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Epistemic Landscapes, Optimal Search, and the Division of Cognitive Labor

Jason McKenzie Alexander, Johannes Himmelreich and Christopher Thompson
Philosophy of Science
Vol. 82, No. 3 (July 2015), pp. 424-453
DOI: 10.1086/681766
Stable URL: http://www.jstor.org/stable/10.1086/681766
Page Count: 30
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Epistemic Landscapes, Optimal Search, and the Division of Cognitive Labor
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Abstract

This article 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, “Epistemic Landscapes and the Division of Cognitive Labor” (this journal, 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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