IEEE Computational Intelligence Magazine - May 2021 - 76

game dynamics, " Commun. Nonlinear Sci. Numer. Simul.,
vol. 79, 104,870, 2019. doi: 10.1016/j.cnsns.2019.104870.
[13] H. Dawid, H. La Poutre, and X. Yao, " Computational intelligence in economic games and policy design, "
IEEE Comput. Intell. Mag., vol. 3, no. 4, pp. 22-26, 2008.
doi: 10.1109/MCI.2008.929845.
[14] M. Pickhardt and A. Prinz, " Behavioral dynamics of
tax evasion-a survey, " J. Econ. Psychol., vol. 40, pp. 1-19,
2014. doi: 10.1016/j.joep.2013.08.006.
[15] A. Antoci, P. Russu, and L. Zarri, " Tax evasion in
a behaviorally heterogeneous society: An evolutionary
analysis, " Econ. Model., vol. 42, pp. 106-115, 2014. doi:
10.1016/j.econmod.2014.06.002.
[16] L. S. Di Mauro, A. Pluchino, and A. E. Biondo, " Tax
evasion as a contagion game: evidences from an agentbased model, " Euro. Phys. J. B, vol. 92, no. 5, 103, 2019.
doi: 10.1140/epjb/e2019-90722-9.
[17] D. De Giovanni, F. Lamantia, and M. Pezzino, " A behavioral model of evolutionary dynamics and optimal regulation of tax evasion, " Struct. Change Econ. Dynam., vol.
50, pp. 79-89, 2019. doi: 10.1016/j.strueco.2019.05.004.
[18] C. M. Macal and M. J. North, " Tutorial on agentbased modeling and simulation, " in Proc. 37th Conf. Winter
Simul., 2005, pp. 2-15.
[19] M. Chica and W. Rand, " Building agent-based decision support systems for word-of-mouth programs. A
freemium application, " J. Market. Res., vol. 54, no. 5,
pp. 752-767, Oct. 2017. doi: 10.1509/jmr.15.0443.
[20] H. Abbass, A. Bender, S. Gaidow, and P. Whitbread,
" Computational red teaming: Past, present and future, "
IEEE Comput. Intell. Mag., vol. 6, no. 1, pp. 30-42, 2011.
doi: 10.1109/MCI.2010.939578.
[21] M. Newman, A.-L. Barabási, and D. J. Watts, The
Structure and Dynamics of Networks. Princeton Univ. Press,
2006.
[22] F. C. Santos, M. D. Santos, and J. M. Pacheco, " Social diversity promotes the emergence of cooperation in
public goods games, " Nature, vol. 454, no. 7201, pp. 213-
216, 2008. doi: 10.1038/nature06940.
[23] M. A. Amaral, L. Wardil, M. Perc, and J. K. da Silva,
" Evolutionary mixed games in structured populations:
Cooperation and the benefits of heterogeneity, " Phys.
Rev. E, vol. 93, no. 4, 042304, 2016. doi: 10.1103/PhysRevE.93.042304.
[24] A. D. Broido and A. Clauset, " Scale-free networks
are rare, " Nature Commun., vol. 10, no. 1, pp. 1-10, 2019.
doi: 10.1038/s41467-019-08746-5.
[25] M. G. Allingham and A. Sandmo, " Income tax
evasion: A theoretical analysis, " J. Public Econ., vol.  1,
nos. 3-4, pp. 323-338, 1972. doi: 10.1016/00472727(72)90010-2.
[26] M. Bordignon, " A fairness approach to income tax, "
J. Public Econ., vol. 52, no. 3, pp. 345-362, 1993. doi:
10.1016/0047-2727(93)90039-V.
[27] C. Bazart and A. Bonein, " Reciprocal relationships
in tax compliance decisions, " J. Econ. Psychol., vol. 40,
pp. 83-102, 2014. doi: 10.1016/j.joep.2012.10.002.
[28] C. Traxler, " European Journal of Political Economy
Social norms and conditional cooperative taxpayers, "
Euro. J. Polit. Econ., vol. 26, no. 1, pp. 89-103, 2010. doi:
10.1016/j.ejpoleco.2009.11.001.

