Accounting for Complexity: Thinking With Idealisations, Models, and Data

Kathryne Metcalf
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引用次数: 0

Abstract

What does it mean to call something complex? This Review essay describes three recent books which take up complex problems and the problem of complexity: philosopher Angela Potochnik’s Idealization and the Aims of Science (2017); science and technology studies (STS) scholar Nicole Nelson’s Model Behavior: Animal Experiments, Complexity, and the Genetics of Psychiatric Disorders (2018); and historian of science Bruno Strasser’s Collecting Experiments: Making Big Data Biology (2019). Taken together, these works lay out a refreshed analytic vocabulary and set of guiding concerns for thinking about what complexity is and does in medical research, and how complexity mediates public participation in science and medicine.
核算复杂性:用理想化、模型和数据思考
什么叫复杂?这篇评论文章描述了最近三本探讨复杂问题和复杂性问题的书:哲学家安吉拉·波托奇尼克的《理想化和科学的目标》(2017);科学技术研究(STS)学者Nicole Nelson的《模式行为:动物实验、复杂性和精神疾病的遗传学》(2018);以及科学史学家布鲁诺·斯特拉塞的《收集实验:制作大数据生物学》(2019)。综合起来,这些作品为思考复杂性是什么以及在医学研究中做了什么,以及复杂性如何调解公众对科学和医学的参与,提供了一套全新的分析词汇和指导关注。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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