The complexity of contemporary science far exceeds the physical and cognitive resources of any individual scientist. Because of this, scientific research is almost always tackled by communities of scientists, which vary in size and degree of coordination. In other words, modern science requires the division of cognitive labor. My research on this topic aims to construct new models of the division of cognitive labor. The first part of this project, a collaboration with my former student Ryan Muldoon, involves developing an agent-based computational framework within which specific models can be constructed and specific questions can be investigated. I call this framework the epistemic landscape approach, because we construct a space in which points correspond to research approaches, each of which has an epistemic payoff. We can think of this space as a landscape not initially known to scientists, but whose topography is learned by scientific exploration. Questions about the division of cognitive labor can be posed in terms of the distribution and coordination between scientists distributed through the landscape.
Another component of this project critically evaluates earlier literature on the topic. Specifically, Ryan Muldoon and I have criticized Philip Kitcher’s and Michael Strevens’ highly influential models of the division of cognitive labor and argue that small weakenings of two key assumptions lead to qualitatively different behavior. is analysis suggests that the models adopted by Kitcher and Strevens are not sufficiently robust to support their conclusions about the ability of the priority rule to ensure the optimum division of cognitive labor.