Probabilistic Machine Learning Driven Decision Support System for Enhancing Policy Decision-Making Under Uncertainty - Ekrem, Adskhan Reyhan (Jurnal Teknik Informatika C.I.T Medicom, 2024)
This research examines the role of uncertainty modeling in enhancing the quality, reliability, and adaptability of policymaking. Traditional policy decisions often rely on fixed assumptions that fail to account for the inherent volatility of social, economic, and environmental systems. By integrating probabilistic techniques, scenario analysis, and sensitivity-based evaluation, the study demonstrates how policymakers can better anticipate variability in economic, social, and environmental outcomes. The findings indicate that uncertainty modeling not only improves predictive accuracy but also strengthens policy resilience by revealing hidden risks, alternative pathways, and the range of possible impacts under differing conditions. The research contributes a structured framework for incorporating uncertainty into policy design and evaluation, providing practical tools for evidence-based decision-making. In practice, the model enables policymakers to make more adaptive, transparent, and risk-aware decisions, ultimately transforming traditional deterministic approaches into dynamic strategies capable of responding effectively to complex and unpredictable real-world challenges.