, 89:437 | Cite as

Does cumulative advantage affect collective learning in science? An agent-based simulation

  • Christopher WattsEmail author
  • Nigel Gilbert


Agent-based simulation can model simple micro-level mechanisms capable of generating macro-level patterns, such as frequency distributions and network structures found in bibliometric data. Agent-based simulations of organisational learning have provided analogies for collective problem solving by boundedly rational agents employing heuristics. This paper brings these two areas together in one model of knowledge seeking through scientific publication. It describes a computer simulation in which academic papers are generated with authors, references, contents, and an extrinsic value, and must pass through peer review to become published. We demonstrate that the model can fit bibliometric data for a token journal, Research Policy. Different practices for generating authors and references produce different distributions of papers per author and citations per paper, including the scale-free distributions typical of cumulative advantage processes. We also demonstrate the model’s ability to simulate collective learning or problem solving, for which we use Kauffman’s NK fitness landscape. The model provides evidence that those practices leading to cumulative advantage in citations, that is, papers with many citations becoming even more cited, do not improve scientists’ ability to find good solutions to scientific problems, compared to those practices that ignore past citations. By contrast, what does make a difference is referring only to publications that have successfully passed peer review. Citation practice is one of many issues that a simulation model of science can address when the data-rich literature on scientometrics is connected to the analogy-rich literature on organisations and heuristic search.


Simulation Cumulative advantage Landscape search Science models Science policy 

Mathematic subject classification

91D10 (primary) 91D30 90B70 

JEL classification

C63 D83 D85 



This research was supported by SIMIAN (Simulation Innovation: A Node), a part of the UK’s National Centre for Research Methods, funded by the Economic and Social Research Council.


