Uncovering Decision-Making Styles in Iran Using Advanced Machine Learning: Deep Embedded Clustering and Explainable AI for Psychological Profiling

Taghi Shakouri Youvalari; İnci Zaim Gökbay

doi:10.26650/d3ai.1854272

Uncovering Decision-Making Styles in Iran Using Advanced Machine Learning: Deep Embedded Clustering and Explainable AI for Psychological Profiling

Abstract

Psychological identity develops through the interaction of internal traits and external sociopolitical conditions. In Iran, repeated exposure to war, sanctions, and uncertainty has shaped identity across generations. This study uses machine learning to identify latent identity patterns and generational differences in a non-clinical Iranian sample (N = 620; ages 18–60).

Deep embedded clustering (DEC) was applied to decision-making and self-regulation traits. Four psychological profiles were identified, each defined by different combinations of impulsivity, coping flexibility, emotional regulation, and decisional insecurity. Profile entropy and drift indices were used to describe internal stability and ambiguity. SHAP analysis and counterfactual simulations were used to examine which traits most influenced potential profile change.

War-experienced adults were more likely to show stable but emotionally restricted profiles. Post-war adults more often showed profiles with higher entropy and less coherence. A simple transition model based on psychological proximity and entropy was used to explore possible movement between profiles.

The findings indicate that unsupervised learning approaches can identify non-clinical psychological risk and resilience patterns in culturally specific contexts. Generational differences suggest that sociopolitical exposure is associated with variation in identity organisation. These results contribute to the understanding of psychological adaptation in populations exposed to chronic structural stress.

Keywords

References

A. Ahmadi and A. Sadeghi. 2019. Psychological resilience and generational identity in Iranian adults. google scholar
S. M. Lundberg and S.-I. Lee. 2017. A unified approach to interpreting model predictions. In Advances in Neural Information Processing Systems, Vol. 30. Curran Associates, Inc. google scholar
D. P. McAdams. 2001. The psychology of life stories. Review of General Psychology 5, 2 (2001), 100–122. google scholar
D. P. McAdams and J. L. Pals. 2006. A new Big Five: Fundamental principles for an integrative science of personality. American Psychologist 61, 3 (2006), 204–217. google scholar
E. Min, X. Guo, Q. Liu, G. Zhang, J. Cui, and J. Long. 2018. A survey of clustering with deep learning: From the perspective of network architecture. IEEE Access 6 (2018), 39501–39514. google scholar
P. C. M. Molenaar. 2004. A manifesto on psychology as idiographic science: Bringing the person back into scientific psychology, this time forever. Measurement 2, 4 (2004), 201–218. google scholar
S. Moradi, H. Farahani, and A. Vahidi. 2022. Intergenerational differences in coping and adaptation in Iran: A post-conflict psychological analysis. Middle East Current Psychology 3, 1 (2022), 45–59. google scholar
R. K. Mothilal, A. Sharma, and C. Tan. 2020. Explaining machine learning classifiers through diverse counterfactual explanations. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (2020), 607–617. google scholar

