Choosing between prediction and explanation in geological engineering: lessons from psychology

Amichai Mitelman, Beverly Yang, Davide Elmo, Yahel Giat

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

In their highly influential paper, Yarkoni, Tal, and Jacob Westfall. 2017. “Choosing Prediction over Explanation in Psychology: Lessons from Machine Learning.” Perspectives on Psychological Science 12 (6):1100–1122. https://doi.org/10.1177/1745691617693393 the authors highlight difficulties in traditional explanatory research in the field of psychology and argue in favour of novel data-driven science. By applying machine-learning methods to large data sets, predictive power has been shown to increase significantly. Geological engineers are responsible for a wide range of applications, including the design of tunnels, dams, foundations, and mines. While the field of geological engineering stands on solid mechanistic grounds, we argue that its predictive aspect aligns more closely with psychology than other mechanistic sciences. We therefore propose a paradigm shift in geological engineering research towards a prediction-centric approach. Potentially, this could enhance cost-effectiveness in structural design and lead to substantial societal savings.

Original languageEnglish
Pages (from-to)651-668
Number of pages18
JournalInterdisciplinary Science Reviews
Volume48
Issue number4
DOIs
StatePublished - 2023

Keywords

  • Data science
  • Explanatory models
  • Factor of safety
  • Geological engineering
  • Machine-learning
  • Prediction
  • Psychology
  • Rock mechanics

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