameter specification in dynamic feedback models based on behavior pattern features. Syst Dyn Rev 27(2):195–215

    Article  Google Scholar 

Download references

Acknowledgements

This simulation project was conducted in partnership with the Munich Security Conference. The results were shown at the Munich Strategy Forum in November 2015 in Schloss Elmau (Germany). We greatly acknowledge Datenflug for the visualizations developed for the workshop. Finally, we want to thank Jan H. Kwakkel and Willem L. Auping for their contributions to TU Delft’s EMA Workbench.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Erik Pruyt .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this chapter

Cite this chapter

Pruyt, E., Islam, T., Arzt, T. (2018). On the Spot and Map: Interactive Model-Based Policy Support Under Deep Uncertainty. In: Gil-Garcia, J., Pardo, T., Luna-Reyes, L. (eds) Policy Analytics, Modelling, and Informatics. Public Administration and Information Technology, vol 25. Springer, Cham. https://doi.org/10.1007/978-3-319-61762-6_14

Download citation

Publish with us

Policies and ethics