Epistemic landscapes, optimal search and the division of cognitive labor

Publication Year:
2014
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Repository URL:
http://philsci-archive.pitt.edu/id/eprint/11187
Author(s):
Himmelreich, Johannes, Alexander, J. McKenzie, Thompson, Christopher
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preprint description
This paper examines two questions about scientists’ search for knowledge. First, which search strategies generate discoveries effectively? Second, is it advantageous to diversify search strategies? We argue pace Weisberg and Muldoon (2009) that, on the first question, a search strategy that deliberately seeks novel research approaches need not be optimal. On the second question, we argue they have not shown epistemic reasons exist for the division of cognitive labor, identifying the errors that led to their conclusions. Furthermore, we generalize the epistemic landscape model, showing that one should be skeptical about the benefits of social learning in epistemically complex environments.

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