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

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State: Public
Version: Final published version
License: CC BY-NC-ND 4.0
Serval ID
serval:BIB_82B1B030B2B5
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Is Infidelity Predictable? Using Explainable Machine Learning to Identify the Most Important Predictors of Infidelity.
Journal
Journal of sex research
Author(s)
Vowels L.M., Vowels M.J., Mark K.P.
ISSN
1559-8519 (Electronic)
ISSN-L
0022-4499
Publication state
Published
Issued date
02/2022
Peer-reviewed
Oui
Volume
59
Number
2
Pages
224-237
Language
english
Notes
Publication types: Journal Article ; Research Support, Non-U.S. Gov't
Publication Status: ppublish
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.
Keywords
Humans, Interpersonal Relations, Love, Machine Learning, Marriage, Sexual Partners
Pubmed
Web of science
Open Access
Yes
Create date
04/10/2021 10:36
Last modification date
24/09/2023 5:57
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