Using deep Q-learning to understand the tax evasion behavior of risk-averse firms

TL;DR

This paper employs deep Q-learning to model and analyze the behavior of risk-averse firms in tax evasion scenarios, providing insights into optimal policies and taxpayer behavior under various tax enforcement strategies.

Contribution

It introduces a novel application of deep reinforcement learning to approximate optimal tax evasion strategies for risk-averse firms, extending beyond traditional analytical methods.

Findings

Identifies expected tax evasion behavior of risk-averse firms.

Estimates the degree of risk aversion based on empirical data.

Evaluates the impact of different tax policies on government revenue.

Abstract

Designing tax policies that are effective in curbing tax evasion and maximize state revenues requires a rigorous understanding of taxpayer behavior. This work explores the problem of determining the strategy a self-interested, risk-averse tax entity is expected to follow, as it "navigates" - in the context of a Markov Decision Process - a government-controlled tax environment that includes random audits, penalties and occasional tax amnesties. Although simplified versions of this problem have been previously explored, the mere assumption of risk-aversion (as opposed to risk-neutrality) raises the complexity of finding the optimal policy well beyond the reach of analytical techniques. Here, we obtain approximate solutions via a combination of Q-learning and recent advances in Deep Reinforcement Learning. By doing so, we i) determine the tax evasion behavior expected of the taxpayer entity, ii) calculate the degree of risk aversion of the "average" entity given empirical estimates of tax evasion, and iii) evaluate sample tax policies, in terms of expected revenues. Our model can be useful as a testbed for "in-vitro" testing of tax policies, while our results lead to various policy recommendations.