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Epistemic landscapes, optimal search and the division of cognitive labor

Himmelreich, Johannes and Alexander, J. McKenzie and Thompson, Christopher (2014) Epistemic landscapes, optimal search and the division of cognitive labor. [Preprint]

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Abstract

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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Item Type: Preprint
Creators:
CreatorsEmailORCID
Himmelreich, Johannesjohannes.himmelreich@gmail.com
Alexander, J. McKenzie
Thompson, Christopher
Keywords: Epistemic division of labor, agent-based modelling, social structure of science, fitness landscapes, social learning, diversity
Subjects: General Issues > Models and Idealization
General Issues > Science and Society
General Issues > Theory Change
Depositing User: Mr Johannes Himmelreich
Date Deposited: 08 Dec 2014 01:45
Last Modified: 08 Dec 2014 01:45
Item ID: 11187
Subjects: General Issues > Models and Idealization
General Issues > Science and Society
General Issues > Theory Change
Date: November 2014
URI: https://philsci-archive-dev.library.pitt.edu/id/eprint/11187

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