Using agent-based models for prediction in complex and wicked systems

J. Gareth Polhill, Matthew Hare, Tom Bauermann, David Enrique Anzola Pinzon, Erika Palmer, Doug Salt, Patrycja Antosz

Research output: Contribution to journalResearch Articlepeer-review

13 Scopus citations


This paper uses two thought experiments to argue that the complexity of the systems to which agent-based models (ABMs) are often applied is not the central source of difficulties ABMs have with prediction. We define various levels of predictability, and argue that insofar as path-dependency is a necessary attribute of a complex system, ruling out states of the system means that there is at least the potential to say something useful. ‘Wickedness’ is argued to be a more significant challenge to prediction than complexity. Critically, however, neither complexity nor wickedness makes prediction theoretically impossible in the sense of being formally undecidable computationally-speaking: intractable being the more apt term given the exponential sizes of the spaces being searched. However, endogenous ontological novelty in wicked systems is shown to render prediction futile beyond the immediately short term.
Original languageSpanish (Colombia)
Article number3
Pages (from-to)1-24
Number of pages25
Issue number3
StatePublished - 2021

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