In contrast, environmental models work to create more accurate depictions of urban environmental problems, their ramifications within the larger urban system and methods of mitigation. These models help planners predict future actions as simulations show of individuals “should” act based on a specific stimuli. Typically game theory, multi-agent systems and Monte Carlo simulations have been used to simulate the actions of individuals within a system. Agent based models are focused on individual decision making and the impact that decision can have on the whole system. Agent based modeling is one of the more popular forms of computation modeling in modern urban studies. More modern approaches to urban modeling have become more quantitative and computational as technology has improved. While Hoyt and Harris and Ullman started to focus on economic functions in their models, their models still maintained a strong grounding in residential location. Most of these models focus on residential location as the foundation for the zones in their models. The use of models to explain urban land use also extended to international comparative studies, to include Griffin and Ford’s model of Latin American city structures, and McGee’s model of Southeast Asian Cities.
Historically, the focus was on urban land use, starting with Burgess and Park’s concentric zone model, moving to Hoyt’s sectoral model and finally to Harris and Ulman’s multiple nuclei model. Urban scholars and practitioners benefit from the use of urban models, especially those interested in topics such as: environmental impacts, consumer behavior, disaster response and transportation planning. As cities continue to grow and become more complex, it becomes essential to create abstractions of reality in order to help predict how certain actions will influence the functioning of the city. Urban models are abstractions of reality that attempt to explain and predict urban spatial patterns and the functions of cities in a simplified manner.
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