Integrating Fine-Tuned GPT with Agent-Based Economic Modeling for Transparent Wage Policy Decisions

This study presents a decision-support system powered by GPT-enhanced insights to help policymakers explore the economic effects of minimum wage policies in the Philippines. The system integrates agent-based simulation, fuzzy logic, reinforcement learning, and Fuzzy Analytic Hierarchy Process (Fuzzy AHP) to model the complex relationships between wages, inflation, firm behavior, and employment. At its core is a fine-tuned GPT model trained on synthetic simulation outputs, capable of generating human-readable interpretations that explain dynamic trends, trade-offs, and fuzzy economic behaviors that are often difficult to decipher from numbers alone. Two policy scenarios were simulated over 100 months: increasing the minimum wage from ‚500 to ‚600, and from ‚500 to ‚700. While the ‚700 scenario led to short-term boosts in productivity and real wages, it also triggered early inflation, unstable profits, and reduced employment. In contrast, the ‚600 scenario produced more stable results, balancing moderate wage growth with firm sustainability and lower inflationary pressure. Fuzzy AHP was used to evaluate each scenario across four key criteria—real wages, firm profitability, employment, and inflation—favoring ‚600 as the more sustainable policy path. What sets this study apart is the integration of GPT-generated policy narratives that accompany each simulation run. These insights help translate fuzzy, nonlinear model behaviors into clear, accessible language—supporting more inclusive, transparent, and evidence-based wage policy decisions. By combining simulation and generative AI, the framework offers not just predictions, but practical understanding of how economic systems respond to complex changes.

@article{Sr.2025,
  title     = {Integrating Fine-Tuned GPT with Agent-Based Economic Modeling for Transparent Wage Policy Decisions},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {8},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Daniel A. Ariaso Sr. and Ken D. Gorro and Deofel Balijon and Meshel Balijon},
  doi       = {10.14569/IJACSA.2025.0160806},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160806}
}