Epistemic Landscapes: Models of Cognitive Labor

University Of Pennsylvania, Philadelphia PA

Investigators

Abstract

The PI will use computational models of the social structure of science to study several issues associated with the division of cognitive labor. Those issues include the following. How do individual scientists allocate their cognitive labor given research costs and limited access to information? How does drawing on the cognitive resources of the scientific community enhance or retard the discovery of significant truths? How should the scientific community as a whole optimally allocate its cognitive labor? The notion of the division of cognitive labor may be characterized as follows. Scientists are not lone agents, cut off from the outside world, responding only to information generated in their laboratories. Rather, they make decisions about what to investigate by integrating what they discover for themselves with what they learn from others. They also take into account external factors such as grants, prizes, and prestige. This feedback leads scientists to divide their resources among differing approaches to studying phenomena of interest. While this coordination is neither planned nor explicit, it seems to enhance the ability of scientific communities to discover significant, true things about the world. Although the results of this research project will primarily contribute to the philosophy of science literature, it has three major areas of potential broader impact. First, it will involve training undergraduate students in the use of computational modeling to investigate philosophical problems. Second, while the models developed in this project are not appropriate for making direct policy advice, they can investigate sets of incentives that would change the division of cognitive labor in more epistemically productive ways. Finally, the project will generate materials that can be reformulated for use in secondary and higher education with the goal of making citizens more informed consumers of science, especially in fields such as evolution and climate modeling where critics prey on public misunderstanding of how theories are tested.