  1. Ahrweiler, P., & Gilbert, G. N. (2005). Caffè Nero: The evaluation of social simulation. Journal of Artificial Societies and Social Simulation, 8(4), 14. Retrieved 26, Feb 2010 from
  2. Axelrod, R. (1997). The complexity of cooperation: agent-based models of competition and collaboration. Princeton: Princeton University Press.Google Scholar
  3. Barabási, A.-L., & Albert, R. (1999). Emergence of scaling in random networks. Science, 286, 509–512.MathSciNetCrossRefGoogle Scholar
  4. Bentley, R. A., Ormerod, P., & Batty, M. (2009). An evolutionary model of long tailed distributions in the social sciences. arXiv:0903.2533v1 [physics.soc-ph] March 14, 2009. Retrieved 1, May 2010 from
  5. Boerner, K., Maru, J. T., & Goldstone, R. L. (2004). The simultaneous evolution of author and paper networks. Proceedings of the National Academy of Science USA, 101(suppl. 1), S266–S273.Google Scholar
  6. Bornmann, L. (2010). Does the journal peer review select the ‘best’ from the work submitted? The state of empirical research. IETE Technical Review, 27(2), 93–96.CrossRefGoogle Scholar
  7. Burrell, Q. L. (2001). Stochastic modelling of the first-citation distribution. Scientometrics, 52(1), 3–12.CrossRefGoogle Scholar
  8. Burrell, Q. L. (2007). Hirsch’s h-index: a stochastic model. Journal of Informetrics, 1, 16–25.CrossRefGoogle Scholar
  9. Burt, R. (2005). Brokerage and closure: an introduction to social capital. Oxford: Oxford Univerity Press.Google Scholar
  10. Castellano, C., Marsili, M., & Vespignani, A. (2000). Nonequilibrium phase transition in a model for social influence. Physical Review Letters, 85(16), 3536–3539.CrossRefGoogle Scholar
  11. Chen, C., Chen, Y., Horowitz, H., Hou, H., Liu, Z., & Pellegrino, D. (2009). Towards an explanatory and computational theory of scientific discovery. Journal of Informetrics, 3, 191–209.CrossRefGoogle Scholar
  12. Clerc, M. (2006). Particle swarm optimisation. London: ISTE.CrossRefGoogle Scholar
  13. Clerc, M., & Kennedy, J. (2002). The particle swarm—explosion, stability, and convergence in a multidimensional complex space. IEEE Transactions on Evolutionary Computation, 6(1), 58–73.CrossRefGoogle Scholar
  14. Collins, R. (1998). The sociology of philosophies: a global theory of intellectual change. London: Belknap Press, Harvard University Press.Google Scholar
  15. Corne, D., Dorigo, M., & Glover, F. (Eds.). (1999). New ideas in optimisation. London: McGraw-Hill.Google Scholar
  16. Cyert, R. M., & March, J. G. (1963). A behavioral theory of the firm. Englewood Cliffs: Prentice-Hall.Google Scholar
  17. Fuller, S. (2000). The governance of science. Buckingham: Open University Press.Google Scholar
  18. Gilbert, N. (1997). A simulation of the structure of academic science. Sociological Research Online, 2(2), 3. Retrieved 10, Feb 2009 from
  19. Gilbert, G. N., & Troitzsch, K. G. (2005). Simulation for the social scientist. Maidenhead: Open University Press.Google Scholar
  20. Glaenzel, W., & Schubert, A. (1990). The cumulative advantage function. A mathematical formulation based on conditional expectations and its application to scientometric distributions. Informetrics, 89/90, 139–147.Google Scholar
  21. Glaenzel, W., & Schubert, A. (1995). Predictive aspects of a stochastic model for citation processes. Information Processing and Management, 31(1), 69–80.CrossRefGoogle Scholar
  22. Glover, F. (1989). Tabu Search—Part I. ORSA Journal on Computing, 1(3), 190–206.zbMATHGoogle Scholar
  23. Glover, F. (1990). Tabu search—Part II. ORSA Journal on Computing, 2(1), 4–32.zbMATHGoogle Scholar
  24. Hegselmann, R., & Krause, U. (2002). Opinion dynamics and bounded confidence models, analysis, and simulation. Journal of Artificial Societies and Social Simulation, 5(3), 2. Retrieved 28, Sep 2009 from
  25. Jin, Y., & Branke, J. (2005). Evolutionary optimization in uncertain environments–a survey. IEEE Transactions on Evolutionary Computation, 9(3), 303–317.CrossRefGoogle Scholar
  26. Kahneman, D., Slovic, P., & Tversky, A. (1982). Judgment under uncertainty: Heuristics and biases. Cambridge: Cambridge University Press.Google Scholar
  27. Kauffman, S. (1993). The origins of order: self-organization and selection in evolution. New York: Oxford University Press.Google Scholar
  28. Kauffman, S. (1995). At home in the universe: the search for laws of complexity. London: Penguin.Google Scholar
  29. Kauffman, S. (2000). Investigations. Oxford: Oxford University Press.Google Scholar
  30. Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by simulated annealing. Science, 220, 671–680.MathSciNetCrossRefGoogle Scholar
  31. Latour, B. (1987). Science in action. Cambridge: Harvard University Press.Google Scholar
  32. Latour, B. (1996). Aramis or the love of technology. Cambridge: Harvard University Press.Google Scholar
  33. Law, J., & Callon, M. (1992). The life and death of an aircraft: A network analysis of technical change. In W. Bijker & J. Law (Eds.), Shaping technology/building society: Studies in sociotechnical change (pp. 21–52). Cambridge: The MIT Press.Google Scholar
  34. Lazer, D., & Friedman, A. (2007). The network structure of exploration and exploitation. Administrative Science Quarterly, 52, 667–694.CrossRefGoogle Scholar
