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Fighting cheaters: How and how much to invest

Published online by Cambridge University Press:  21 April 2010

JUAN CARLOS NUÑO ,
MIGUEL ÁNGEL HERRERO  and
MARIO PRIMICERIO
JUAN CARLOS NUÑO
Affiliation:
Departmento de Matemática Aplicada a los Recursos Naturales, ETSI de Montes, Universidad Politécnica de Madrid, 28040-Madrid, Spain email: [email protected]
MIGUEL ÁNGEL HERRERO
Affiliation:
Institute for Interdisciplinary Mathematics and Departamento de Matemática Aplicada, Facultad de Ciencias Matemáticas, Universidad Complutense de Madrid, 28040 Madrid, Spain
MARIO PRIMICERIO
Affiliation:
Dipartimento di Matematica ‘Ulisse Dini’, Università degli Studi di Firenze, 50134-Firenze, Italy
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Abstract

Human societies are formed by different socio-economical classes which are characterized by their contribution to, and their share of, the common wealth available. Cheaters, defined as those individuals that do not contribute to the common wealth but benefit from it, have always existed, and are likely to be present in all societies in the foreseeable future. Their existence brings about serious problems since they act as sinks for the community wealth and deplete resources which are always limited and often scarce.

To fight cheaters, a society can invest additional resources to pursue one or several aims. For instance, an improvement in social solidarity (e.g. by fostering education) may be sought. Alternatively, deterrence (e.g. by increasing police budget) may be enhanced. Then the following questions naturally arise: (i) how much to spend and (ii) how to allocate the expenditure between both strategies above. This paper addresses this general issue in a simplified setting, which however we believe of some interest. More precisely, we consider a society constituted by two productive classes and an unproductive one, the cheaters, and proposes a dynamical system that describes their evolution in time. We find it convenient to formulate our model as a three-dimensional ordinary differential equation (ODE) system whose variables are the cheater population, the total wealth and one of the productive social classes. The stationary values of the cheater population and the total wealth are studied in terms of the two parameters: φ (how much to invest) and s (how to distribute such expenditure). We show that it is not possible to simultaneously minimize the cheater population and maximize the total wealth with respect to φ and s. We then discuss the possibility of defining a compromise function to find suitable values of φ and s that optimize the response to cheating. In our opinion, this qualitative approach may be of some help to plan and implement social strategies against cheating.

Type
Papers
Information
European Journal of Applied Mathematics , Volume 21 , Issue 4-5: Criminality , October 2010 , pp. 459 - 478
Copyright
Copyright © Cambridge University Press 2010

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References

[1]Azevedo Araujo, R. & Moreira, T. B. S. (1994) A dynamic model of production and traffic of drugs. Econ. Lett. 82, 371–376.CrossRefGoogle Scholar
[2]Ball, P. (2004) The physical modelling of human social systems. ComPlexUs 1, 190–206.CrossRefGoogle Scholar
[3]Becker, G. S. (1968) Crime and punishment: An economic approach. J. Political Econ. 76, 168–217.CrossRefGoogle Scholar
[4]Becker, G. S. (1993) Nobel lecture: The economic way of looking at behavior. J. Political Econ. 101, 385–409.CrossRefGoogle Scholar
[5]Brandt, H., Hauert, C. & Sigmund, K. (2006) Punishing and abstaining for public goods. PNAS 103, 495–497.CrossRefGoogle ScholarPubMed
[6]Campbell, M. & Ormerod, P. (1998) Social interactions and the dynamics of crime. Accessed April 2010. URL: http://www.volterra.co.uk/publications/04/crime.pdfGoogle Scholar
[7]Carpenter, J. P. (2007) The demand for punishment. J. Econ. Behav. Organ. 62, 522–542.CrossRefGoogle Scholar
[8]Fehr, E. & Gachter, S. (2000) Cooperation and punishment in public goods experiments. Am. Econ. Rev. 90, 980–994.CrossRefGoogle Scholar
[9]Feichtinger, G., Grienauer, W. & Tragler, G. (2002) Optimal dynamic law enforcement. Eur. J. Oper. Res. 141, 58–69.CrossRefGoogle Scholar
[10]Grass, D., Caulkins, J. P., Feichtinger, G., Tragler, G. & Behrens, D. A. (2008) Optimal Control of Nonlinear Processes, with Applications in Drugs, Corruption and Terror. Springer Verlag, Berlin.CrossRefGoogle Scholar
[11]Henrich, J., McElreath, R., Barr, A., Ensminger, J., Barrett, C., Bolyanatz, A., Cardenas, J. C., Gurven, M., Gwako, E., Henrich, N., Lesorogol, C., Marlowe, F., Tracer, D. & Ziker, J. (2006) Costly punishment across human societies. Science 312, 1767–1770.CrossRefGoogle ScholarPubMed
[12]Hirsch, M. W. & Smale, S. (1974) Differential Equations, Dynamical Systems and Linear Algebra. Academic press, New York.Google Scholar
[13]Johnson, D., Stopka, P. & Knights, S. (2003) The puzzle of human cooperation (Brief communications). Nature 421, 911–912.CrossRefGoogle Scholar
[14]May, R. M. (2001) Stability and Complexity in Model Ecosystems. Princeton University Press, New Jersey.CrossRefGoogle Scholar
[15]Meacci, L. & Nuño, J. C. (2010) Spatially distributed W-models (in preparation).Google Scholar
[16]Nuño, J. C., Herrero, M. A. & Primicerio, M. (2008) A triangle model of criminality. Physica A 387, 2926–2936.CrossRefGoogle Scholar
[17]Nuño, J. C., Herrero, M. A. & Primicerio, M. (2010) A mathematical model of a criminal-prone society. Discrete Continuous Dyn. Syst. - S (in press).Google Scholar
[18]Ormerod, P., Mounfield, C. & Smith, L. (2001) Non-Linear Modelling of Burglary and Violent Crime in the UK. Volterra Consulting Ltd.Google Scholar
[19]Pierazzini, S. & Nuño, J. C. (2008) Mathematica Demonstration. Accessed April 2010. URL: http://demonstrations.wolfram.com/ATriangleModelOfCriminality/Google Scholar
[20]Reed, W. J. (2001) The Pareto, Zipf and other power law. Economics lett. 74, 15–19.CrossRefGoogle Scholar
[21]Smith, D. A. (1977) Human population growth: Stability or explositon? Math. Mag. 50, 186–197.CrossRefGoogle Scholar
[22]Vargo, L. G. (1966) A note on crime control. Bull. Math. Biophys. 28, 375–378CrossRefGoogle ScholarPubMed
[23]Yu, P. L. (1973) A class of solutions for group decision problems. Manage. Sci. 19, 936–946.CrossRefGoogle Scholar
[24]Zeleny, M. (1974) A concept of compromise solutions and the method of the displaced ideal. Comput. Oper. Res. 1, 479–496.CrossRefGoogle Scholar
[25]Zhao, H., Zhilan, F. & Castillo-Chavez, C. (2002) The dynamics of poverty and crime. MTBI-02-08M.Google Scholar