Modeling Cultural Influences on Risk-Taking Using Machine Learning: Sensation Seeking, Norm Deviance, and Peer Influence
Keywords:
Risk-taking behavior, sensation seeking, norm deviance, peer influence, machine learning, cultural influencesAbstract
Objective: The present study aimed to model and predict risk-taking behavior by examining the interactive effects of sensation seeking, norm deviance, and peer influence within a cultural framework using machine learning techniques.
Methods and Materials: This study employed a cross-sectional, predictive-correlational design with a sample of 462 young adults from Greece selected through stratified sampling. Data were collected using standardized self-report instruments assessing risk-taking behavior, sensation seeking, norm deviance, and peer influence. After data preprocessing, including normalization and missing data imputation, both statistical and machine learning analyses were conducted. Pearson correlations were used to examine associations among variables, followed by the implementation of multiple supervised machine learning models, including Random Forest, Support Vector Machine, Gradient Boosting, and Artificial Neural Networks. The dataset was divided into training and testing subsets using an 80/20 split, and 10-fold cross-validation was applied to enhance model generalizability. Model performance was evaluated using accuracy, precision, recall, F1-score, and AUC-ROC metrics, while SHAP analysis was used to determine feature importance and interpret model predictions.
Findings: Inferential analyses indicated significant positive relationships between risk-taking behavior and sensation seeking (r = 0.54, p < 0.01), peer influence (r = 0.52, p < 0.01), and norm deviance (r = 0.49, p < 0.01). Machine learning results revealed that the Gradient Boosting model demonstrated the highest predictive performance (accuracy = 0.88, AUC-ROC = 0.93), followed by Random Forest and Neural Network models. Feature importance analysis using SHAP values showed that sensation seeking was the strongest predictor (mean SHAP = 0.37), followed by peer influence (0.31) and norm deviance (0.28), indicating that both individual and social factors significantly contribute to the prediction of risk-taking behavior.
Conclusion: The superior performance of ensemble machine learning models highlights the importance of capturing nonlinear and complex relationships among predictors.
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References
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