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Is Infidelity Predictable? Using Explainable Machine Learning to Identify the Most Important Predictors of Infidelity
Laura M Vowels1, Matthew J Vowels2, Kristen P Mark3
1Department of Psychology, University of Lausanne.
Journal of Sex Research
|August 25, 2021
Summary
Predicting relationship infidelity is possible using machine learning. Interpersonal factors like relationship satisfaction and desire are key predictors of both online and in-person infidelity.
Area of Science:
- Psychology
- Relationship Science
- Computational Social Science
Background:
- Infidelity significantly disrupts romantic relationships and partner well-being.
- Previous research lacked methods to determine the relative importance of infidelity predictors.
Purpose of the Study:
- To predict infidelity using machine learning.
- To identify and quantify the most important predictors of infidelity.
Main Methods:
- Employed a random forest machine learning algorithm to predict infidelity.
- Utilized Shapley values, a game theoretic technique, to estimate predictor effect sizes.
- Analyzed data from two studies (N=1,295) on in-person and online infidelity.
Main Results:
- Infidelity was found to be somewhat predictable.
- Key predictors included relationship satisfaction, love, desire, and relationship length.
- These interpersonal factors were significant for both online and in-person infidelity.
Conclusions:
- Machine learning can effectively predict infidelity.
- Addressing relationship issues like satisfaction and desire may help prevent infidelity.
- Early intervention in relationship difficulties is recommended.
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