O’Sullivan posits that the complex nature of ABMs violates “one of the most common tenets of practical science, the imperative to prefer simplicity over elaboration” (p. 546). As stated in the post “ABM and Toolmaking,” many issues arise due to this complexity. It is difficult to image, however, a simplified model of human processes; there are so many important variables to take into consideration. But in developing these models—especially when they are used not just to understand a phenomena but also to predict—what happens when the predictions are incorrect? Perhaps this is an issue that Bonabeau does not delve into enough: it is possible that the over-simplification of a system or the inability to consider enough variables can lead to error and uncertainty. In class we discussed the Turcot interchange and the designing of the freeway system in general. Computer models, which may have not taken into consideration enough transportation demand and urban growth variables, may have let to inappropriate policy and planning decisions.
Again, this may be a problem that can be solved through technological advancements. Revisiting the freeway example, we can now model how expanded roads quickly reach capacity. But maybe there is another issue at play, that of scale. How can a model that represents the actions of agents be simplified and, therefore, more accurate? Bonabeau uses the very small-scale example of the fire escape simulation to assert the benefits of ABM. In this example, while there are relatively few and homogenous actors who all have a single aim, the idea to construct a column in front of the exit would likely not have been arrived at without the aid of ABM. An alternative way of problem solving and perceiving a process, in other words, was enabled. Therefore, perhaps the effectiveness of ABM lies in fully understanding its limitations.
According to O’Sullivan, “while simple, abstract models can be useful for exploring the implications of theories under particular assumptions, they cannot establish the truth of those theories” (p. 546). So, it’s great that we can now determine the optimal location of a column, but the current nature of ABM will mean that fully understanding a complex social phenomena will be riddled with uncertainty. Keeping this important aspect in mind will arguably be key to the success and future development of ABM.