Abstract
Scientific research is almost always conducted by communities of scientists of varying size and complexity. Such communities are effective, in part, because they divide their cognitive labor: not every scientist works on the same project. Philip Kitcher and Michael Strevens have pioneered efforts to understand this division of cognitive labor by proposing models of how scientists make decisions about which project to work on. For such models to be useful, they must be simple enough for us to understand their dynamics, but faithful enough to reality that we can use them to analyze real scientific communities. To satisfy the first requirement, we must employ idealizations to simplify the model. The second requirement demands that these idealizations not be so extreme that we lose the ability to describe real-world phenomena. This paper investigates the status of the assumptions that Kitcher and Strevens make in their models, by first inquiring whether they are reasonable representations of reality, and then by checking the models’ robustness against weakenings of these assumptions. To do this, we first argue against the reality of the assumptions, and then develop a series of agent-based simulations to systematically test their effects on model outcomes. We find that the models are not robust against weakenings of these idealizations. In fact we find that under certain conditions, this can lead to the model predicting outcomes that are qualitatively opposite of the original model outcomes.
Similar content being viewed by others
References
Brock B., Durlaf S. (1999) A formal model of theory choice in science. Economic Theory XIV: 113–130
Kitcher P. (1990) The division of cognitive labor. Journal of Philosophy LXXXVII(1): 5–22
Kitcher P. (1993) The advancement of science. Oxford University Press, New York
Miller J. H., Page S. (2007) Complex adaptive systems. Princeton University Press, Princeton
Newman M. E. J. (2001) The structure of scientific collaboration networks. Proceedings of the National Academy of Science USA 98: 404–409
Schelling T. (1978) Micromotives and macrobehavior. Norton, New York
Strevens M. (2003) The role of the priority rule in science. Journal of Philosophy C 2: 55–79
Weisberg M., Muldoon R. (2009) Epistemic landscapes and the division of cognitive labor. Philosophy of Science 76(2): 225–252
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Muldoon, R., Weisberg, M. Robustness and idealization in models of cognitive labor. Synthese 183, 161–174 (2011). https://doi.org/10.1007/s11229-010-9757-8
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11229-010-9757-8