Skip to main content

Policy Making and Modelling in a Complex World

Part of the Public Administration and Information Technology book series (PAIT,volume 10)


In this chapter, we discuss the consequences of complexity in the real world together with some meaningful ways of understanding and managing such situations. The implications of such complexity are that many social systems are unpredictable by nature, especially when in the presence of structural change (transitions). We shortly discuss the problems arising from a too-narrow focus on quantification in managing complex systems. We criticise some of the approaches that ignore these difficulties and pretend to predict using simplistic models. However, lack of predictability does not automatically imply a lack of managerial possibilities. We will discuss how some insights and tools from “complexity science” can help with such management. Managing a complex system requires a good understanding of the dynamics of the system in question—to know, before they occur, some of the real possibilities that might occur and be ready so they can be reacted to as responsively as possible. Agent-based simulation will be discussed as a tool that is suitable for this task, and its particular strengths and weaknesses for this are discussed.


  • Regime Shift
  • Complex Adaptive System
  • Policy Model
  • Instrumental Approach
  • Double Pendulum

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

This is a preview of subscription content, access via your institution.

Buying options

USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
USD   149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD   199.99
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

Learn about institutional subscriptions


  1. 1.

    Obviously predictions can always be made, but it has been proved analytically that the predictive value of models is zero in these cases.

  2. 2.

    Even if one could measure them with extreme accuracy, there would never be complete accuracy due to the uncertainty theorem of Heisenberg (1927).

  3. 3.

    Subsequent elaborations of this model have tried to make the relationship to what is observed more direct, but the original model, however visually suggestive, was not related to any data.

  4. 4.

    Even if, as in statistics, they are being precise about variation and levels of uncertainty of other numbers.

  5. 5.

    This apparent central tendency might be merely the result of the way data are extracted from the model and the assumptions built into the model rather than anything that represents the fundamental behaviour being modelled.

  6. 6.

    For an account of actual forecasting and its reality, see Silver (2012).

  7. 7.

    Or other null model, such as “what happened last time” or “no change”.

  8. 8.

    See mailing list SIMSOC@JISCMAIL.AC.UK. Mail distributed by Nigel Gilbert on December 14, 2013, subject: ABMs in action: second summary.

  9. 9.

    Descartes’ mechanistic worldview implies that the universe works like a clockwork, and prediction is possible when one has knowledge of all the wheels, gears, and levers of the clockwork. In policy this translates as the viable society.


  • Boettiger C, Hastings A (2012) Quantifying limits to detection of early warning for critical transitions. J R Soc Interface 9(75):2527–2539

    CrossRef  Google Scholar 

  • Campbell DT (1960) Blind variation and selective retention in creative thought as in other knowledge processes. Psychol Rev 67:380–400

    CrossRef  Google Scholar 

  • Dai L, Vorselen D et al (2012) Generic indicators for loss of resilience before a tipping point leading to population collapse. Science 336(6085):1175–1177

    CrossRef  Google Scholar 

  • Dakos V, Carpenter RA et al (2012) Methods for detecting early warnings of critical transitions in time series illustrated using simulated ecological data. PLoS ONE 7(7) e41010

    CrossRef  Google Scholar 

  • Edmonds B (2000) The purpose and place of formal systems in the development of science. CPM report 00–75, MMU, UK (

  • Edmonds B (2001) The use of models—making MABS actually work. In: Moss S, Davidsson P (eds) Multi agent based simulation. Lecture Notes in Artificial Intelligence 1979. Springer, Berlin, pp 15–32

    Google Scholar 

  • Edmonds B (2010) Bootstrapping knowledge about social phenomena using simulation models. J Artif Soc Soc Simul 13(1):8 (

  • Edmonds B (2013) Complexity and context-dependency. Found Sci 18(4):745–755. doi:10.1007/s10699-012-9303-x

    CrossRef  Google Scholar 

  • Galán JM, Izquierdo LR, Izquierdo SS, Santos JI, del Olmo R, López-Paredes A, Edmonds B (2009) Errors and artefacts in agent-based modelling. J Artif Soc Soc Simul 12(1):1 (

  • Heisenberg W (1927) Ueber den anschaulichenInhalt der quantentheoretischen. Kinematik and Mechanik Zeitschriftfür Physik 43:172–198. English translation in (Wheeler and Zurek, 1983), pp 62–84

    CrossRef  Google Scholar 

  • May RM (1976) Simple mathematical models with very complicated dynamics. Nature 261(5560):459–467

    CrossRef  Google Scholar 

  • Moss S (2002) Policy analysis from first principles. Proc US Natl Acad Sci 99(Suppl 3):7267–7274

    CrossRef  Google Scholar 

  • Scheffer et al (2009) Early warnings of critical transitions. Nature 461:53–59

    CrossRef  Google Scholar 

  • Silver N (2012) The signal and the noise: why so many predictions fail-but some don’t. Penguin, New York

    Google Scholar 

  • Waldherr A, Wijermans N (2013) Communicating social simulation models to sceptical minds. J Artif Soc Soc Simul 16(4):13 (

Download references


This chapter has been written in the context of the eGovPoliNet project. More information can be found on

Author information

Authors and Affiliations


Corresponding author

Correspondence to Wander Jager .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Jager, W., Edmonds, B. (2015). Policy Making and Modelling in a Complex World. In: Janssen, M., Wimmer, M., Deljoo, A. (eds) Policy Practice and Digital Science. Public Administration and Information Technology, vol 10. Springer, Cham.

Download citation