An Evolutionary Game Model for Understanding Fraud in Consumption Taxes Chica Serrano, Manuel Hernández Guerra, Juan Manrique de Lara, Casiano Chiong, Raymond This work is jointly supported by the Spanish Ministry of Science, Andalusian Government, and ERDF under grants EXASOCO (PGC2018-101216-B-I00), SIMARK (P18-TP-4475) and RYC-2016-19800. The data used here is based on the project “Elaboration of a Computable General Equilibrium Model for the Analysis of Fiscal Policies on Sales Taxes” commissioned by the Economy and Finance Council of the Regional Government of the Canary Islands (FULP, CN45/08 240/57/100). This paper presents a computational evolutionary game model to study and understand fraud dynamics in the consumption tax system. Players are cooperators if they correctly declare their value added tax (VAT), and are defectors otherwise. Each player's payoff is influenced by the amount evaded and the subjective probability of being inspected by tax authorities. Since transactions between companies must be declared by both the buyer and seller, a strategy adopted by one influences the other?s payoff. We study the model with a wellmixed population and different scalefree networks. Model parameters were calibrated using real-world data of VAT declarations by businesses registered in the Canary Islands region of Spain. We analyzed several scenarios of audit probabilities for high and low transactions and their prevalence in the population, as well as social rewards and penalties to find the most efficient policy to increase the proportion of cooperators. Two major insights were found. First, increasing the subjective audit probability for low transactions is more efficient than increasing this probability for high transactions. Second, favoring social rewards for cooperators or alternative penalties for defectors can be effective policies, but their success depends on the distribution of the audit probability for low and high transactions. 2025-06-24T11:12:08Z 2025-06-24T11:12:08Z 2021-04-12 journal article Chica Serrano, Manuel et al. IEEE Computational Intelligence Magazine 16 (2) 62-76. DOI: 10.1109/MCI.2021.3061878 https://hdl.handle.net/10481/104819 10.1109/MCI.2021.3061878 eng open access IEEE