Policies for allocation of information in task-oriented groups: elitism and egalitarianism outperform welfarism

  • Sandro M. Reia
  • Paulo F. Gomes
  • José F. FontanariEmail author
Regular Article


Communication or influence networks are probably the most controllable of all factors that are known to impact on the problem-solving capability of task-forces. In the case connections are costly, it is necessary to implement a policy to allocate them to the individuals. Here we use an agent-based model to study how distinct allocation policies affect the performance of a group of agents whose task is to find the global maxima of NK fitness landscapes. Agents cooperate by broadcasting messages informing on their fitness and use this information to imitate the fittest agent in their influence neighborhoods. The larger the influence neighborhood of an agent, the more links, and hence information, the agent receives. We find that the elitist policy in which agents with above-average fitness have their influence neighborhoods amplified, whereas agents with below-average fitness have theirs deflated, is optimal for smooth landscapes, provided the group size is not too small. For rugged landscapes, however, the elitist policy can perform very poorly for certain group sizes. In addition, we find that the egalitarian policy, in which the size of the influence neighborhood is the same for all agents, is optimal for both smooth and rugged landscapes in the case of small groups. The welfarist policy, in which the actions of the elitist policy are reversed, is always suboptimal, i.e., depending on the group size it is outperformed by either the elitist or the egalitarian policies.

