Question 1: What potential new forms of knowledge can spatial simulation provide to your discipline, and more importantly, to your specific graduate research topic?
The discipline of geography has drawn on spatial simulation models over the past few decades, but the utilization of these models has been unevenly distributed through the sub-disciplines. A great deal more focus has been put on spatial simulation within physical and socio-environmental sub-disciplines than within human geography (with urban planning being a notable exception). Large swathes of human geography remain firmly ensconced in what O’Sullivan and Perry typify as verbally described conceptual models (6). Due to its dominance, the opportunities and limitations of this approach have come to characterize much of the work done in human geography. Particularly for work that draws heavily on the writings of social theorists such as Marx, Foucault, and so forth, even basic graphical representations such as maps and diagrams can be in short supply. While written description allows for a level of conceptual flexibility that is difficult to capture with static graphics or mathematical representations, this flexibility sometimes comes at the cost of intellectual clarity.
Consequently, spatial simulations are a relatively under-utilized form of knowledge production in the sub-disciplines of human geography within which I work (critical political and economic geography). I am particularly interested in their capacity to instantiate and explore alternative models of economic development that have heretofore primarily been described and understood through purely verbal conceptual representations. [alsdfh]
O’Sullivan and Perry note that the social sciences often suffer from problems when attempting to empirically analyze complex social systems (9). Much Marxist economic analysis, for example, hinges on the concept of socially necessary labor time (SNLT): the amount of labor-time necessary within a given society to produce a certain commodity; a value that monetary prices approximate but are not equivalent to. SNLT, in other words, is vital yet impossible to measure concretely: how to confirm or falsify any theory regarding such a value? A spatial simulation offers a novel way to explore this theory that do not depend on empirical observation: as the elements of the model are directly accessible by the modeler, the flow of SNLT (and its relation to monetary price) through an economy can be tracked directly. As noted by O’Sullivan and Perry, this kind of model can give researchers “a tool to think with” regarding SNLT. In addition to deepening understanding, it could also open new avenues of data collection by illuminating unanticipated connections between SNLT and more accessible variables (ibid 13).
My own research focuses on how the interactions of individuals and infrastructure lead to the emergence of national territory, or a culturally-specific spatial entity. This is a complex, emergent system where individual choices play a critical role in the outcome. Spatial simulation is well-suited to explore this kind of system for a number of reasons: empirical investigation is difficult, space plays an important role, and it is driven (to a considerable extent) by bottom up processes. O’Sullivan and Perry write primarily about the first of these: models are of particular use when experimental methods are closed off. By articulating my conceptual framework into an agent-based spatial simulation, I can explore the dynamics of the system in a way that is impossible to replicate in the real world.
O’Sullivan, David., and Perry, George L. W. Spatial Simulation Exploring Pattern and Process. Hoboken: Wiley, 2013.