Multiple, differing agent-based models, being “simulation[s] that [use] agents to represent actors in the real world,” (O’Sullivan, 542), can produce results “that would match with empirical observations equally well” (546). Many basic, abstract models can reach the same conclusion, but how well can these be applied to specific occurrences in different locations?
In geography we know about the importance of locational specificity; the particular environmental, social, economic, political etc. factors that influence—and ultimately shape—the place. Abstract ABMs disregard any detail of real world situations, and therefore the emergent phenomena is incredibly abstract itself—making it impractical for it be applied to a wide variety of different places. Things occurring in one space cannot be blindly applied to a different one without acknowledging the different factors which comprise and inhabit the space. The substitutability of space is an inherent problem.
Abstract ABMs may not be seeking to generate results explaining phenomena for a particular location, but then what is their use in GIScience? Without incorporating any specific spatial entities, micro-level factors cannot be discerned and, ultimately, the phenomena cannot be explained. In the real world, the local has a great influence on agent behavior and interactions which is being overlooked in abstract ABMs. ABMs focus on the heterogeneity of individual agents, but basic models don’t consider the heterogeneity of different environments and how it influences agent behavior.
From a GIScience perspective, it seems that basic models don’t hold much weight in the explanation of phenomena for specific locations. Knowing that circumstances change in different areas, the behaviour depicted in abstract ABMs is incredibly superficial without model concerns for the particular.
O’Sullivan, David. (2008). “Geographical information science: agent-based models.” Progress in Human Geography, 32(4) 541-550.
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Certainly you point out many of the issues with simple ABMs – they are thought experiments and an opportunity to formalize theories and explore new territory. What about a model like RB Sim, that uses GIS data as a landscape upon which agents interact?