Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/61382
Title: An Evolutionary Game Model for Understanding Fraud in Consumption Taxes [Research Frontier]
Contributor(s): Chica, Manuel (author); Hernandez, Juan M (author); Manrique-De-Lara-Penate, Casiano (author); Chiong, Raymond  (author)orcid 
Publication Date: 2021
DOI: 10.1109/MCI.2021.3061878
Handle Link: https://hdl.handle.net/1959.11/61382
Abstract: 

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.

Publication Type: Journal Article
Source of Publication: IEEE Computational Intelligence Magazine, 16(2), p. 62-76
Publisher: Institute of Electrical and Electronics Engineers
Place of Publication: United States of America
ISSN: 1556-6048
1556-603X
Fields of Research (FoR) 2020: 4602 Artificial intelligence
Peer Reviewed: Yes
HERDC Category Description: C1 Refereed Article in a Scholarly Journal
Appears in Collections:Journal Article
School of Science and Technology

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