Publication:
Fighting cheaters: How and how much to invest

Loading...
Thumbnail Image
Full text at PDC
Publication Date
2010-10
Advisors (or tutors)
Editors
Journal Title
Journal ISSN
Volume Title
Publisher
Cambridge University Press
Citations
Google Scholar
Research Projects
Organizational Units
Journal Issue
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: phi (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 phi and s. We then discuss the possibility of defining a compromise function to find suitable values of phi 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.
Description
Keywords
Citation
AZEVEDO ARAUJO, R. & MOREIRA, T. B. S. (1994) A dynamic model of production and traffic of drugs. Econ. Lett. 82, 371-376. BALL, P. (2004) The physical modelling of human social systems. ComPlexUs 1, 190-206. BECKER, G. S. (1968) Crime and punishment: An economic approach. J. Political Econ. 76, 168-217. BECKER, G. S. (1993) Nobel lecture: The economic way of looking at behavior. J. Political Econ. 101, 385-409. BRANDT, H., HAUERT, C. & SIGMUND, K. (2006) Punishing and abstaining for public goods. PNAS 103, 495-197. CAMPBELL, M. & ORMEROD, P. (1998) Social interactions and the dynamics of crime. Accessed April 2010. URL: http://www.volterra.co.uk/publications/04/crime.pdf CARPENTER, J. P. (2007) The demand for punishment. J. Econ. Behav. Organ. 62, 522-542. FEHR, E. & GACHTER, S. (2000) Cooperation and punishment in public goods experiments. Am. Econ. Rev. 90, 980-994. FEICHTINGER, G., GRIENAUER, W. & TRAGLER, G. (2002) Optimal dynamic law enforcement. Eur. J. Oper. Res. 141, 58-69. 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. 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. HIRSCH, M. W. & SMALE, S. (1974) Differential Equations, Dynamical Systems and Linear Algebra. Academic press, New York. JOHNSON, D., STOPKA, P. & KNIGHTS, S. (2003) The puzzle of human cooperation (Brief communications). Nature 421, 911-912. MAY, R. M. (2001) Stability and Complexity in Model Ecosystems. Princeton University Press, New Jersey. MEACCI, L. & NUÑO, J. C. (2010) Spatially distributed W-models (In preparation). NUÑO, J. C, HERRERO, M. A. & PRIMICERIO, M. (2008) A triangle model of criminality. Physica A 387, 2926-2936. NUÑO, J. C, HERRERO, M. A. & PRIMICERIO, M. (2010) A mathematical model of a criminalprone society. Discrete Continuous Dyn. Syst. - S (in press). ORMEROD, P., MOUNFIELD, C. & SMITH, L. (2001) Non-Linear Modelling of Burglary and Violent Crime in the UK. Volterra Consulting Ltd. PIERAZZINI, S. & NUÑO, J. C. (2008) Mathematica Demonstration. Accessed April 2010. URL: http://demonstrations.wolfram.com/ATriangleModelOfCriminality/ REED, W. J. (2001) The Pareto, Zipf and other power law. Economics lett. 74, 15-19. SMITH, D. A. (1977) Human population growth: Stability or explositon? Math. Mag. 50, 186-197. VARGO, L. G. (1966) A note on crime control. Bull. Math. Biophys. 28, 375-378 Yu, P. L. (1973) A class of solutions for group decision problems. Manage. Sci. 19, 936-946. ZELENY, M. (1974) A concept of compromise solutions and the method of the displaced ideal. Comput. Oper. Res. 1, 479-496. ZHAO, H., ZHILAN, F & CASTILLO-CHAVEZ, C. (2002) The dynamics of poverty and crime. MTBI-02-08M.
Collections