Genetic Algorithm and Social Simulation

  • Pinata Winoto
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2417)


Artificial agents have been deployed in simulating social or economic phenomena in order to find optimal policy to govern agents’ society. However, with an increase of the complexity of agents’ internal behaviors as well as their social interactions, modeling social behaviors and tracking down optimal policies in mathematical form become intractable. In this paper, genetic algorithm is used to find optimal solutions to deter criminals in order to reduce the social cost caused by the crimes in the artificial society. The society is characterized by multiple-equilibria and noisy parameters. Sampling evaluation is used to evaluate every candidate. The results of experiments show that genetic algorithms can quickly find the optimal solutions.


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  1. [1]
    Conte, R., Hegselmann, R. and Terna, P. (Eds.) Simulating Social Phenomena. Springer, 1997.Google Scholar
  2. [2]
    Gilbert, N. and Conte, R. (Eds.) Artificial Societies: the computer simulation of social life. UCL Press, London, 1995.Google Scholar
  3. [3]
    Luna, F. and Stefansson, B. (Eds.) Economic Simulations in Swarm: Agent-based modelling and object oriented programming. Kluwer Academic Publishers, 2000.Google Scholar
  4. [4]
    McCain, R. A. Agent-Based Computer Simulation of Dichotomous Economic Growth. Kluwer Academic Publishers, 2000.Google Scholar
  5. [5]
    Sichman, J. S., Conte, R. and Gilbert, N. (Eds.) Multi-Agent Systems and Agent-Based Simulation. Springer, 1998.Google Scholar
  6. [6]
    Tesfatsion, L. Guest editorial agent-based modeling of evolutionary economic systems. IEEE Transactions on Evolutionary Computation, vol. 5(5), Oct. 2001, 437–441.Google Scholar
  7. [7]
    Winoto, P. and Tang, T. Y. Evaluating the Stochastic Properties of Agent Society from Economics’ Perspective. Proceedings of the 2001 IEEE Systems, Man and Cybernetics Conference, Tucson, AZ, USA, vol. 5, Oct. 2001, 2905–2910.Google Scholar
  8. [8]
    Takadama, K., Terano, T. and Shimohara, K., Nongovernance rather than governance in a multiagent economic society. IEEE Transactions on Evolutionary Computation, vol. 5(5), Oct. 2001, 535–545.Google Scholar
  9. [9]
    Bingül, Z, Sekmen, A.•., Palaniappan, S. and Zein-Sabatto, S. Genetic Algorithms Applied to Real Time Multiobjective Optimization Problems. Proceedings of the IEEE Southeast Con 2000, Piscataway, NJ. USA, 2000, 95–103.Google Scholar
  10. [10]
    Stroud, P.D. Kalman-extended genetic algorithm for search in nonstationary environments with noisy fitness evaluations. IEEE Transactions on Evolutionary Computation, vol. 5(1), Feb. 2001, 66–77.Google Scholar
  11. [11]
    Fitzpatrick, J., and Grefenstette, J. Genetic Algorithms in Noisy Environments. Machine Learning, 3, 1988, 101–120.Google Scholar
  12. [12]
    Miller, B.L. and Goldberg, D.E. Optimal Sampling for Genetic Algorithms. IlliGAL Report No. 96005, Univ. of Illinois, Urbana-Champaign, August 1996.Google Scholar
  13. [13]
    Miller, B.L. Noise, Sampling, and Efficient GA’s. Ph.D. dissertation, Univ. of Illinois, Urbana-Champaign, 1997.Google Scholar
  14. [14]
    Nissen, V. and Propach, J. On the robustness of population-based versus point-based optimization in the presence of noise. IEEE Transactions on Evolutionary Computation, vol. 2(3), Sept. 1998, 107–119.Google Scholar
  15. [15]
    Smith, R., Dike, B., and Stegmann, S. Fitness Inheritance in Genetic Algorithms. Proceedings of the ACM Symposium on Applied Computing, New York, NY, 1995, 345–350Google Scholar
  16. [16]
    Sastry, K., Goldberg, D.E., Pelikan, M. Don’t Evaluate, Inherit. IlliGAL Report No. 2001013, Univ. of Illinois, Urbana-Champaign, Jan. 2001.Google Scholar
  17. [17]
    Albert, L.A. and Goldberg, D.E. Efficient Evaluation Genetic Algorithms under Integrated Fitness Functions. IlliGAL Report No. 2001024, Univ. of Illinois, Urbana-Champaign, July 2001.Google Scholar
  18. [18]
    Becker, G. S. Crime and Punishment: An Economic Approach. The Journal of Political Economy; 76(2), 1968, pp. 169–217.CrossRefGoogle Scholar
  19. [19]
    Ehrlich, I. The Deterrent Effect of Criminal Law Enforcement. Journal of Legal Studies, Vol. L(2), June 1972, 259–276.Google Scholar
  20. [20]
    Leung, S. F. Dynamic deterrence theory. Economica, 62, 1995, 65–87.CrossRefGoogle Scholar
  21. [21]
    Polinsky, A. M.. and Shavell, S. On the disutility and discounting of imprisonment and the theory of deterrence. NBER Working Paper, 6259, November 1997.
  22. [22]
    Sah, Raaj K. Social osmosis and patterns of crime. The Journal of Political Economy, 99(6), 1991, 1272–1295.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Pinata Winoto
    • 1
  1. 1.Department of Computer ScienceUniversity of SaskatchewanSaskatoonCanada

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