Review ArticleOpen Access

Tax Evasion and Multi-Agent-Based Model on Various Topologies

F. W. S. Lima

Dietrich Stauffer Computational Physics Lab, Departamento de Física, Universidade Federal do Piauí, Teresina-PI, 64049-550 Brazil

E-mail Address: fwslima@gmail.com

Search for more papers by this author

https://doi.org/10.1142/S242494241730001XCited by:1 (Source: Crossref)

Abstract

In this work, we use Monte-Carlo simulations to study the control of the fluctuations for tax evasion in the economics model proposed by [G. Zaklan, F. Westerhoff and D. Stauffer, J. Econ. Interact. Coordination. 4 (2009) 1; G. Zaklam, F.W.S. Lima and F. Westerhofd, Physica A387 (2008) 5857.] via a nonequilibrium model with two states ( $- 1, + 1$ ) and a noise $q$ proposed for [M. J. Oliveira, J. Stat. Phys.66 (1992) 273] and known as Majority-Vote model (MVM) and Sánchez–López-Rodríguez model on communities of agents or persons on some topologies as directed and undirected Barabási–Albert networks and Erdös–Rényi random graphs, Apollonian networks, directed small-world networks and Stauffer–Hohnisch–Pittnauer networks. The MVM is applied around the noise critical $q_{c}$ to evolve the Zaklan model.

Keywords:

References

1. K. Bloomquist, Soc. Sci. Comput. Rev. 24 (2006) 411. Crossref, Google Scholar
2. J. Andreoni, B. Erard and J. Feinstein, J. Econ. Lit. 36 (1998) 818. Google Scholar
3. L. Lederman, Public Law Research paper No. 50, (2003). Google Scholar
4. J. Slemrod, J. Econ. Perspective 21 (2007) 25. Crossref, Google Scholar
5. S. Gächter, Discussion Papers 2006-03, CeDEx, University of Nottingham (2006). Google Scholar
6. B. S. Frey and B. Togler, Tax morale and conditional cooperation, J. Comp. Econ. 35 (1) (2007) 136. Crossref, Google Scholar
7. G. Zaklan, F. Westerhoff and D. Stauffer, J. Econ. Interact. Coor. 4 (2009) 1. Crossref, Google Scholar
8. G. Zaklan, F. W. S. Lima and F. Westerhoff, Physica A 387 (2008) 5857. Crossref, Google Scholar
9. M. J. Oliveira, J. Stat. Phys. 66 (1992) 273. Crossref, Google Scholar
10. M. E. J. Newman, S. H. Strogatz and D. J. Watts, Phys. Rev. E 64 (2001) 026118. Crossref, Google Scholar
11. A. D. Sanchez, J. M. Lopez and M. A. Rodriguez, Phys. Rev. Lett. 88 (2002) 048701. Crossref, Google Scholar
12. A.-L. Barabási and R. Albert, Science 286 (1999) 509. Crossref, Google Scholar
13. A. Aleksiejuk, J. A. Hołyst and D. Stauffer, Physica A 310 (2002) 269. Crossref, Google Scholar
14. M. A. Sumour and M. M. Shabat, Int. J. Mod. Phys. C 16 (2005) 585. Link, Google Scholar
15. M. A. Sumour, M. M. Shabat and D. Stauffer, Islam. Univ. J. (Gaza) 14 (2006) 209. Google Scholar
16. F. W. S. Lima and D. Stauffer, Physica A 359 (2006) 423. Crossref, Google Scholar
17. P. Erdös and A. Rényi, Publ. Math. Debrecen 6 (1959) 290; P. Erdös, A. Rényi, Publ. Math. Inst. Hung. Acad. Sci. 5 (1960) 17; P. Erdös and A. Rényi, Bull. Inst. Int. Stat. 38 (1961) 343. Google Scholar
18. J. S. Andrade Jr., H. J. Herrmann, R. F. S. Andrade and L. R. da Silva, Phys. Rev. Lett. 94 (2005) 018702. Crossref, Google Scholar
19. R. F. S. Andrade and H. J. Herrmann, Phys. Rev. E 71 (2005) 05613. Google Scholar
20. B. Balobás, Random Graphs (Academic Press, New York, 1985). Google Scholar
21. D. J. Watts and S. H. Strogatz, Nature 393 (1998) 440. Crossref, Google Scholar
22. S. Wasseman, K. Faust and B. Balobás, Social Networks Analysis (Cambridge University Press, Cambridge, 1994). Crossref, Google Scholar
23. F. W. S. Lima, T. Hadzibeganovic and D. Stauffer, Physica A 388 (2009) 4999. Crossref, Google Scholar
24. T. Qiu, T. Hadzibeganovic, G. Chen, L.-X. Zhong and X.-R. Wu, Comput. Phys. Comm. 181 (2010) 2057. Crossref, Google Scholar
25. D. Stauffer, M. Hohnisch and S. Pittnauer, Physica A 370 (2006) 734. Crossref, Google Scholar
26. F. W. S. Lima, Int. J. Mod. Phys. C. 17 (2006) 1267. Link, Google Scholar
27. F. W. S. Lima, Commun. Comput. Phys. 2 (2007) 358. Google Scholar
28. F. W. S. Lima, Int. J. Mod. Phys. C 19 (2008) 1063. Link, Google Scholar
29. F. P. Fernandes and F. W. S. Lima, Int. J. Mod. Phys. C 19 (2008) 1777. Link, Google Scholar
30. F. W. S. Lima and G. Zaklan, Int. J. Mod Phys. C 19 (2008) 1797. Link, Google Scholar
31. F. W. S. Lima, T. Hadzibeganovic and D. Stauffer, Physica A 388 (2009) 4999. Crossref, Google Scholar
32. F. W. S. Lima, J. Phys. Conf. Ser. 246 (2010) 012033. Crossref, Google Scholar
33. S. Hokamp and M. Pickardt, Int. Econ. J. 24 (2010) 541. Crossref, Google Scholar
34. M. Pickhardt and G. Seibold, Income tax evasion dynamics: Evidence from an agent-based econophysics model, J. Econ. Psychol. 40 (2014) 147. Crossref, Google Scholar
35. F. W. S. Lima, Analysing and controlling the tax evasion dynamics via majority-vote model, J. Phys. Conf. Ser. 246 (2010) 012033. Crossref, Google Scholar
36. R. Wintrobe and K. Gërxhani, Tax Evasion and Trust: A Comparative Analysis, in Proc. Annual Meeting of the European Public Choice Society (Berlin, 2004), p. 15. Google Scholar

Vol. 01, No. 02

Metrics

Downloaded 294 times

History

Received 16 December 2016

Accepted 3 May 2017

Published: 30 June 2017

Information

This is an Open Access article published by World Scientific Publishing Company. It is distributed under the terms of the Creative Commons Attribution 4.0 (CC-BY) License. Further distribution of this work is permitted, provided the original work is properly cited.

Keywords

PDF download

System Upgrade on Tue, May 28th, 2024 at 2am (EDT)

Tax Evasion and Multi-Agent-Based Model on Various Topologies

Abstract

Recommended