Business Intelligence Management Studies

Machine-learning-driven agent-based modeling: Simulating the decision of health sector human resources to migrate

Document Type : Research Paper

Authors

Homa Khodadadi 1
Mostafa Kzaemi 2
Naser Motahari Farimani 3
Seyyed Mohammad Tabatabaei 4

1 Ph.D. student in Industrial Management, Concentration in Systems Management, Department of Management, Faculty of Economics and Administrative Sciences, Ferdowsi University of Mashhad, Mashhad, Iran

2 Professor, Department of Management, Faculty of Economics and Administrative Sciences, Ferdowsi University of Mashhad, Mashhad, Iran Corresponding Author: kazemi@um.ac.ir

3 Associate Professor, Department of Management, Faculty of Economics and Administrative Sciences, Ferdowsi University of Mashhad, Mashhad, Iran

4 Associate professor of Department of Medical Informatics, Mashhad University of Medical Sciences, Mashhad, Iran

https://doi.org/10.22054/ims.2026.89993.2727
Abstract
Migration of human resources in the health sector not only reduces 
the quality of healthcare services but also imposes detrimental social 
and economic consequences on developing countries such as Iran. 
Therefore, accurately modeling the decision-making behavior of this 
workforce requires advanced analytical approaches to capture 
complexities and social interactions. This study aimed to design and 
validate a data driven agent-based model to simulate migration 
behavior among healthcare professionals in Iran. Secondary data 
were employed from the 2023 survey entitled “National Survey on 
Elite Migration and Factors Influencing the Outflow of Human 
 Corresponding Author: kazemi@um.ac.ir 
How to Cite: Khodadadi, H., Kazemi, M., Motahari Frimani, N., Tabatabaei, S.M. 
(2026). Machine-learning-driven agent-based modeling: Simulating the decision of 
health sector human resources to migrate, Journal of Business Intelligence 
Management Studies, 15(55), 127-175. DOI: 10.22054/ims.2026.89993.2727 
Original Research    
Received: 29 November 2025       Revised: 10 March 2026    Accepted: 10 March 2026          
eISSN: 2821-0816        
ISSN: 2821-0964          
Spring 2026 | No.55 | Vol.15 | Business Intelligence Management Studies | 128 
Capital in the Health Sector” conducted by the Iranian Migration 
Observatory using a standardized questionnaire. The research 
adopted a hybrid framework in which 384 balanced samples were 
used for training, and the Random Forest machine learning algorithm 
served as the behavioral meta model of agents to directly extract 
nonlinear decision-making rules from microdata. The model output, 
representing the migration probability of each agent, was then 
integrated into the agent-based simulation, where comparison with 
an optimal decision threshold determined the final migration or non
migration action. Results indicated that the data driven agent model 
significantly outperformed the theory driven agent model based on 
logistic regression in predicting migration intentions. Furthermore, 
analyses confirmed that key variables such as age, work experience, 
and social network effects played nonlinear and essential roles in 
shaping final decisions.

Keywords

Agent-based modeling
Machine learning
Migration
Human resources
Health sector
Subjects
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  10. Vedadhir, A., & Eshraghi, S. (2019). Attitude toward migrate abroad in Iranian medical community: A qualitative study. Journal of Research and Planning in Higher Education, 25(2), 23–42. https://sid.ir/paper/68063/en

 

 

 

 

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‌ ‌استناد به این مقاله: خدادادادی، هما.، کاظمی، مصطفی.، مطهری فریمانی، ناصر.، طباطبائی، سید محمد. (1405). مدل‌سازی عامل‌بنیان مبتنی بر یادگیری ماشین: شبیه‌سازی تصمیم مهاجرت نیروهای انسانی بخش سلامت، مطالعات مدیریت کسب وکار هوشمند، 15(55)، 127-175. DOI: 10.22054/ims.2026.89993.2727

 Journal of Business Intelligence Management Studies is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License..

Business Intelligence Management Studies
Volume 15, Issue 55
Spring 2026
Pages 127-175

  • Receive Date 09 December 2025
  • Revise Date 10 March 2026
  • Accept Date 10 March 2026

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