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Jason McKenzie Alexander, Johannes Himmelreich & Christopher Thompson (2015). Epistemic Landscapes, Optimal Search, and the Division of Cognitive Labor.

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  1.  4
    The Division of Cognitive Labor: Two Missing Dimensions of the Debate.Baptiste Bedessem - 2018 - European Journal for Philosophy of Science 9 (1):3.
    The question of the division of cognitive labor has given rise to various models characterizing the way scientists should distribute their efforts. These models often consider the scientific community as a self-governed sphere constituted by rational agents making choices on the basis of fixed rules. Such models have recently been criticized for not taking into account the real mechanisms of science funding. Hence, the question of the utility of the DCL models in guiding science policy remains an open one. In (...)
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    What Is the Epistemic Function of Highly Idealized Agent-Based Models of Scientific Inquiry?Daniel Frey & Dunja Šešelja - 2018 - Philosophy of the Social Sciences 48 (4):407-433.
    In this paper we examine the epistemic value of highly idealized agent-based models of social aspects of scientific inquiry. On the one hand, we argue that taking the results of such simulations as informative of actual scientific inquiry is unwarranted, at least for the class of models proposed in recent literature. Moreover, we argue that a weaker approach, which takes these models as providing only “how-possibly” explanations, does not help to improve their epistemic value. On the other hand, we suggest (...)
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    Centralized Funding and Epistemic Exploration.Shahar Avin - 2017 - British Journal for the Philosophy of Science:axx059.
    Computer simulation of an epistemic landscape model, modified to include explicit representation of a centralized funding body, show the method of funding allocation has significant effects on communal trade-off between exploration and exploitation, with consequences for the community’s ability to generate significant truths. The results show this effect is contextual, and depends on the size of the landscape being explored, with funding that includes explicit random allocation performing significantly better than peer-review on large landscapes. The paper proposes a way of (...)
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