Publication Spotlight

Public Finance Rev., vol. 39, no. 1, pp. 25-49, 2011. doi:
10.1177/1091142110381640.
[47] M. Pickhardt and G. Seibold, " Income tax evasion
dynamics: Evidence from an agent-based econophysics
model, " J. Econ. Psychol., vol. 40, pp. 147-160, 2014. doi:
10.1016/j.joep.2013.01.011.
[48] A. L. Andrei, K. Comer, and M. Koehler, " An agentbased model of network effects on tax compliance and
evasion, " J. Econ. Psychol., vol. 40, pp. 119-133, 2014. doi:
10.1016/j.joep.2013.01.002.
[49] K. M. Bloomquist, " Incorporating indirect effects
in audit case selection: An agent-based approach, " IRS
Document, pp. 1-14, 2012.
[50] L. Wardil and J. K. da Silva, " The evolution of
cooperation in mixed games, " Chaos Solitons Fractals, vol. 56, pp. 160-165, 2013. doi: 10.1016/j.chaos.
2013.07.018.
[51] B. Allen and M. A. Nowak, " Games among relatives
revisited, " J. Theor. Biol., vol. 378, pp. 103-116, 2015. doi:
10.1016/j.jtbi.2015.04.031.
[52] M. A. Nowak, C. E. Tarnita, and T. Antal, " Evolutionary dynamics in structured populations, " Philosoph.
Trans. Roy. Soc. Lond. B, Biol. Sci., vol. 365, no. 1537,
pp. 19-30, 2010. doi: 10.1098/rstb.2009.0215.
[53] A. R. Góis, F. P. Santos, J. M. Pacheco, and F. C.
Santos, " Reward and punishment in climate change
dilemmas, " Sci. Rep., vol. 9, no. 1, pp. 1-9, 2019. doi:
10.1038/s41598-019-52524-8.
[54] V. V. Vasconcelos, F. C. Santos, J. M. Pacheco, and S.
A. Levin, " Climate policies under wealth inequality, " Proc.
Nat. Acad. Sci., vol. 111, no. 6, pp. 2212-2216, 2014. doi:
10.1073/pnas.1323479111.
[55] A. Clauset, C. R. Shalizi, and M. E. Newman,
" Power-law distributions in empirical data, " SIAM
Rev., vol.  51, no. 4, pp. 661-703, 2009. doi: 10.1137/
070710111.
[56] IPYME, Estadísticas pyme: Evolución e indicadores.
www.ipyme.org. Gobierno de España, Mar. 2019.
[57] A. L. Barabási and R. Albert, " Emergence of scaling in random networks, " Science, vol. 286, no. 5439,
pp. 509-512, 1999. doi: 10.1126/science.286.5439.509.
[58] R. Xulvi-Brunet and I. M. Sokolov, " Reshuff ling
scale-free networks: From random to assortative, " Phys.
Rev. E, vol. 70, no. 6, 066102, 2004. doi: 10.1103/PhysRevE.70.066102.
[59] A. Antonioni, L. A. Martinez-Vaquero, C. Mathis,
L. Peel, and M. Stella, " Individual perception dynamics
in drunk games, " Phys. Rev. E, vol. 99, no. 5, 052311,
2019. doi: 10.1103/PhysRevE.99.052311.
[60] E. Fehr and K. M. Schmidt, " Adding a stick to the carrot? the interaction of bonuses and fines, " Amer. Econ. Rev.,
vol. 97, no. 2, pp. 177-181, 2007. doi: 10.1257/aer.97.2.177.
[61] X. Chen, T. Sasaki, Å. Brännström, and U. Dieckmann,
" First carrot, then stick: how the adaptive hybridization of incentives promotes cooperation, " J. Roy. Soc. Interface, vol. 12,
no. 102, 20140935, 2015. doi: 10.1098/rsif.2014.0935.
[62] J. F. Robles, M. Chica, and O. Cordon, " Evolutionary multiobjective optimization to target social network
inf luentials in viral marketing, " Expert Syst. Appl., vol.
147, 113183, 2020.


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" Real-world optimization problems,
such as aerodynamic design of turbine
engines and automated trading, have
been successfully solved by metaheuristics. However, practitioners are confronted with the challenge of how to
choose an appropriate metaheuristic
algorithm to solve a particular instance
of these problems. This paper proposes a

