The enthusiasm of adopting the ABM approach for geospatial modeling is curtailed by some important limitations. Although common to all modeling techniques, one issue relates to the purpose of the model; a model is only as useful as the purpose for which it was constructed. A model has to be built at the right level of description for every phenomenon, judiciously using the right amount of detail for the model to serve its purpose (Couclelis, 2002). This remains more of an art than a science (Axelrod, 2007). The nature of the system being modeled is another consideration. For example, a system based on human beings will involve agents with potentially irrational behavior, subjective choices, and complex psychology. These factors are difficult to quantify, calibrate, and sometimes justify. This complicates the implementation and development of a model, as well as the interpretation of the simulation output. However, the fundamental motivation for modeling arises from a lack of full access to data relating to a phenomenon of interest. Often, the target itself is neither well understood nor easy to access.
The development of agent-based models offers a means to increase the utility of simulation models, by closely tailoring the model and subsequent analysis to the needs of end users. In particular, the often visual communication provided by spatially explicit models (especially those coupled with GIS) can be effective at depicting formal model results to a wide range of users. Nevertheless, a model’s output must be interpreted appropriately. Varying degrees of accuracy and completeness in the model inputs determine whether the output should be used purely for qualitative insight, or quantitative forecasting. Section 8.2.7, Explanation or prediction?, reviews the purpose of different ABM approaches in more detail.
By their very definition, agent-based models consider systems at a disaggregated level. This level of detail involves the description of potentially many agent attributes and behaviors, and their interaction with an environment. The only way to treat this type of problem in agent computing is through multiple runs, systematically varying initial conditions or parameters in order to assess the robustness of results. There is a practical upper limit to the size of the parameter space that can be checked for robustness, and this process can be extremely computationally intensive, thus time consuming. Although computing power is increasing rapidly, the high computational requirement of ABM remains a limitation when modeling large systems. Furthermore, agent-based models can be more difficult to analyze, understand and communicate than traditional analytical/ mathematical models (Grimm, 1999), as it is difficult to provide detailed descriptions of the inner workings of such models.
Critics of complexity theory point out that the wide variety of surprising behavior exhibited by mathematical and computational models is rarely found in the real-world. In particular, agent-based models are very sensitive to initial conditions and to small variations in interaction rules. Consequently, modelers of complex systems are never likely to enjoy the intellectual comfort of laws. Despite this, and the other limitations that have been highlighted, ABM is a very useful tool for exploring systems that exhibit complex behavior. Complexity theory has brought awareness of the subtle, diverse, and interconnected facets common to many phenomena, and continues to contribute many powerful concepts, modeling approaches and techniques.