The Role of AI-Enhanced Policy Simulations in Decision-Making

Abstract

This paper discusses the role of AI-enhanced policy simulations in decision-making with the focus on their potential to improve policymaking in multiple areas.  It is a mixed-methods and experimental research based on the following presentation of qualitative insights (expert opinions and case studies) with a quantitative one (historical policy outcomes and socioeconomic indicators).  The researchers model various situations of policy and predict their potential outcome using machine learning algorithms, including agent-based modelling and reinforcement learning.  To ensure their accuracy and reliability, these AI-based models are evaluated on the basis of a mixture of performance indicators and human judgment.  The results demonstrate that AI-enhanced simulations can contribute much when it comes to policy prediction, decision-making, and providing decision-makers with the valuable information.  The research indicates that AI can be deployed to complement the efficiency and agility of policy response and lead to more informed and efficient government.  These findings present a paradigm in using AI tools in policy formulation, evaluation and decision support systems with broad implications in the application of AI in governance.