  35. Levinthal, D. A. (1997). Adaptation on rugged landscapes. Management Science, 43(7), 934–950.zbMATHCrossRefGoogle Scholar
  36. Levinthal, D. A., & Warglien, M. (1999). Landscape design: Designing for local action in complex worlds. Organization Science, 10(3), 342–357.CrossRefGoogle Scholar
  37. Lotka, A. J. (1926). The frequency distribution of scientific productivity. Journal of the Washington Academy of Sciences, 16, 317–323.Google Scholar
  38. March, J. G. (1991). Exploration and exploitation in organisational learning. Organization Science, 2(1), 71–87.MathSciNetCrossRefGoogle Scholar
  39. March, J. G., & Simon, H. A. (1958). Organizations. New York: Wiley.Google Scholar
  40. McKelvey, B. (1999). Avoiding complexity catastrophe in coevolutionary pockets: Strategies for rugged landscapes. Organization Science, 10(3), 294–321.CrossRefGoogle Scholar
  41. McPherson, M., Smith-Lovin, L., & Cook, J. M. (2001). Birds of a feather: Homophily in social networks. Annual Review of Sociology, 27, 415–444.CrossRefGoogle Scholar
  42. Merton, R. K. (1968). The Matthew effect in science. Science, 159(3810), 56–63.CrossRefGoogle Scholar
  43. Merton, R. K. (1988). The Matthew effect in science, II. Cumulative advantage and the symbolism of intellectual property. ISIS, 79, 606–623.CrossRefGoogle Scholar
  44. Mitchell, M. (1996). An introduction to genetic algorithms. Cambridge: MIT Press.Google Scholar
  45. Mulkay, M. J., Gilbert, G. N., & Woolgar, S. (1975). Problem areas and research networks in science. Sociology, 9, 187–203.CrossRefGoogle Scholar
  46. Newman, M. E. J. (2001a). Scientific collaboration networks. I. Network construction and fundamental results. Physical Review E, 64, 016131.CrossRefGoogle Scholar
  47. Newman, M. E. J. (2001b). Scientific collaboration networks. II. Shortest paths, weighted networks, and centrality. Physical Review E, 64, 016132.CrossRefGoogle Scholar
  48. Newman, M. E. J. (2001c). The structure of scientific collaboration networks. Proceedings of the National Academy of Science USA, 98(2), 404–409.zbMATHCrossRefGoogle Scholar
  49. Newman, M. E. J. (2003). The structure and function of complex networks. SIAM Review, 45, 167–256.MathSciNetzbMATHCrossRefGoogle Scholar
  50. Price, D. J. de S. (1963). Little science, big science. New York: Columbia University Press.Google Scholar
  51. Price, D. J. de S. (1965). Networks of scientific papers. Science, 149(3683), 510–515.Google Scholar
  52. Price, D. J. de S. (1976). A general theory of bibliometric and other cumulative advantage processes. Journal of the American Society for Information Science, 27, 292–306.Google Scholar
  53. Redner, S. (1998). How popular is your paper? An empirical study of the citation distribution. The European Physical Journal B, 4, 131–134.CrossRefGoogle Scholar
  54. Sandstrom, P. E. (1999). Scholars as subsistence foragers. Bulletin of the American Society for Information Science, 25(3), 17–20.Google Scholar
  55. Scharnhorst, A. (1998). Citation–networks, science landscapes and evolutionary strategies. Scientometrics, 43(1), 95–106.MathSciNetCrossRefGoogle Scholar
  56. Scharnhorst, A. (2002). Evolution in adaptive landscapes - examples of science and technology development. Discussion Paper FS II 00–302. Berlin: Wissenschaftszentrum Berlin für Sozialforschung.Google Scholar
  57. Scharnhorst, A., & Ebeling, W. (2005). Evolutionary search agents in complex landscapes. A new model for the role of competence and meta-competence (EVOLINO and other simulation tools). arXiv:0511232. Retrieved April 16, 2010 from
  58. Schubert, A., & Glaenzel, W. (1984). A dynamic look at a class of skew distributions. A model with scientometric applications. Scientometrics, 6(3), 149–167.CrossRefGoogle Scholar
  59. Simon, H. A. (1955). On a class of skew distribution functions. Biometrika, 42(3/4), 425–440.MathSciNetzbMATHCrossRefGoogle Scholar
  60. Steels, L. (2001). The methodology of the artificial. Commentary on Webb, B. (2001) Can robots make good models of biological behaviour? Behavioral and Brain Sciences, 24(6), 1071–1072. Retrieved May 5 2008 from
  61. Watts, D. J. (2004). The ‘New’ science of networks. Annual Review of Sociology, 30, 243–270.CrossRefGoogle Scholar
  62. Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of ‘small-world’ networks. Nature, 393, 440–442.CrossRefGoogle Scholar
  63. Weisberg, M., & Muldoon, R. (2009). Epistemic landscapes and the division of cognitive labor. Philosophy of Science, 76(2), 225–252.CrossRefGoogle Scholar
  64. Whitley, R. (2000). The intellectual and social organization of the sciences. Oxford: Oxford University Press.Google Scholar
  65. Wolpert, D. H., & Macready, W. G. (1997). No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation, 1(1), 67–82.CrossRefGoogle Scholar
  66. Zuckerman, H. (1977). Scientific Elite: Nobel Laureates in the United States. New York: Free Press.Google Scholar

Copyright information

© Akadémiai Kiadó, Budapest, Hungary 2011

Authors and Affiliations

  1. 1.Department of Sociology, Centre for Research in Social SimulationUniversity of SurreyGuildfordUK

Personalised recommendations