Strategic directions for agent-based modeling: avoiding the YAAWN syndrome

David O’Sullivan, Tom Evans, Steven Manson, Sara Metcalf, Arika Ligmann-Zielinska, Chris Bone

Research output: Contribution to journalArticlepeer-review

78 Scopus citations


In this short communication, we examine how agent-based modeling has become common in land change science and is increasingly used to develop case studies for particular times and places. There is a danger that the research community is missing a prime opportunity to learn broader lessons from the use of agent-based modeling (ABM), or at the very least not sharing these lessons more widely. How do we find an appropriate balance between empirically rich, realistic models and simpler theoretically grounded models? What are appropriate and effective approaches to model evaluation in light of uncertainties not only in model parameters but also in model structure? How can we best explore hybrid model structures that enable us to better understand the dynamics of the systems under study, recognizing that no single approach is best suited to this task? Under what circumstances – in terms of model complexity, model evaluation, and model structure – can ABMs be used most effectively to lead to new insight for stakeholders? We explore these questions in the hope of helping the growing community of land change scientists using models in their research to move from ‘yet another model’ to doing better science with models.

Original languageEnglish (US)
Pages (from-to)177-187
Number of pages11
JournalJournal of Land Use Science
Issue number2
StatePublished - Mar 3 2016

Bibliographical note

Publisher Copyright:
© 2015 Taylor & Francis.


  • agent-based model
  • hybrid model
  • land systems science
  • validation


Dive into the research topics of 'Strategic directions for agent-based modeling: avoiding the YAAWN syndrome'. Together they form a unique fingerprint.

Cite this