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.

摘要

不忠可能是浪漫关系中的破坏性事件，对双方的幸福造成毁灭性影响。因此，识别可以解释或预测不忠的因素是有好处的，但先前的研究没有使用可以提供每个预测因素相对重要性的方法。我们使用机器学习算法随机森林（一种可解释的高度非线性决策树）来预测两项研究中的面对面和在线不忠行为（一个个体和一个二元，N = 1,295）。我们还使用了一种博弈论解释技术，Shapley 值，它允许我们估计每个预测变量对不忠的影响大小。目前的研究表明，不忠在整体和人际关系方面是可以预测的，例如关系满意度、爱情、欲望、和关系长度最能预测在线和面对面的不忠行为。结果表明，在关系早期解决关系困难可能有助于防止不忠。