Abstract
The use of computational models has proliferated in the last few decades due to the practical benefits that they offer in terms of understanding complex systems. However, the process of developing a computational model is not only shaped by scientific and practical concerns. Modelers must also navigate the institutional structures in which the models are constructed and implemented. In the process, they must often draw upon existing or create new social relationships that can, themselves, alter the institutional structures they are attempting to navigate. In this article, I use an ethnographic approach to examine three examples in which the process of building and implementing a computational model was constrained by institutional factors and the strategies the modelers used to navigate them. The research was conducted with computational modelers at the Chesapeake Bay Program who are involved in constructing the Chesapeake Bay modeling system for nutrient management in the watershed. Modelers in this context had to: build relationships with the broader scientific community to reinforce the “believability” of the model, draw upon institutional relationships in order to work around limitations on data-sharing between organizations, and manage differing incentive structures to motivate and coordinate the research needed to complete the model
Notes
1 Zhang, Hirsch, and Ball (Citation2016) used a statistical model known as weighted regressions on time, discharge, and season, which can estimate sediment and nutrient fluxes from existing data. Using this method, they were able to estimate the fluxes of sediment through the Conowingo reservoir and dam. Their research showed that nutrient storage in the reservoir had reached its maximum.
2 Sparrow is another computational model that can track water quality, which is built and maintained by the USGS.