Policy Making and Modelling in a Complex World

  • Wander JagerEmail author
  • Bruce Edmonds
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 chapter has been written in the context of the eGovPoliNet project. More information can be found on


  1. Boettiger C, Hastings A (2012) Quantifying limits to detection of early warning for critical transitions. J R Soc Interface 9(75):2527–2539CrossRefGoogle Scholar
  2. Campbell DT (1960) Blind variation and selective retention in creative thought as in other knowledge processes. Psychol Rev 67:380–400CrossRefGoogle Scholar
  3. Dai L, Vorselen D et al (2012) Generic indicators for loss of resilience before a tipping point leading to population collapse. Science 336(6085):1175–1177CrossRefGoogle Scholar
  4. 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) e41010CrossRefGoogle Scholar
  5. Edmonds B (2000) The purpose and place of formal systems in the development of science. CPM report 00–75, MMU, UK (
  6. 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 15–32Google Scholar
  7. Edmonds B (2010) Bootstrapping knowledge about social phenomena using simulation models. J Artif Soc Soc Simul 13(1):8 (
  8. Edmonds B (2013) Complexity and context-dependency. Found Sci 18(4):745–755. doi:10.1007/s10699-012-9303-xCrossRefGoogle Scholar
  9. 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 (
  10. Heisenberg W (1927) Ueber den anschaulichenInhalt der quantentheoretischen. Kinematik and Mechanik Zeitschriftfür Physik 43:172–198. English translation in (Wheeler and Zurek, 1983), pp 62–84CrossRefGoogle Scholar
  11. May RM (1976) Simple mathematical models with very complicated dynamics. Nature 261(5560):459–467CrossRefGoogle Scholar
  12. Moss S (2002) Policy analysis from first principles. Proc US Natl Acad Sci 99(Suppl 3):7267–7274CrossRefGoogle Scholar
  13. Scheffer et al (2009) Early warnings of critical transitions. Nature 461:53–59CrossRefGoogle Scholar
  14. Silver N (2012) The signal and the noise: why so many predictions fail-but some don’t. Penguin, New YorkGoogle Scholar
  15. Waldherr A, Wijermans N (2013) Communicating social simulation models to sceptical minds. J Artif Soc Soc Simul 16(4):13 (

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Groningen Center of Social Complexity StudiesUniversity GroningenGroningenThe Netherlands
  2. 2.Manchester Metropolitan UniversityManchesterUK

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