Studia Logica

, Volume 89, Issue 2, pp 237–256

Emergence of Information Transfer by Inductive Learning



We study a simple game theoretic model of information transfer which we consider to be a baseline model for capturing strategic aspects of epistemological questions. In particular, we focus on the question whether simple learning rules lead to an efficient transfer of information. We find that reinforcement learning, which is based exclusively on payoff experiences, is inadequate to generate efficient networks of information transfer. Fictitious play, the game theoretic counterpart to Carnapian inductive logic and a more sophisticated kind of learning, suffices to produce efficiency in information transfer.


Information game theory inductive logic reinforcement learning fictitious play 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Argiento, R., R. Pemantle, B. Skyrms, and S. Volkov, ‘Learning to Signal: Analysis of a Micro Level Reinforcement Model’, Stochastic Processes and their Applications (forthcoming).Google Scholar
  2. 2.
    Bala, V., and S. Goyal, ‘A Noncooperative Model of Network Formation’, Econometrica 68 (2000), 1181–1129.Google Scholar
  3. 3.
    Beggs, A. W., ‘On the Convergence of Reinforcement Learning’, Journal of Economic Theory 122 (2005), 1–36.Google Scholar
  4. 4.
    Benaïm, M., ‘Dynamics of Stochastic Approximation Algorithms’, in Le Seminaire de Probabilites, Lecture Notes in Mathemtics, vol. 1709, Springer-Verlag, New York, 1999, pp. 1–68.Google Scholar
  5. 5.
    Camerer, C., and T. Ho, ‘Experience-weighted attraction learning in normal form games’, Econometrica 67 (1999), 827–874.Google Scholar
  6. 6.
    Carnap R.: Logical Foundations of Probability. Chicago University Press, Chicago (1950)Google Scholar
  7. 7.
    Carnap R.: The Continuum of Inductive Methods. Chicago University Press, Chicago (1952)Google Scholar
  8. 8.
    Carnap, R., ‘A Basic System of Inductive Logic’, in Rudolf Carnap, and Richard C. Jeffrey (eds.), Studies in Inductive Logic and Probability I, University of California Press, Los Angeles, 1971, pp. 33–31.Google Scholar
  9. 9.
    Cesa-Bianchi N., Lugosi G.: Prediction, Learning, and Games. Cambridge University Press, Cambridge (2006)Google Scholar
  10. 10.
    De Finetti, B., ‘Foresight: Its Logical Laws, its Subjective Sources’, in Henry E. Kyburg, and Howard E. Smokler (eds.), Studies in Subjective Probability, John Wiley and Sons, New York, 1964, pp. 93–158.Google Scholar
  11. 11.
    Durrett R.: Theory and Examples. Duxbury Press, Belmont, CA (1996)Google Scholar
  12. 12.
    Erev, I., and A. E. Roth, ‘Predicting How People Play Games: Reinforcement Learning in Experimental Games with Unique, Mixed Strategy Equilibria’, American Economic Review 88 (1998), 848–880.Google Scholar
  13. 13.
    Fudenberg D., Levine D.K.: The Theory of Learning in Games. MIT Press, Cambridge, Mass (1998)Google Scholar
  14. 14.
    Goldman A.: Knowledge in a Social World. Oxford University Press, Oxford (1999)Google Scholar
  15. 15.
    Hofbauer J., Sigmund K.: Evolutionary Games and Population Dynamics. Cambridge University Press, Cambridge (1998)Google Scholar
  16. 16.
    Hopkins, E., ‘Two Competing Models of How People Learn in Games’, Econometrica 70 (2002), 2141–2166.Google Scholar
  17. 17.
    Hopkins, E., and M. Posch, ‘Attainability of Boundary Points under Reinforcement Learning’, Games and Economic Behavior 53 (2005), 110–125.Google Scholar
  18. 18.
    Kitcher, P., ‘The Division of Cognitive Labor’, Journal of Philosophy, June (1990), 5–22.Google Scholar
  19. 19.
    Laslier, J.-F., R. Topol, and B. Walliser, ‘A Behavioral Learning Process in Games’, Games and Economic Behavior 37 (2001), 340–366.Google Scholar
  20. 20.
    Leslie, D. S., and E. J. Collins, ‘Convergent Multiple-Times-Scales Reinforcement Learning Algorithms in Normal Form Games’, The Annals of Applied Probability 13 (2003), 1231–1251.Google Scholar
  21. 21.
    Leslie, D. S., and E. J. Collins, ‘Generalized Weakened Fictitious Play’, Games and Economic Behavior 56 (2006), 285–298.Google Scholar
  22. 22.
    Malinowski B.: Argonauts of the Western Pacific: An Account of Native Enterprise and Adventures in the Archipelagoes of Melanesian New Guinea. Routledge and Kegan Paul, London (1922)Google Scholar
  23. 23.
    McKinnon, S., From a Shattered Sun. Hierarchy, Gender, and Alliance in the Tanimbar Islands, The University of Wisconsin Press, Madison, 1991.Google Scholar
  24. 24.
    Monderer, D., and A. Sela, ‘A 2 X 2 game without the fictitious play property’, Games and Economic Behavior 14 (1994), 144–148.Google Scholar
  25. 25.
    Pemantle, R., and B. Skyrms, ‘Reinforcment Schemes may take a long Time to Exhibit Limiting Behavior’, Preprint (2001).Google Scholar
  26. 26.
    Pemantle, R., and S. Volkov, ‘Vertex Reinforced Random Walk on Z has Finite Range’, Annals of Probability 48 (2004), 1368–1388.Google Scholar
  27. 27.
    Posch, M., A. Pichler, and K. Sigmund, ‘The Efficiency of Adapting Aspiration Levels’, Proceedings of the Royal Society London B266 (1999), 1427–1436.Google Scholar
  28. 28.
    Roth, A., and I. Erev, ‘Learning in Extensive Form Games: Experimental Data and Simple Dynamic Models in the Intermediate Term’, Games and Economic Behavior 8 (1995), 164–212.Google Scholar
  29. 29.
    Rustichini, A., ‘Optimal Properties of Stimulus Response Learning Models’, Games and Economic Behavior29 (1999), 244–273.Google Scholar
  30. 30.
    Skyrms, B., ‘Carnapian Inductive Logic for Markov Chains’, Erkenntnis 35 (1991), 439–460.Google Scholar
  31. 31.
    Skyrms, B., and R. Pemantle, ‘A dynamic model of social network formation’, Proceedings of the National Academy of Sciences 97 (2000), 16, 9340–9346.Google Scholar
  32. 32.
    Van der Genugten, B., ‘A Weakened Form of Fictitious Play in Two-Person Zero- Sum Games’, International Game Theory Review 2 (2000), 307–328.Google Scholar
  33. 33.
    Young H.P.: Strategic Learning and its Limits. Oxford University Press, Oxford (2004)Google Scholar
  34. 34.
    Zabell S.: Symmetry and Its Discontents. Cambridge University Press, Cambridge (2006)Google Scholar
  35. 35.
    Zollman, K. J. S., ‘The Epistemic Benefit of Transient Diversity’, Philosophy of Science (forthcoming).Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2008

Authors and Affiliations

  1. 1.Konrad Lorenz Institute for Evolution and Cognition ResearchAltenbergAustria
  2. 2.Department of Logic and Philosophy of ScienceUniversity of California at IrvineIrvineUSA

Personalised recommendations