Smart Decision Support Systems For Dynamic Tax Policy Optimization Using Reinforcement Learning

Abstract

Tax policy aims to stabilize the economy and provide basic public services to meet the needs of the domestic economy. However, it is dynamically implemented by governments to regulate economic fluctuations. Tax revenue, affected by many internal and external factors, is difficult for governments to predict. Tax noncompliance and evasion also hinder the effectiveness of tax policy. Consequently, the formulation of tax policy is difficult and needs to be based on a predictive model that can provide reliable decision support. Reinforcement learning (RL) is a branch of machine learning that forms policy through reward-driven interaction with an environment. By setting the control problem of tax policy into an RL framework, reinforcement learning can realize the adaptive and autonomous optimization of tax policy.

Tax policy needs to improve the return on taxation while pursuing other economic goals and maintaining the stability of taxation in order to stimulate compliance and avoid deformation of the tax base. Therefore, it is necessary to ensure that the improvement of taxation return does not affect investment incentives, both domestic and foreign. For public systems, such as social security and education, the rational planning of short-term expenditure and its cycle through a counter-cyclical stance of tax policy in line with economic needs within the overall revenue and expenditure plan.