Is Infidelity Predictable? Using Explainable Machine Learning to Identify the Most Important Predictors of Infidelity

Laura M. Vowels, Matthew J. Vowels, Kristen P. Mark

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

Abstract

Infidelity can be a disruptive event in a romantic relationship with a devastating impact on both partners’ well-being. Thus, there are benefits to identifying factors that can explain or predict infidelity, but prior research has not utilized methods that would provide the relative importance of each predictor. We used a machine learning algorithm, random forest (a type of interpretable highly non-linear decision tree), to predict in-person and online infidelity across two studies (one individual and one dyadic, N = 1,295). We also used a game theoretic explanation technique, Shapley values, which allowed us to estimate the effect size of each predictor variable on infidelity. The present study showed that infidelity was somewhat predictable overall and interpersonal factors such as relationship satisfaction, love, desire, and relationship length were the most predictive of online and in person infidelity. The results suggest that addressing relationship difficulties early in the relationship may help prevent infidelity.

Original languageEnglish
Pages (from-to)224-237
Number of pages14
JournalJournal of Sex Research
Volume59
Issue number2
DOIs
StatePublished - 2022

Bibliographical note

Publisher Copyright:
© 2021 The Author(s). Published with license by Taylor & Francis Group, LLC.

ASJC Scopus subject areas

  • Gender Studies
  • Sociology and Political Science
  • General Psychology
  • History and Philosophy of Science

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