Machine Learning-Based Modeling of Family Decision Processes Using Shared Mental Models, Power Dynamics, and Negotiation Styles
Keywords:
Family decision-making, Shared mental models, Power dynamics, Negotiation styles, Machine learning, Predictive modelingAbstract
Objective: The present study aimed to model and predict family decision-making effectiveness using machine learning techniques by examining the roles of shared mental models, power dynamics, and negotiation styles.
Methods and Materials: This descriptive–correlational study with a predictive modeling approach was conducted on 412 married adults in Thailand selected through multistage cluster sampling. Data were collected using standardized instruments assessing shared mental models, family power dynamics, and negotiation styles, all of which demonstrated acceptable validity and reliability. Data analysis was performed using a hybrid approach combining statistical analysis in IBM SPSS Statistics (version 27) and machine learning modeling in Python with scikit-learn and TensorFlow. Predictive models including Random Forest, Support Vector Machine, Gradient Boosting, and Multilayer Perceptron were trained and evaluated using cross-validation, with performance assessed through accuracy, precision, recall, F1-score, and AUC-ROC metrics.
Findings: The results indicated that shared mental models had a significant positive effect on family decision-making effectiveness, while power dynamics showed a significant negative effect. Integrative negotiation style significantly and positively predicted decision-making effectiveness, whereas dominating style had a significant negative association. Machine learning analysis revealed that the Gradient Boosting model achieved the highest predictive performance (accuracy = 0.89, AUC = 0.92), outperforming other models. Feature importance analysis demonstrated that shared mental models were the strongest predictor, followed by integrative negotiation style and power dynamics, confirming the relative contribution of cognitive, behavioral, and structural variables in predicting decision outcomes.
Conclusion: The findings highlight the central role of cognitive alignment and collaborative negotiation in enhancing family decision-making effectiveness, while unequal power structures undermine optimal outcomes. The integration of machine learning approaches provides a robust and nuanced framework for modeling complex family processes, offering both theoretical advancement and practical implications for improving relational functioning.
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