从经验归纳到数据归纳:特征、机制与意义

From Empirical Induction to Datal Induction: Characteristics,Mechanism and Significance

基于以大量、多样和快速为特征的大数据归纳,可以实现传统经验归纳无法替代和完成的工作,导致了一种极端的数据主义观点,以为人们已经不用再寻找现象背后的原因,不再需要科学的理论或模型了。本文旨在厘清大数据方法的意义及其与现实世界的关系,厘清相关性与因果性的关系等,以理性地看待大数据带来的挑战。

Based on the big data which have the characteristics of Volume,Volocity and Variety,the Big Datal Induction can finish the works which traditional empirical induction cannot complete. This leads to an extreme view that people need not to look for reasons hiding behind the phenomena,and need not the scientific theories or models. The purpose of this paper is to clarify the significance of the method of big data and its relationship with the real world,to clarify the relationship between correlation and causality,in order to comprehend rationally the challenges coming from BDI.