Integrating psychological factors into AI-supported decision-making for reverse brain drain policies: A human-centered computing approach

Reverse brain drain represents both a challenge and an opportunity for countries seeking to attract highly skilled professionals back home. While existing policies often emphasize economic and institutional incentives, psychological factors, such as belonging, identity, trust, and future expectations are equally decisive but remain underexplored. This study proposes a human-centered decision-making model that systematically integrates these psychological dimensions into policy prioritization. Expert judgments are analyzed using a fuzzy decision support system, and uncertainty is addressed through Sierpiński Triangle Fuzzy Sets, which capture multi-layered hesitation more effectively than traditional fuzzy approaches. The findings highlight belonging and identity and hope and future expectations as the most influential factors shaping return decisions. In terms of strategies, predictable economic policies and political stability and competitive salary schemes emerge as top priorities. By combining psychological insights with advanced uncertainty modeling, the study offers both theoretical contributions to migration research and practical guidance for policymakers seeking to design sustainable reverse brain drain strategies.