Modular, spatial ABMs: Haklay et al., 2001

In the intervening fourteen years since “So go down town” (Hackley et al., 2001) was published, agent-based modeling has, unsurprisingly, been harnessed for an ever-expanding number of applications. In the wake of the late-2000s recession, which appeared to discredit the economistic assumption of equilibrium, influential science journalĀ Nature published an editorial calling for the synthesis of existing ABM techniques into a modular representation of the existing economy. Spatial ABMs (such as the STREETS model) have surfaced in mainstream news as potential predictors of crowd behaviour. Needless to say, Hackley, et al. were on to something very important with the development of their modular, multi-scalar representation of pedestrian behaviour. Avoidable catastrophes such as the 2010 Love Parade disaster, in which 21 people were killed by trampling due to a dynamic feedback phenomenon now known as “crowd turbulence,” have provided fodder for the study of the effects of interrelated psychological and physical forces on large crowds.

In general, ABMs appear to be one of the most promising intersections of social science and computer science, due to its ability to model situations of staggering complexity, involving thousands or millions of agents whose dynamic interactions produce highly unpredictable results. Our last discussion about geolocated SNA produced some interesting conjecture about what could be done — for better or worse — with the datasets of Google or Facebook, which contain geolocated information on billions of real individuals. Haklay’s observation that ABM research in the 1990s was hindered by “sufficiently powerful comptuers and suitably rich data sets” points to the potential that this information has to expand human knowledge, as well as to enable much more effective control of human populations.

I would venture that combinations of current-day iterations of modular ABMs like STREETS, combined with these ever-growing, dynamic sources of socioeconomic data, hold the potential to create very well-informed models that capture the dynamism of emergence with the power of immense and ever-evolving observations of real people. With so much relevant research now being conducted behind closed doors at intelligence agencies and in corporations whose business is selling data, the current and future possibilities of spatial ABMs remain both fascinating and frightening.



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