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
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Obviously predictions can always be made, but it has been proved analytically that the predictive value of models is zero in these cases.
Even if one could measure them with extreme accuracy, there would never be complete accuracy due to the uncertainty theorem of Heisenberg (1927).
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.
Even if, as in statistics, they are being precise about variation and levels of uncertainty of other numbers.
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.
For an account of actual forecasting and its reality, see Silver (2012).
Or other null model, such as “what happened last time” or “no change”.
See mailing list SIMSOC@JISCMAIL.AC.UK. Mail distributed by Nigel Gilbert on December 14, 2013, subject: ABMs in action: second summary.
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.
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This chapter has been written in the context of the eGovPoliNet project. More information can be found on http://www.policy-community.eu/.
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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. https://doi.org/10.1007/978-3-319-12784-2_4
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-12783-5
Online ISBN: 978-3-319-12784-2