- Physical Review Journals
All JournalsPhysics Magazine
Access Provided by Xinjiang University

Physical Review E

Export citation

Export citation

Choose format for download:

Download Citation
  • Access by Xinjiang University

Impact of centrality on cooperative processes

Sandro M. Reia1, Sebastian Herrmann2, and José F. Fontanari1

  • 1Instituto de Física de São Carlos, Universidade de São Paulo, Caixa Postal 369, 13560-970 São Carlos, São Paulo, Brazil
  • 2Department of Information Systems and Business Administration, Johannes Gutenberg-Universität, Jakob Welder-Weg 9, 55128 Mainz, Germany

PDF

Phys. Rev. E 95, 022305 – Published 9 February, 2017

DOI: https://doi.org/10.1103/PhysRevE.95.022305

Abstract

The solution of today's complex problems requires the grouping of task forces whose members are usually connected remotely over long physical distances and different time zones. Hence, understanding the effects of imposed communication patterns (i.e., who can communicate with whom) on group performance is important. Here we use an agent-based model to explore the influence of the betweenness centrality of the nodes on the time the group requires to find the global maxima of NK-fitness landscapes. The agents cooperate by broadcasting messages, informing on their fitness to their neighbors, and use this information to copy the more successful agents in their neighborhood. We find that for easy tasks (smooth landscapes), the topology of the communication network has no effect on the performance of the group, and that the more central nodes are the most likely to find the global maximum first. For difficult tasks (rugged landscapes), however, we find a positive correlation between the variance of the betweenness among the network nodes and the group performance. For these tasks, the performances of individual nodes are strongly influenced by the agents' dispositions to cooperate and by the particular realizations of the rugged landscapes.

View figure in article

Physics Subject Headings (PhySH)

Article Text

References (47)

  1. S. E. Page, The Difference: How the Power of Diversity Creates Better Groups, Firms, Schools, and Societies (Princeton University Press, Princeton, NJ, 2007).
  2. A. Bavelas, J. Acoustical Soc. Amer. 22, 725 (1950).
  3. H. J. Leavitt, J. Abnorm. Soc. Psych. 46, 38 (1951).
  4. G. A. Heise and G. A. Miller, J. Abnorm. Soc. Psych. 46, 327 (1951).
  5. H. Guetzkow and H. A. Simon, Manage. Sci. 1, 233 (1955).
  6. M. E. Shaw, J. Abnorm. Soc. Psych. 49, 547 (1954).
  7. M. Mulder, Sociometry 23, 1 (1960).
  8. L. C. Freeman, Sociometry 40, 35 (1977).
  9. W. Mason and D. J. Watts, Proc. Natl. Acad. Sci. USA 109, 764 (2012).
  10. B. A. Huberman, Physica D 42, 38 (1990).
  11. S. H. Clearwater, B. A. Huberman, and T. Hogg, Science 254, 1181 (1991).
  12. S. H. Clearwater, T. Hogg, and B. A. Huberman, in Computation: The Micro and the Macro View, edited by B. A. Huberman (World Scientific, Singapore, 1992), p. 33.
  13. J. F. Fontanari, PLoS ONE 9, e110517 (2014).
  14. S. Blackmore, The Meme Machine (Oxford University Press, Oxford, 2000).
  15. L. Rendell, R. Boyd, D. Cownden, M. Enquist, K. Eriksson, M. W. Feldman, L. Fogarty, S. Ghirlanda, T. Lillicrap, and K. N. Laland, Science 328, 208 (2010).
  16. M. E. J. Newman, Soc. Networks 27, 39 (2005).
  17. J. R. Seeley, Can. J. Psychol. 3, 234 (1949).
  18. M. Shanahan and M. Wildie, PLoS ONE 7, e36579 (2012).
  19. S. A. Kauffman and S. Levin, J. Theor. Biol. 128, 11 (1987).
  20. D. A. Levinthal, Manag. Sci. 43, 934 (1997).
  21. J. W. Rivkin, Manag. Sci. 46, 824 (2000).
  22. D. Lazer and A. Friedman, Admin. Sci. Quart. 52, 667 (2007).
  23. S. Herrmann, J. Grahl, and F. Rothlauf, in Proceedings of the International Workshop on Modeling, Analysis and Management of Social Networks and their Applications, edited by K. Fischbach, M. Grossmann, U. R. Krieger, and T. Staake (, Bamberg, 2014), p. 77.
  24. S. Billinger, N. Stieglitz, and T. R. Schumacher, Organ. Sci. 25, 93 (2013).
  25. B. Derrida, Phys. Rev. B 24, 2613 (1981).
  26. D. B. Saakian and J. F. Fontanari, Phys. Rev. E 80, 041903 (2009).
  27. D. Solow, A. Burnetas, M. Tsai, and N. S. Greenspan, Complex Syst. 12, 423 (2000).
  28. M. R. Garey and D. S. Johnson, Computers and Intractability: A Guide to the Theory of NP-Completeness (Freeman, San Francisco, 1979).
  29. J. F. Fontanari, Eur. Phys. J. B 88, 251 (2015).
  30. Y. Shibanai, S. Yasuno, and I. Ishiguro, J. Conflict Res. 45, 80 (2001).
  31. L. R. Peres and J. F. Fontanari, Europhys. Lett. 96, 38004 (2011).
  32. R. Axelrod, J. Conflict Res. 41, 203 (1997).
  33. J. F. Fontanari, Europhys. Lett. 113, 28009 (2016).
  34. A. Bandura, Social Learning Theory (Prentice Hall, New York, 1977).
  35. M. Girvan and M. E. J. Newman, Proc. Natl. Acad. Sci. USA 99, 7821 (2002).
  36. R. Albert and A.-L. Barabási, Rev. Mod. Phys. 74, 47 (2002).
  37. S. M. Reia and J. F. Fontanari, Ecol. Complex., doi:10.1016/j.ecocom.2016.09.002.
  38. M. Nagy, Z. Akos, D. Biro, and T. Vicsek, Nature (London) 464, 890 (2010).
  39. M. E. J. Newman, Proc. Natl. Acad. Sci. USA 103, 8577 (2006).
  40. M. Kitsak, L. K. Gallos, S. Havlin, F. Liljeros, L. Muchnik, H. E. Stanley, and H. A. Makse, Nat. Phys. 6, 888 (2010).
  41. J. F. Fontanari and F. A. Rodrigues, Theory Biosci. 135, 101 (2016).
  42. W. A. Mason, A. Jones, and R. L. Goldstone, J. Exp. Psychol. Gen. 137, 422 (2008).
  43. C. Fang, J. Lee, and M. A. Schilling, Organ. Sci. 21, 625 (2010).
  44. R. Axelrod, The Evolution of Cooperation (Basic Books, New York, 1984).
  45. I. L. Janis, Groupthink: Psychological Studies of Policy Decisions and Fiascoes (Houghton Mifflin, Boston, 1982).
  46. N. Perra, B. Goncalves, R. Pastor-Satorras, and A. Vespignani, Sci. Rep. 2, 469 (2012).
  47. W. B. Lindquist and I. D. Chase, Bull. Math. Biol. 71, 556 (2009).

Outline

Information

Phys. Rev. E 95, 022305– Published 9 February, 2017
  • Received 31 October 2016
  • Revised 23 January 2017

©2017 American Physical Society

Sign In to Your Journals Account

Filter

Filter

Article Lookup

Enter a citation