ALGORITHMIC INDUCTION THROUGH MACHINE LEARNING: USING PREDICTIONS TO THEORIZE

33 Pages Posted: 19 Mar 2018 Last revised: 24 Aug 2018

Date Written: August 24, 2018

Abstract

Machine learning (ML) algorithms are rapidly advancing research across many fields of social science, including economics, marketing, and management information systems. Management and organization studies are yet to fully leverage these methods beyond application to coding unstructured data. This may be in part due to the distaste for “predictions without causal explanations” that ML algorithms are known to produce. Yet, we argue, precisely because of this property, ML techniques can be extremely useful in theory construction by separating the two key components of inductive theorizing- pattern detection and pattern explanation. ML can facilitate “algorithmic induction”— formally specifiable operations aiding inductive inference that yields identical (or highly similar) conclusions when applied by different observers to the same data. We propose that algorithmic induction is valuable for researchers interested in using quantitative data to both develop and test theories in a transparent and reproducible manner.

Keywords: Machine learning, algorithmic induction, theory building

Suggested Citation

Puranam, Phanish and Shrestha, Yash Raj and He, Vivianna Fang and von Krogh, Georg, ALGORITHMIC INDUCTION THROUGH MACHINE LEARNING: USING PREDICTIONS TO THEORIZE (August 24, 2018). INSEAD Working Paper No. 2018/11/STR. Available at SSRN: https://ssrn.com/abstract=3140617 or http://dx.doi.org/10.2139/ssrn.3140617

Phanish Puranam

INSEAD ( email )

1 Ayer Rajah Avenue
Singapore, 138676
Singapore

HOME PAGE: http://www.insead.edu/facultyresearch/faculty/profiles/ppuranam/

Yash Raj Shrestha

ETH Zurich ( email )

Rämistrasse 101
ZUE F7
Zürich, 8092
Switzerland

Vivianna Fang He (Contact Author)

ETH Zurich ( email )

Rämistrasse 101
ZUE F7
Zürich, 8092
Switzerland

Georg Von Krogh

ETH Zurich ( email )

D-MTEC, SMI, WEV J 411
Weinbergstrasse 56/58
Zurich, 8092
Switzerland
+41 44 632 88 50 (Phone)

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