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近年来“大数据”崛起,成为一种改变商业、科学和社会的“破坏性力量”。而对大数据及其价值,人们既抱有极大的热情和期盼,也存在质疑。在笔者看来,这种质疑源于对大数据利用目的的根本性混淆:是更好的科学,还是更好的工程?质疑者对摈弃传统数据采集、分析方法,混淆相关、因果关系,建构单一解释力模型等做法提出了批评。然而,基于发展社会科学的考量,这些观点又有存在的价值。但笔者仍然认为,如果要利用大数据革新计算方法以改善效率,所设计的测量指标就应该是客观、公正的。那些听起来科学、有用的说法不一定能够优化工程工艺。厘清了科学与工程之间的异同,也就能够明白并解决围绕大数据产生的诸种论争,从而有助于设计测量贡献率的指标。
In recent years, the rise of “big data” has become a “destructive power” that changes business, science and society. As for big data and its value, people have both great enthusiasm and hope, as well as questions. In the author’s opinion, this challenge stems from fundamental confusion over the purpose of big data use: is it better science or better engineering? The skeptics reject the traditional data collection, analytics, confusion, causation, construction Single explanatory model and other practices put forward criticism. However, based on considerations of developing social sciences, these views have their value in existence. However, the author still believes that if we want to take advantage of big data innovation to improve efficiency, the designed measure should be objective and fair. Those that seem scientifically sound and useful do not necessarily optimize the engineering process. By clarifying the similarities and differences between science and engineering, we can understand and solve various controversies surrounding big data, which helps to design indicators that measure the contribution rate.