M. Najafi, M. Habibi, and Z. Khosravi. 2020. Limitations of clinical diagnostic models for capturing subclinical psychological risk in Iranian populations. Archives of Clinical Psychiatry 47, 2 (2020), 92–99. google scholar
M. Nazari and A. Etemadi. 2022. Limitations of linear clustering in psychological profiling: A simulation-based comparison. International Journal of Behavioural Data Science 9, 3 (2022), 115–129. google scholar
S. N. Noorbakhsh, M. Yazdani, and R. Dastjerdi. 2019. Decision-making quality and emotional regulation among Iranian students in uncertain environments. Cognitive Psychology and Psychiatry 27, 4 (2019), 323–336. google scholar
J. R. Norris. 1998. Markov chains. Cambridge University Press. google scholar
M. W. Pratt, M. L. Arnold, and H. L. Lawford. 2018. Narrative identity and personality development. Personality and Social Psychology Review 22, 1 (2018), 74–96. google scholar
M. Reza and L. Yousefi. 2022. Sociopolitical embedding of psychological development: Case evidence from post-revolutionary Iran. Middle East Psychology Review 14, 3 (2022), 141–158. google scholar
S. Rezaei, H. Jafari, and S. Motevalian. 2022. Emerging adult identity in post-revolutionary Iran: The role of sociopolitical instability and perceived agency. Middle East Journal of Psychology 8, 2 (2022), 98–113. google scholar
P. J. Rousseeuw. 1987. Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics 20 (1987), 53–65. google scholar
F. Sadeghi. 2008. Gender, ideology, and identity in post-revolutionary Iran. Middle East Critique 17, 2 (2008), 117–133. google scholar
K. Sadeghi, A. Rezaei, and V. Farzad. 2017. Behavioural manifestations of identity instability in emerging adulthood: A case study in Iranian society. Iranian Journal of Psychiatry and Clinical Psychology 23, 1 (2017), 16–25. google scholar
M. J. Shabani, B. Gharraee, and K. Zahedi Tajrishi. 2024. Factor structure and reliability of the Persian DASS Y in youth. Frontiers in Psychology 15 (2024), Article 1452878. google scholar
Z. Shahhosseini, Z. Hamzehgardeshi, and M. Bijani. 2018. The psychological burden of sociopolitical restrictions in Iranian youth: A qualitative review. International Journal of Human Rights in Healthcare 11, 1 (2018), 41–50. google scholar
C. E. Shannon. 1948. A mathematical theory of communication. Bell System Technical Journal 27, 3 (1948), 379–423. google scholar
O. Shokri, S. M. Taghavi, and A. R. Moradi. 2011. Validation of the Multidimensional Scale of Perceived Social Support in Iranian Students. Psychological Reports 109, 1 (2011), 89–98. https://doi.org/10.2466/02.07.21.PR0.109.1.89-98 google scholar
M. Vossoughi, M. Kharazi, A. Mani, et al. 2024. Psychological strains after the crisis: Separation anxiety among Iranian adolescent post–COVID-19. BMC Psychology 12 (2024), Article 471. google scholar
J. Xie, R. Girshick, and A. Farhadi. 2016. Unsupervised deep embedding for clustering analysis. In Proceedings of the 33rd International Conference on Machine Learning (2016), 478–487. google scholar
N. Yousefi, A. Etemadi, F. Bahrami, M. Fatehizadeh, and M. R. Abedi. 2010. Effectiveness of coping-based group therapy on students’ coping strategies. Journal of Counselling Research 9, 1 (2010), 19–36. google scholar
M. Zarei and R. Ghanbari. 2023. Emotion regulation and behavioural inconsistency in high-pressure environments: Evidence from Iranian youth. Journal of Behavioural Science Research 35, 2 (2023), 85–101. google scholar
Z. Zhou, Y. Luo, and S. Pan. 2022. Deep clustering with generative models for psychological trait analysis. Pattern Recognition 126 (2022), Article 108531. google scholar
Koen Luyckx, Bart Soenens, Maarten Vansteenkiste, Luc Goossens, and Michael D. Berzonsky. 2007. Parental psychological control and dimensions of identity formation in emerging adulthood. Journal of Family Psychology 21, 3 (2007), 546–550. google scholar
Note: Your in-text says “Luyckx et al. 2008,” but the matching paper is 2007 and is widely indexed. If your text truly says 2008, we need to check whether you meant a different Luyckx 2008 work. google scholar
Seth J. Schwartz, Koen Luyckx, and Vivian L. Vignoles (Eds.). 2011. Handbook of Identity Theory and Research. Springer, New York, NY. google scholar
Morteza A. Cyders and Yakup Coskunpinar. 2011. Measurement of constructs using self-report and behavioural lab tasks: Is there overlap in impulsivity? Clinical Psychology Review 31, 6 (2011), 965–977. doi:10.1016/j.cpr.2011.06.001 google scholar
Susan Folkman and Judith T. Moskowitz. 2004. Coping: Pitfalls and promise. Annual Review of Psychology 55 (2004), 745–774. doi:10.1146/annurev.psych.55.090902.141456 google scholar
Charles S. Carver and Jennifer Connor-Smith. 2010. Personality and coping. Annual Review of Psychology 61 (2010), 679–704. doi:10.1146/annurev.psych.093008.100352 google scholar
Moin Syed and Kate C. McLean. 2016. Understanding identity integration: theoretical, methodological, and applied issues. Journal of Adolescence 47 (2016), 109–118. doi:10.1016/j.adolescence.2015.09.005. google scholar

