Exploring NK fitness landscapes using imitative learning

Regular Article

Abstract

The idea that a group of cooperating agents can solve problems more efficiently than when those agents work independently is hardly controversial, despite our obliviousness of the conditions that make cooperation a successful problem solving strategy. Here we investigate the performance of a group of agents in locating the global maxima of NK fitness landscapes with varying degrees of ruggedness. Cooperation is taken into account through imitative learning and the broadcasting of messages informing on the fitness of each agent. We find a trade-off between the group size and the frequency of imitation: for rugged landscapes, too much imitation or too large a group yield a performance poorer than that of independent agents. By decreasing the diversity of the group, imitative learning may lead to duplication of work and hence to a decrease of its effective size. However, when the parameters are set to optimal values the cooperative group substantially outperforms the independent agents.

Keywords

Statistical and Nonlinear Physics 

References

  1. 1.
    E. Wilson, Sociobiology (Harvard University Press, Cambridge, 1975)Google Scholar
  2. 2.
    A. Whiten, D. Erdal, Phil. Trans. R. Soc. B 367, 2119 (2012) CrossRefGoogle Scholar
  3. 3.
    H. Bloom, Global Brain: The Evolution of Mass Mind from the Big Bang to the 21st Century (Wiley, New York, 2001)Google Scholar
  4. 4.
    C.L. Nehaniv, K. Dautenhah, in Imitation and Social Learning in Robots, Humans and Animals (Cambridge University Press, Cambridge, 2007), pp. 1–18Google Scholar
  5. 5.
    J. Kennedy, Adapt. Behav. 7, 269 (1999)CrossRefGoogle Scholar
  6. 6.
    E. Bonabeau, M. Dorigo, G. Theraulaz, Swarm Intelligence: From Natural to Artificial Systems (Oxford University Press, Oxford, 1999)Google Scholar
  7. 7.
    J. Kennedy, J. Conflict Res. 42, 56 (1998)CrossRefGoogle Scholar
  8. 8.
    J.F. Fontanari, Phys. Rev. E 82, 056118 (2010) CrossRefADSGoogle Scholar
  9. 9.
    L. Hong, S.E. Page, J. Econ. Theory 97, 123 (2001)MathSciNetCrossRefMATHGoogle Scholar
  10. 10.
    S.E. Page, The Difference: How the Power of Diversity Creates Better Groups, Firms, Schools, and Societies (Princeton University Press, Princeton, 2007)Google Scholar
  11. 11.
    B.A. Huberman, Physica D 42, 38 (1990)CrossRefADSGoogle Scholar
  12. 12.
    S.H. Clearwater, B.A. Huberman, T. Hogg, Science 254, 1181 (1991) CrossRefADSGoogle Scholar
  13. 13.
    J.F. Fontanari, PLoS One 9, e110517 (2014) CrossRefADSGoogle Scholar
  14. 14.
    R. Axelrod, The Evolution of Cooperation (Basic Books, New York, 1984)Google Scholar
  15. 15.
    S. Kauffman, S. Levin, J. Theor. Biol. 128, 11 (1987)MathSciNetCrossRefGoogle Scholar
  16. 16.
    S. Kauffman, E. Weinberger, J. Theor. Biol. 141, 211 (1989) CrossRefGoogle Scholar
  17. 17.
    D. Solow, A. Burnetas, M. Tsai, N.S. Greenspan, Complex Systems 12, 423 (2000)MathSciNetMATHGoogle Scholar
  18. 18.
    M.R. Garey, D.S. Johnson, Computers and Intractability: A Guide to the Theory of NP-Completeness (Freeman, San Francisco, 1979)Google Scholar
  19. 19.
    B. Derrida, Phys. Rev. B 24, 2613 (1981) MathSciNetCrossRefADSGoogle Scholar
  20. 20.
    D.B. Saakian, J.F. Fontanari, Phys. Rev. E 80, 041903 (2009) CrossRefADSGoogle Scholar
  21. 21.
    Y. Shibanai, S. Yasuno, I. Ishiguro, J. Conflict Res. 45, 80 (2001)CrossRefGoogle Scholar
  22. 22.
    J.C. González-Avella, M.G. Cosenza, M. Eguíluz, M. San Miguel, New J. Phys. 12, 013010 (2010)CrossRefADSGoogle Scholar
  23. 23.
    L.R. Peres, J.F. Fontanari, Europhys. Lett. 96, 38004 (2011) CrossRefADSGoogle Scholar
  24. 24.
    L.R. Peres, J.F. Fontanari, Phys. Rev. E 86, 031131 (2012) CrossRefADSGoogle Scholar
  25. 25.
    R. Axelrod, J. Conflict Res. 41, 203 (1997)CrossRefGoogle Scholar
  26. 26.
    M. Perc, A. Szolnoki, BioSystems 99, 109 (2010)CrossRefGoogle Scholar
  27. 27.
    Z. Wang, L. Wang, M. Perc, Phys. Rev. E 89, 052813 (2014) CrossRefADSGoogle Scholar
  28. 28.
    W.H. Press, S.A. Teukolsky, W.T. Vetterling, B.P. Flannery, in Numerical Recipes in Fortran: The Art of Scientific Computing (Cambridge University Press, Cambridge, 1992), pp. 480–486 Google Scholar
  29. 29.
    W. Feller, in An Introduction to Probability Theory and Its Applications, 3rd edn. (Wiley, New York, 1968), Vol. 1, p. 220Google Scholar
  30. 30.
    R.I.M. Dunbar, J. Human Evol. 22, 469 (1992)CrossRefGoogle Scholar
  31. 31.
    T. Wey, D.T. Blumstein, W. Shen, F. Jordán, Anim. Behav. 75, 333 (2008)CrossRefGoogle Scholar
  32. 32.
    S. Watts, The People’s Tycoon: Henry Ford and the American Century (Vintage, New York, 2006)Google Scholar

Copyright information

© EDP Sciences, SIF, Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.Instituto de Física de São CarlosUniversidade de São PauloSão CarlosBrazil

Personalised recommendations