Neetyabhas: A Framework for Uncertainty-Aware Public Policy Optimization in Rational Agent-Based Models
arXiv:2606.04562v1 Announce Type: cross Abstract: Purpose The WHO's COVID-19 non-pharmaceutical interventions (e.g., lockdowns, vaccinations) effectively curb transmission but impose heavy economic strains. Existing research often neglects individual behaviors and falsely assumes perfect infection tracking and flawless policy execution, failing to account for real-world uncertainties and errors. Methods We propose an integrative approach incorporating uncertainties in both epidemic measurement (infections/hospitalizations) and policy implementation. We built a simulation model of 1,000 individ
The proliferation of AI and advanced modeling techniques allows for more sophisticated analyses of complex systems like public policy and epidemiology, addressing long-standing limitations.
Strategic readers should care as it offers a framework for more robust, uncertainty-aware policy design across various domains, potentially improving governmental effectiveness and societal outcomes.
This framework shifts policy optimization from idealistic assumptions to a more realistic inclusion of real-world uncertainties and imperfect implementation, enhancing decision-making quality.
- · Public health organizations
- · Policymakers
- · AI/ML researchers
- · Government agencies
- · Traditional epidemiological modelers
- · Policymaking based solely on deterministic models
More resilient and effective public policy solutions in areas like pandemic response are developed.
Increased trust in governmental responses and a reduction in the negative 'unintended' consequences of policy interventions.
The methodology is adopted across other complex policy areas, such as climate change or economic regulation, leading to improved societal resilience and reduced policy-induced shocks.
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Read at arXiv cs.LG