Neetyabhas: A Framework for Uncertainty-Aware Public Policy Optimization in Rational Agent-Based Models

Researchers developed Neetyabhas, an agent-based simulation framework that models pandemic policy decisions under real-world uncertainty, incorporating individual behavioral choices and imperfect data. Using reinforcement learning, the model demonstrates that masks and vaccines effectively reduce outbreak severity when policies account for implementation errors and measurement gaps.

This research addresses a critical gap in epidemiological modeling by embedding realistic constraints into policy simulation. Traditional pandemic models assume perfect information and flawless execution, whereas this framework acknowledges that governments operate with incomplete infection data, implementation delays, and individual behavioral variability. The use of hierarchical reinforcement learning—specifically deep Q-networks and uncertainty-aware variants like DDPG and TD3—represents a methodological advance in how complex systems respond to policy interventions.

The work builds on years of pandemic policy debates that exposed tensions between health and economic objectives. By simulating 1,000 agents making autonomous decisions about mask-wearing and vaccination while policymakers react to noisy health signals, the model captures real-world dynamics that spreadsheet models miss. This addresses legitimate criticisms that lockdown policies sometimes relied on overly optimistic assumptions about behavioral compliance and data accuracy.

For public health institutions and policy analysts, this framework offers a practical tool for stress-testing interventions before deployment. The finding that masks and vaccines remain effective despite implementation uncertainty provides empirical support for focused interventions rather than blunt economy-wide restrictions. However, the model's scalability to populations of millions and its transferability to different cultural contexts remain open questions.

Future applications could integrate this uncertainty-aware approach into real-time policy dashboards, allowing officials to adjust strategies as feedback loops reveal actual implementation gaps. The framework's emphasis on behavioral heterogeneity and measurement error positions it as a foundation for more realistic pandemic preparedness planning.

• →Agent-based modeling with reinforcement learning captures policy implementation uncertainty better than traditional epidemiological approaches.
• →Masking and vaccination remain effective interventions even when accounting for real-world execution errors and incomplete data.
• →Individual behavioral choices significantly influence pandemic outcomes and must be integrated into policy optimization models.
• →The framework demonstrates that perfect information assumptions in prior models critically underestimated policy complexity.
• →Uncertainty-aware policy algorithms (DDPG, TD3) outperform deterministic approaches in managing competing health and economic objectives.