Modeling tax evasion with genetic algorithms
The U.S. tax gap is estimated to exceed $450 billion, most of which arises from non-compliance on the part of individual taxpayers (GAO 2012; IRS 2006). Much is hidden in innovative tax shelters combining multiple business structures such as partnerships, trusts, and S-corporations into complex tran...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Springer Berlin Heidelberg
2016
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| Online Access: | http://hdl.handle.net/1721.1/104640 https://orcid.org/0000-0002-2153-3506 |
| _version_ | 1826189442037055488 |
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| author | Warner, Geoffrey Wijesinghe, Sanith Marques, Uma Badar, Osama Rosen, Jacob Benjamin Hemberg, Erik O'Reilly, Una-May |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Warner, Geoffrey Wijesinghe, Sanith Marques, Uma Badar, Osama Rosen, Jacob Benjamin Hemberg, Erik O'Reilly, Una-May |
| author_sort | Warner, Geoffrey |
| collection | MIT |
| description | The U.S. tax gap is estimated to exceed $450 billion, most of which arises from non-compliance on the part of individual taxpayers (GAO 2012; IRS 2006). Much is hidden in innovative tax shelters combining multiple business structures such as partnerships, trusts, and S-corporations into complex transaction networks designed to reduce and obscure the true tax liabilities of their individual shareholders. One known gambit employed by these shelters is to offset real gains in one part of a portfolio by creating artificial capital losses elsewhere through the mechanism of “inflated basis” (TaxAnalysts 2005), a process made easier by the relatively flexible set of rules surrounding “pass-through” entities such as partnerships (IRS 2009). The ability to anticipate the likely forms of emerging evasion schemes would help auditors develop more efficient methods of reducing the tax gap. To this end, we have developed a prototype evolutionary algorithm designed to generate potential schemes of the inflated basis type described above. The algorithm takes as inputs a collection of asset types and tax entities, together with a rule-set governing asset exchanges between these entities. The schemes produced by the algorithm consist of sequences of transactions within an ownership network of tax entities. Schemes are ranked according to a “fitness function” (Goldberg in Genetic algorithms in search, optimization, and machine learning. Addison-Wesley, Boston, 1989); the very best schemes are those that afford the highest reduction in tax liability while incurring the lowest expected penalty. |
| first_indexed | 2024-09-23T08:14:44Z |
| format | Article |
| id | mit-1721.1/104640 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T08:14:44Z |
| publishDate | 2016 |
| publisher | Springer Berlin Heidelberg |
| record_format | dspace |
| spelling | mit-1721.1/1046402022-09-23T11:54:15Z Modeling tax evasion with genetic algorithms Warner, Geoffrey Wijesinghe, Sanith Marques, Uma Badar, Osama Rosen, Jacob Benjamin Hemberg, Erik O'Reilly, Una-May Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Badar, Osama Rosen, Jacob Benjamin Hemberg, Erik O'Reilly, Una-May The U.S. tax gap is estimated to exceed $450 billion, most of which arises from non-compliance on the part of individual taxpayers (GAO 2012; IRS 2006). Much is hidden in innovative tax shelters combining multiple business structures such as partnerships, trusts, and S-corporations into complex transaction networks designed to reduce and obscure the true tax liabilities of their individual shareholders. One known gambit employed by these shelters is to offset real gains in one part of a portfolio by creating artificial capital losses elsewhere through the mechanism of “inflated basis” (TaxAnalysts 2005), a process made easier by the relatively flexible set of rules surrounding “pass-through” entities such as partnerships (IRS 2009). The ability to anticipate the likely forms of emerging evasion schemes would help auditors develop more efficient methods of reducing the tax gap. To this end, we have developed a prototype evolutionary algorithm designed to generate potential schemes of the inflated basis type described above. The algorithm takes as inputs a collection of asset types and tax entities, together with a rule-set governing asset exchanges between these entities. The schemes produced by the algorithm consist of sequences of transactions within an ownership network of tax entities. Schemes are ranked according to a “fitness function” (Goldberg in Genetic algorithms in search, optimization, and machine learning. Addison-Wesley, Boston, 1989); the very best schemes are those that afford the highest reduction in tax liability while incurring the lowest expected penalty. Mitre Corporation (Innovation Program) 2016-10-03T19:21:26Z 2016-10-03T19:21:26Z 2014-11 2013-11 2016-08-18T15:36:17Z Article http://purl.org/eprint/type/JournalArticle 1435-6104 1435-8131 http://hdl.handle.net/1721.1/104640 Warner, Geoffrey, Sanith Wijesinghe, Uma Marques, Osama Badar, Jacob Rosen, Erik Hemberg, and Una-May O’Reilly. “Modeling Tax Evasion with Genetic Algorithms.” Econ Gov 16, no. 2 (November 18, 2014): 165-178. https://orcid.org/0000-0002-2153-3506 en http://dx.doi.org/10.1007/s10101-014-0152-7 Economics of Governance Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. Springer-Verlag Berlin Heidelberg application/pdf Springer Berlin Heidelberg Springer Berlin Heidelberg |
| spellingShingle | Warner, Geoffrey Wijesinghe, Sanith Marques, Uma Badar, Osama Rosen, Jacob Benjamin Hemberg, Erik O'Reilly, Una-May Modeling tax evasion with genetic algorithms |
| title | Modeling tax evasion with genetic algorithms |
| title_full | Modeling tax evasion with genetic algorithms |
| title_fullStr | Modeling tax evasion with genetic algorithms |
| title_full_unstemmed | Modeling tax evasion with genetic algorithms |
| title_short | Modeling tax evasion with genetic algorithms |
| title_sort | modeling tax evasion with genetic algorithms |
| url | http://hdl.handle.net/1721.1/104640 https://orcid.org/0000-0002-2153-3506 |
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