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

Goumagias, Nikolaos, Hristu-Varsakelis, Dimitrios and Assael, Yannis (2018) Using Deep Q-learning to understand the tax evasion behavior of risk-averse firms. Expert Systems with Applications. ISSN 0957-4174 (In Press)

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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.

Item Type: Article
Uncontrolled Keywords: Markov Decision Processes; Tax Evasion; Q-Learning; Deep Learning
Subjects: L900 Others in Social studies
N100 Business studies
Department: Faculties > Business and Law > Newcastle Business School > Business and Management
Depositing User: Becky Skoyles
Date Deposited: 08 Feb 2018 13:13
Last Modified: 08 Feb 2018 15:25

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