نوع مقاله : مقاله پژوهشی
نویسندگان
1 دانشجوی دکتری رشته مدیریت صنعتی گرایش مدیریت سیستمها، گروه مدیریت، دانشکده علوم اداری و اقتصاد، دانشگاه فردوسی مشهد، مشهد، ایران
2 استاد، گروه مدیریت، دانشکده علوم اداری و اقتصاد، دانشگاه فردوسی مشهد، مشهد، ایران نویسنده مسئول : kazemi@um.ac.ir
3 دانشیار، گروه مدیریت، دانشکده علوم اداری و اقتصاد، دانشگاه فردوسی مشهد، مشهد، ایران
4 دانشیار، گروه انفورماتیک پزشکی، دانشکده پزشکی، دانشگاه علوم پزشکی مشهد، مشهد، ایران
کلیدواژهها
عنوان مقاله English
نویسندگان English
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.
کلیدواژهها English
References
References [In Persian]
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
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