Graphical abstract


Statistical and Nonlinear Physics 


  1. 1.
    S. Reijula, J. Kuorikoski, Modeling epistemic communities, in The Routledge Handbook of Social Epistemology, edited by M. Fricker, P.J. Graham, D. Henderson, N.J.L.L. Pedersen (Routledge, Abingdon, UK, 2019) Google Scholar
  2. 2.
    P. Kitcher, The Advancement of Science: Science Without Legend, Objectivity Without Illusions (Oxford University Press, New York, 1993) Google 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.
    D. Lazer, A. Friedman, Admin. Sci. Quart. 52, 667 (2007) CrossRefGoogle Scholar
  5. 5.
    R.L. Goldstone, M.E. Roberts, W. Mason, T. Gureckis, Collective search in concrete and abstract spaces, in Decision Modeling and Behavior in Complex and Uncertain Environments, edited by T. Kugler, J.C. Smith, T. Connolly, Y.-J. Son (Springer, New York, 2008), pp. 277–308 Google Scholar
  6. 6.
    J.F. Fontanari, PLoS ONE 9, e110517 (2014) ADSCrossRefGoogle Scholar
  7. 7.
    J.F. Fontanari, Eur. Phys. J. B 88, 251 (2015) ADSCrossRefGoogle Scholar
  8. 8.
    S.M. Reia, A.C. Amado, J.F. Fontanari, Phys. Life Rev., (2019)
  9. 9.
    E. Gilbert, SIAM J. Appl. Math. 9, 533 (1961) CrossRefGoogle Scholar
  10. 10.
    S.A. Kauffman, S. Levin, J. Theor. Biol. 128, 11 (1987) CrossRefGoogle Scholar
  11. 11.
    R.K. Merton, The Sociology of Science: Theoretical and Empirical Investigations (University of Chicago Press, Chicago, 1973) Google Scholar
  12. 12.
    M. Strevens, J. Philos. 100, 55 (2003) CrossRefGoogle Scholar
  13. 13.
    M. Perc, J.J. Jordan, D.G. Rand, Z. Wang, S. Boccaletti, S. Attila, Phys. Rep. 687, 1 (2017) ADSMathSciNetCrossRefGoogle Scholar
  14. 14.
    S.A. Kauffman, At Home in the Universe: The Search for Laws of Self-Organization and Complexity (Oxford University Press, New York, 1995) Google Scholar
  15. 15.
    H. Kaul, S.H. Jacobson, Math. Program. 108, 475 (2006) MathSciNetCrossRefGoogle Scholar
  16. 16.
    D. Solow, A. Burnetas, M. Tsai, N.S. Greenspan, Complex Syst. 12, 423 (2000) Google Scholar
  17. 17.
    W. Hordijk, S.A. Kauffman, P.F. Stadler, Theory Biosci., (2019)
  18. 18.
    M. Starnini, A. Baronchelli, R. Pastor-Satorras, Phys. Rev. Lett. 110, 168701 (2013) ADSCrossRefGoogle Scholar
  19. 19.
    P.F Gomes, S.M. Reia, F.A. Rodrigues, J.F. Fontanari, Phys. Rev. E 99, 032301 (2019) ADSCrossRefGoogle Scholar
  20. 20.
    J.F. Fontanari, Europhys. Lett. 113, 28009 (2016) ADSCrossRefGoogle Scholar
  21. 21.
    Y. Shibanai, S. Yasuno, I. Ishiguro, J. Conflict Resolut. 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) ADSCrossRefGoogle Scholar
  23. 23.
    L.R. Peres, J.F. Fontanari, Europhys. Lett. 96, 38004 (2011) ADSCrossRefGoogle Scholar
  24. 24.
    R. Axelrod, J. Conflict Resolut. 41, 203 (1997) CrossRefGoogle Scholar
  25. 25.
    R.K. Merton, Science 159, 56 (1968) ADSCrossRefGoogle Scholar
  26. 26.
    M. Newman, Networks: An Introduction (Oxford University Press, New York, 2010) Google Scholar
  27. 27.
    D. Stauffer, A. Aharony, Introduction to Percolation Theory (Taylor & Francis, London, 1992) Google Scholar
  28. 28.
    I.L. Janis, Groupthink: Psychological Studies of Policy Decisions and Fiascoes (Houghton Mifflin, Boston, 1982) Google Scholar
  29. 29.
    T. Malone, R. Laubacher, C. Dellarocas, MIT Sloan Manag. Rev. 51, 1 (2010) Google Scholar
  30. 30.
    B.A. Huberman, Physica D 42, 38 (1990) ADSCrossRefGoogle Scholar
  31. 31.
    A. Bavelas, J. Acoust. Soc. Am. 22, 725 (1950) ADSCrossRefGoogle Scholar
  32. 32.
    H.J. Leavitt, J. Abnorm. Soc. Psychol. 46, 38 (1951) CrossRefGoogle Scholar
  33. 33.
    S.M. Reia, P.F Gomes, J.F. Fontanari, Eur. Phys. J. B 92, 109 (2019) ADSCrossRefGoogle Scholar
  34. 34.
    W. Mason, D.J. Watts, Proc. Natl. Acad. Sci. 109, 764 (2012) ADSCrossRefGoogle Scholar
  35. 35.
    S.M. Reia, S. Herrmann, J.F. Fontanari, Phys. Rev. E 95, 022305 (2017) ADSCrossRefGoogle Scholar
  36. 36.
    J.S. Waters, J.H. Fewell, PLoS ONE 7, e40337 (2012) ADSCrossRefGoogle Scholar
  37. 37.
    C. Pasquaretta, M. Levé, N. Claidière, E. van de Waal, A. Whiten, A.J.J. MacIntosh, M. Pelé, M.L. Bergstrom, C. Borgeaud, S.F. Brosnan, M.C. Crofoot, L.M. Fedigan, C. Fichtel, L.M. Hopper, M.C. Mareno, O. Petit, A.V. Schnoell, E.P. di Sorrentino, B. Thierry, B. Tiddi, C. Sueur, Sci. Rep. 4, 7600 (2014) CrossRefGoogle Scholar
  38. 38.
    R.H.J.M. Kurvers, J. Krause, D.P. Croft, A.D.M. Wilson, M. Wolf, Trends Ecol. Evol. 29, 326 (2014) CrossRefGoogle Scholar
  39. 39.
    M.E. Dickison, M. Magnani, L. Rossi, Multilayer Social Networks (Cambridge University Press, Cambridge, 2016) Google Scholar
  40. 40.
    J.F. Fontanari, F.A. Rodrigues, Theory Biosci. 135, 101 (2016) CrossRefGoogle Scholar

Copyright information

© EDP Sciences / Società Italiana di Fisica / Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Instituto de Física de São Carlos, Universidade de São PauloSão PauloBrazil
  2. 2.Instituto de Ciências Exatas e Tecnológicas, Universidade Federal de GoiásGoiásBrazil

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