TY - JOUR
T1 - Strategic directions for agent-based modeling
T2 - avoiding the YAAWN syndrome
AU - O’Sullivan, David
AU - Evans, Tom
AU - Manson, Steven
AU - Metcalf, Sara
AU - Ligmann-Zielinska, Arika
AU - Bone, Chris
N1 - Publisher Copyright:
© 2015 Taylor & Francis.
PY - 2016/3/3
Y1 - 2016/3/3
N2 - 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.
AB - 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.
KW - agent-based model
KW - hybrid model
KW - land systems science
KW - validation
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UR - http://www.scopus.com/inward/citedby.url?scp=84960195535&partnerID=8YFLogxK
U2 - 10.1080/1747423X.2015.1030463
DO - 10.1080/1747423X.2015.1030463
M3 - Article
C2 - 27158257
AN - SCOPUS:84960195535
SN - 1747-423X
VL - 11
SP - 177
EP - 187
JO - Journal of Land Use Science
JF - Journal of Land Use Science
IS - 2
ER -