Machine Learning-Based Identification of Cultural Determinants of Decision-Making: The Role of Risk Perception, Uncertainty Avoidance, and Norm Compliance

Authors

    Stefan Leitner Department of Cognitive Psychology, University of Vienna, Vienna, Austria
    Tiago Figueiredo * Department of Educational Psychology, University of Lisbon, Lisbon, Portugal tiago.figueiredo@ulisboa.pt
    Søren Mikkelsen Department of Cognitive Psychology, Aarhus University, Aarhus, Denmark

Keywords:

Decision-Making, Machine Learning, Risk Perception, Uncertainty Avoidance, Norm Compliance, Cultural Determinants

Abstract

Objective: The present study aimed to identify and model the cultural determinants of decision-making using machine learning techniques, with a specific focus on the predictive roles of risk perception, uncertainty avoidance, and norm compliance.

Methods and Materials: This study employed a descriptive–correlational design with a machine learning predictive framework. The sample consisted of 412 adult participants from Portugal selected through stratified random sampling to ensure demographic diversity. Data were collected using standardized instruments measuring risk perception, uncertainty avoidance, norm compliance, and decision-making quality. After preprocessing procedures including normalization and handling of missing values, data were analyzed using both traditional statistical methods and advanced machine learning algorithms. Supervised learning models, including Logistic Regression, Support Vector Machine, Random Forest, and Gradient Boosting, were applied to predict decision-making outcomes. Model performance was evaluated using k-fold cross-validation and metrics such as accuracy, precision, recall, F1-score, and area under the ROC curve. Feature importance analysis was conducted to determine the relative contribution of predictors.

Findings: The results indicated that all three cultural variables significantly predicted decision-making quality, with norm compliance emerging as the strongest predictor, followed by risk perception and uncertainty avoidance. Ensemble models demonstrated superior predictive performance, with Gradient Boosting achieving the highest accuracy and classification efficiency compared to other models. Feature importance analysis confirmed the dominant role of norm compliance in influencing decision-making outcomes. Additionally, significant positive relationships were observed among all study variables, indicating that higher levels of cultural alignment correspond to improved decision-making quality.

Conclusion: The findings highlight the critical role of cultural determinants in shaping decision-making processes and demonstrate the effectiveness of machine learning approaches in modeling complex behavioral patterns. Integrating cultural variables into predictive frameworks enhances both theoretical understanding and practical applications of decision-making research.

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Published

2026-04-01

Submitted

2025-07-13

Revised

2025-10-23

Accepted

2025-10-31

Issue

Section

Articles

How to Cite

Leitner, S., Figueiredo, T., & Mikkelsen, S. (2026). Machine Learning-Based Identification of Cultural Determinants of Decision-Making: The Role of Risk Perception, Uncertainty Avoidance, and Norm Compliance. Journal of Psychosociological Research in Family and Culture, 1-10. https://journals.kmanpub.com/index.php/jprfc/article/view/5378