poster

Agents that collude to evade taxes

Authors:

Luis Antunes,

João Balsa,

Helder CoelhoAuthors Info & Claims

AAMAS '07: Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems

Article No.: 212, Pages 1 - 3

https://doi.org/10.1145/1329125.1329381

Published: 14 May 2007 Publication History

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Abstract

We explore the link between micro-level motivations leading to and being influenced by macro-level outcomes to study the complex issue of tax evasion. If it is obvious why there is a benefit for people who evade taxes, it is less obvious why people would pay any taxes at all, given the the small probability of being caught, and the small penalties involved. We use exploratory simulation and progressively deepening models of agents and of simulations to study the reasons behind tax evasion. We have unveiled some relatively simple social mechanisms that can explain the compliance numbers observed in real economies. We claim that simulation with multiple agents provides a strong methodological tool with which to support the design of public policies.

References

[1]

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L. Antunes, J. Balsa, L. Moniz, P. Urbano, and C. R. Palma. Tax compliance in a simulated heterogeneous multi-agent society. In Multi-Agent-Based Simulation VI, volume 3891 of LNAI. Springer, 2006.

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J. Balsa, L. Antunes, A. Respício, and H. Coelho. Autonomous inspectors in tax compliance simulation. In Proc. 18th European Meeting on Cybernetics and Systems Research, 2006.

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Cited By

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Zheng QXu YLiu HShi BWang JDong B(2024)A Survey of Tax Risk Detection Using Data Mining TechniquesEngineering10.1016/j.eng.2023.07.01434(43-59)Online publication date: Mar-2024
https://doi.org/10.1016/j.eng.2023.07.014
Wu YZheng QGao YDong BWei RZhang FHe H(2019)TEDM-PU: A Tax Evasion Detection Method Based on Positive and Unlabeled Learning2019 IEEE International Conference on Big Data (Big Data)10.1109/BigData47090.2019.9006325(1681-1686)Online publication date: Dec-2019
https://doi.org/10.1109/BigData47090.2019.9006325
Coelho H(2019)Reflections on Social Simulation and ComplexityProgress in Artificial Intelligence10.1007/978-3-030-30244-3_52(633-641)Online publication date: 30-Aug-2019
https://doi.org/10.1007/978-3-030-30244-3_52
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Information & Contributors

Information

Published In

AAMAS '07: Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems

May 2007

1585 pages

ISBN:9788190426275

DOI:10.1145/1329125

Conference Chairs:
Edmund Durfee
University of Michigan
,
Makoto Yokoo
Kyushu University
,
Program Chairs:
Michael Huhns
University of South Carolina
,
Onn Shehory
IBM Haifa Research Lab, Israel

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 14 May 2007

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AAMAS07

Sponsor:

AAMAS07: International Conference on Autonomous Agents and Mulitagent Systems

May 14 - 18, 2007

Hawaii, Honolulu

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Overall Acceptance Rate 1,155 of 5,036 submissions, 23%

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Cited By

View all

Zheng QXu YLiu HShi BWang JDong B(2024)A Survey of Tax Risk Detection Using Data Mining TechniquesEngineering10.1016/j.eng.2023.07.01434(43-59)Online publication date: Mar-2024
https://doi.org/10.1016/j.eng.2023.07.014
Wu YZheng QGao YDong BWei RZhang FHe H(2019)TEDM-PU: A Tax Evasion Detection Method Based on Positive and Unlabeled Learning2019 IEEE International Conference on Big Data (Big Data)10.1109/BigData47090.2019.9006325(1681-1686)Online publication date: Dec-2019
https://doi.org/10.1109/BigData47090.2019.9006325
Coelho H(2019)Reflections on Social Simulation and ComplexityProgress in Artificial Intelligence10.1007/978-3-030-30244-3_52(633-641)Online publication date: 30-Aug-2019
https://doi.org/10.1007/978-3-030-30244-3_52
Tian FLan TChao KGodwin NZheng QShah NZhang F(2016)Mining Suspicious Tax Evasion Groups in Big DataIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2016.257168628:10(2651-2664)Online publication date: 1-Oct-2016
https://dl.acm.org/doi/10.1109/TKDE.2016.2571686
Feng Tian Xing Wan Tian Lan Tianliang Qi Qinghua Zheng Chao KLongzhuang Li (2016)Find the most suspicious tax evasion groups from a taxpayer interest interacted network2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC)10.1109/SMC.2016.7844796(003624-003629)Online publication date: Oct-2016
https://doi.org/10.1109/SMC.2016.7844796
Noguera JQuesada FTapia ELlàcer T(2014)Tax Compliance, Rational Choice, and Social Influence: An Agent-Based ModelRevue française de sociologie10.3917/rfs.554.0765Vol. 55:4(765-804)Online publication date: 10-Dec-2014
https://doi.org/10.3917/rfs.554.0765
Magessi NAntunes L(2014)Shadow Economy and Wealth DistributionAdvances in Artificial Economics10.1007/978-3-319-09578-3_14(169-179)Online publication date: 17-Oct-2014
https://doi.org/10.1007/978-3-319-09578-3_14

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Personal Taxes, Endogenous Default, and Corporate Bond Yield Spreads

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