In this paper we aim to give an analysis and cognitive rationalization of a common practice or strategy of modeling in systems biology known as a middle-out modeling strategy. The strategy in the cases we look at is facilitated through the construction of what can be called mesoscopic models. Many models built in computational systems biology are mesoscopic (midsize) in scale. Such models lack the sufficient fidelity to serve as robust predictors of the behaviors of complex biological systems, one of the signature goals of the field. This puts some pressure on the field to provide reasons for why and how these practices are warranted despite not meeting the stated goals of the field. Using the results of ethnographic study of problem-solving practices in systems biology, we aim to examine the middle-out strategy and mesoscopic modeling in detail and to show that these practices are rational responses to complex problem solving tasks on cognitive grounds in particular. However making this claim requires us to update the standard notion of bounded rationality to take account of how human cognition is coupled to computation in these contexts. Our account fleshes out the idea that has been raised by some philosophers on the “hybrid” nature of computational modeling and simulation. What we call “coupling” both extends modelers’ capacities to handle complex systems, but also produces various cognitive and computational constraints which need to be taken into account in any computational problem solving strategy seeking to maintain insight and control over the models produced.
|Journal||Studies in history and philosophy of science. Part C: Studies in history and philosophy of biological and biomedical sciences|
|Early online date||14 Aug 2019|
|Publication status||Published - Dec 2019|
MacLeod, M. A. J., & Nersessian, N. J. (2019). Mesoscopic modeling as a cognitive strategy for handling complex biological systems. Studies in history and philosophy of science. Part C: Studies in history and philosophy of biological and biomedical sciences, 78, . https://doi.org/10.1016/j.shpsc.2019.101201