Epistemic Landscapes, Optimal Search, and the Division of Cognitive Labor
Epistemic Landscapes, Optimal Search, and the Division of Cognitive Labor
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DOI：
10.1086/681766
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发表时间：
2015-07-01
影响因子：
1.7
通讯作者：
Thompson, Christopher
中科院分区：
文献类型：
--
作者：
Alexander, Jason McKenzie;Himmelreich, Johannes;Thompson, Christopher
这篇文章探讨了关于科学家寻求知识的两个问题。首先，哪种搜索策略能有效地产生发现？第二，搜索策略多样化是否有利？我们与Weisberg和Muldoon在《认知景观和认知劳动分工》（本杂志，2009年）中提出，在第一个问题上，刻意寻求新颖研究方法的搜索策略不一定是最佳的。关于第二个问题，我们认为他们没有证明认知劳动分工存在认识论上的原因，并指出了导致他们得出结论的错误。此外，我们推广了认知景观模型，表明人们应该对认知复杂环境中社会学习的好处持怀疑态度。
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.