SmarTax: An Intelligent Tax Policy Maker via Multi-agent Reinforcement Learning and Irrationality of Humans - Xiong, Tianlang (Transactions on Computer Science and Intelligent Systems Research, 2025)
Effective tax policies are difficult to design since taxpayers’ behaviors often deviate from the traditional economic models, which assume rational decision-making. Tax policymakers need tools that predict how individuals will respond to different tax policies, accounting for the irrational behaviors caused by biases, emotions, and imperfect information. This study introduces SmarTax, the first income tax policy-making simulator which integrates economics models, multi-agent reinforcement learning (MARL), and more importantly, different models of human irrationality. The goal is to maximize a society’s GDP and social equity. Compared to traditional tax policy simulators, SmarTax helps reduce the gap between simulation and real-world scenarios by incorporating irrationality of the populations instead of assuming universal rationality. For example, by modeling various levels of rationality, SmarTax is able to predict how traditional RL-based tax policy-making process might become invalid due to the fact that low-income households might underutilize tax credits due to complexity or a lack of information. The evaluation of SmarTax was conducted on three reinforcement learning (RL) algorithms: Independent Proximal Policy Optimization (IPPO), Multi-Agent Deep Deterministic Policy Gradient (MADDPG) and Bi-level Mean Field Actor-Critic (BMFAC). The results indicate that, first, the inclusion of irrational factors impedes the effectiveness of all three RL algorithms. Second, compared to IPPO and MADDPG, BMFAC performs better when the irrationality level is low (