Optimal Tax

Optimal tax design seeks to find tax structures that maximize societal welfare, often by mitigating inefficiencies arising from self-interested behavior. Current research focuses on developing algorithms, such as those employing piecewise linear approximations and strongly convex potential functions, to learn optimal tax policies within complex models like nonatomic congestion games, even with limited feedback on equilibrium states. This field is significant because it offers the potential for more efficient and equitable resource allocation, though challenges remain in balancing competing societal values and ensuring transparency in the design process. The application of AI and machine learning is increasingly prominent in this area, highlighting the need for careful consideration of ethical and societal implications.