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[29] J. Alm, " Measuring, explaining, and controlling
tax evasion: lessons from theory, experiments, and field
studies, " Int. Tax Public Finance, vol. 19, no. 1, pp. 54-77,
2012. doi: 10.1007/s10797-011-9171-2.
[30] J. Alm, K. M. Bloomquist, and M. McKee, " On the
external validity of laboratory tax compliance experiments, " Econ. Inquiry, vol. 53, no. 2, pp. 1170-1186, 2015.
doi: 10.1111/ecin.12196.
[31] A. Bonein, " La réciprocité, entre psychologie et
rationalité économique, " Revue Française d'économie,
vol. 23, no. 1, pp. 203-232, 2018. doi: 10.3406/rfeco.
2008.1664.
[32] H. Gintis, " Strong Reciprocity and Human Sociality, " J. Theor. Biol., vol. 206, no. 2, pp. 169-179, 2000.
doi: 10.1006/jtbi.2000.2111.
[33] B. S. Frey and B. Torgler, " Tax morale and conditional
cooperation, " J. Comp. Econ., vol. 35, no. 1, pp. 136-159,
2007. doi: 10.1016/j.jce.2006.10.006.
[34] K. Blaufus, J. Bob, P. E. Otto, and N. Wolf,
" The effect of tax privacy on tax compliance-An
experimental investigation the effect of tax privacy
on tax compliance-An experimental investigation, "
Euro. Acc. Rev., vol. 26, no. 3, pp. 561-580, 2017. doi:
10.1080/09638180.2016.1258319.
[35] R. Calvet and J. Alm, " Empathy, sympathy, and tax
compliance, " J. Econ. Psychol., vol. 40, pp. 62-82, 2014.
doi: 10.1016/j.joep.2012.10.001.
[36] S. Fedeli and F. Forte, " Joint income-tax and
VAT-chain evasion, " Euro. J. Polit. Econ., vol. 15, no. 3,
pp. 391-415, 1999. doi: 10.1016/S0176-2680(99)00019-1.
[37] M. Abraham, K. Lorek, F. Richter, and M. Wrede,
" Collusive tax evasion and social norms, " Int. Tax Public
Finance, vol. 24, no. 2, pp. 179-197, 2017. doi: 10.1007/
s10797-016-9417-0.
[38] R. Boadway, N. Marceau, and S. Mongrain, " Joint
tax evasion, " Can. J. Econ., vol. 35, no. 3, pp. 417-435,
2002. doi: 10.1111/1540-5982.00138.
[39] K. M. Bloomquist, " A comparison of agent-based models of income tax evasion, " Sci. Comput. Rev., vol. 24, no. 4,
pp. 411-425, 2006. doi: 10.1177/0894439306287021.
[40] S. Hokamp, L. Gulyas, M. Koehler, and S. Wijesinghe, Agent-Based Modeling of Tax Evasion. Wiley, 2018.
[41] S.-H. Chen, " Software-agent designs in economics: An
interdisciplinary, " IEEE Comput. Intell. Mag., vol. 3, no. 4,
pp. 18-22, 2008. doi: 10.1109/MCI.2008.929844.
[42] C. Korobow, A. Johnson, and R. Axtell, " An agent-based
model of tax compliance with social networks, " Nat.Tax J., vol.
60, no. 3, pp. 411-425, 2007. doi: 10.17310/ntj.2007.3.16.
[43] N. Hashimzade, G. Myles, F. Page, and M. Rablen, " Social
networks and occupational choice: The endogenous formation
of attitudes and beliefs about tax compliance, " J. Econ. Psychol.,
vol. 40, pp. 134-146, 2014. doi: 10.1016/j.joep.2012.09.002.
[44] N. Hashimzade, G. D. Myles, F. Page, and M.
Rablen, " The use of agent-based modelling to investigate
tax compliance, " Econ. Govern., vol. 16, no. 2, pp. 143-
164, 2015. doi: 10.1007/s10101-014-0151-8.
[45] N. Hashimzade and G. Myles, " Risk-based audits in
a behavioral model, " Public Finance Rev., vol. 45, no. 1, pp.
140-165, 2017. doi: 10.1177/1091142115602062.
[46] K. Bloomquist, " Tax compliance as an evolutionary coordination game: An agent-based approach, "

recommender system that can automatically select a best-suited metaheuristic
algorithm without trial and error on a
given problem. The proposed method
develops a generic tree-like data structure for representing the difficulties of
optimization problems and then trains a
deep recurrent neural network to learn
to choose the best metaheuristic algo-

IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE | MAY 2021

rithm, making automated algorithm
recommendation practical for realworld problem-solving. The method
will make metaheuristic optimization
techniques accessible to industrial practitioners, policy makers, and other
stakeholders who have no knowledge in
metaheuristic algorithms. "
http://www.ipyme.org
IEEE Computational Intelligence Magazine - May 2021

Table of Contents for the Digital Edition of IEEE Computational Intelligence Magazine - May 2021