Details

Primary Language

English

Subjects

Evolutionary Computation

Journal Section

Research Article

Authors

Taghi Shakouri Youvalari
0000-0003-4452-0772
Türkiye

İnci Zaim Gökbay ^*
0000-0002-4488-1642
Türkiye

Publication Date

January 30, 2026

Submission Date

January 2, 2026

Acceptance Date

January 23, 2026

Published in Issue

Year 2026 Volume: 2 Number: 1

DOI

https://doi.org/10.26650/d3ai.1854272

IZ

https://izlik.org/JA39WA57KW

Cite

RIS / Bibtex

APA

Shakouri Youvalari, T., & Zaim Gökbay, İ. (2026). Uncovering Decision-Making Styles in Iran Using Advanced Machine Learning: Deep Embedded Clustering and Explainable AI for Psychological Profiling. Journal of Data Analytics and Artificial Intelligence Applications, 2(1), 64-95. https://doi.org/10.26650/d3ai.1854272

AMA

1.Shakouri Youvalari T, Zaim Gökbay İ. Uncovering Decision-Making Styles in Iran Using Advanced Machine Learning: Deep Embedded Clustering and Explainable AI for Psychological Profiling. Journal of Data Analytics and Artificial Intelligence Applications. 2026;2(1):64-95. doi:10.26650/d3ai.1854272

Chicago

Shakouri Youvalari, Taghi, and İnci Zaim Gökbay. 2026. “Uncovering Decision-Making Styles in Iran Using Advanced Machine Learning: Deep Embedded Clustering and Explainable AI for Psychological Profiling”. Journal of Data Analytics and Artificial Intelligence Applications 2 (1): 64-95. https://doi.org/10.26650/d3ai.1854272.

EndNote

Shakouri Youvalari T, Zaim Gökbay İ (January 1, 2026) Uncovering Decision-Making Styles in Iran Using Advanced Machine Learning: Deep Embedded Clustering and Explainable AI for Psychological Profiling. Journal of Data Analytics and Artificial Intelligence Applications 2 1 64–95.

IEEE

[1]T. Shakouri Youvalari and İ. Zaim Gökbay, “Uncovering Decision-Making Styles in Iran Using Advanced Machine Learning: Deep Embedded Clustering and Explainable AI for Psychological Profiling”, Journal of Data Analytics and Artificial Intelligence Applications, vol. 2, no. 1, pp. 64–95, Jan. 2026, doi: 10.26650/d3ai.1854272.

ISNAD

Shakouri Youvalari, Taghi - Zaim Gökbay, İnci. “Uncovering Decision-Making Styles in Iran Using Advanced Machine Learning: Deep Embedded Clustering and Explainable AI for Psychological Profiling”. Journal of Data Analytics and Artificial Intelligence Applications 2/1 (January 1, 2026): 64-95. https://doi.org/10.26650/d3ai.1854272.

JAMA

1.Shakouri Youvalari T, Zaim Gökbay İ. Uncovering Decision-Making Styles in Iran Using Advanced Machine Learning: Deep Embedded Clustering and Explainable AI for Psychological Profiling. Journal of Data Analytics and Artificial Intelligence Applications. 2026;2:64–95.

MLA

Shakouri Youvalari, Taghi, and İnci Zaim Gökbay. “Uncovering Decision-Making Styles in Iran Using Advanced Machine Learning: Deep Embedded Clustering and Explainable AI for Psychological Profiling”. Journal of Data Analytics and Artificial Intelligence Applications, vol. 2, no. 1, Jan. 2026, pp. 64-95, doi:10.26650/d3ai.1854272.

Vancouver

1.Taghi Shakouri Youvalari, İnci Zaim Gökbay. Uncovering Decision-Making Styles in Iran Using Advanced Machine Learning: Deep Embedded Clustering and Explainable AI for Psychological Profiling. Journal of Data Analytics and Artificial Intelligence Applications. 2026 Jan. 1;2(1):64-95. doi:10.26650/d3ai.1854272