Simulating Tax Policy: Agent Utility, Elections, and the Dynamics of Labor and Taxation with LLM Generative Agents

Abstract

Experimenting with tax policy in the real world can be prohibitively expensive and politically infeasible. Governments need innovative simulation and modeling techniques to evaluate policy impacts before deployment. Existing approaches in optimal income taxation theory create sup-optimal policies by relying on economic models that make simplifying assumptions about human behavior. This thesis argues that large language models [LLMs] learn tax policies that result in higher social welfare than the tax policies produced by existing economic models by providing a scalable, affordable method to model societal behavior and optimize for social welfare. We model policy decisions as an infinite dynamic game between a tax planner (leader) and workers (followers), optimizing for Stackelberg equilibria that maximize social welfare. We use LLMs to generate synthetic human data facilitating policy mechanism design, testing, and optimization. To increase realism, we implement simulation scenarios where the tax planner is elected by worker agents. We validate our LLM-based approach by comparing our results in a two worker agent, one tax planner simulation to a Stackelberg equilibria that we calculate through backwards induction. We investigate the effect of different simulation scenarios and skill distributions on social welfare. We find that our LLM-based approach achieves higher social welfare than the tax policy calculated according to economist Emmanuel Saez's optimal income taxation formulas. Future work could implement extensions to Saez's formulas that incorporate more elements of human economic activity with the goal of achieving higher social welfare with learned policies in these more complicated scenarios.