核算复杂性:用理想化、模型和数据思考

Kathryne Metcalf
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引用次数: 0

摘要

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