, Volume 183, Supplement 1, pp 87–109 | Cite as

Learning with neighbours

Emergence of convention in a society of learning agents
  • Roland MühlenberndEmail author


I present a game-theoretical multi-agent system to simulate the evolutionary process responsible for the pragmatic phenomenon division of pragmatic labour (DOPL), a linguistic convention emerging from evolutionary forces. Each agent is positioned on a toroid lattice and communicates via signaling games, where the choice of an interlocutor depends on the Manhattan distance between them. In this framework I compare two learning dynamics: reinforcement learning (RL) and belief learning (BL). An agent’s experiences from previous plays influence his communication behaviour, and RL agents act in a non-rational way whereas BL agents display a small degree of rationality by using best response dynamics. The complete system simulates an evolutionary process of communication strategies, which agents can learn in a structured spatial society. The significant questions are: what circumstances could lead to an evolutionary process that doesn’t result in the expected DOPL convention; and to what extent is interlocutor rationality necessary for the emergence of a society-wide convention à la DOPL?


Multi-agent system Division of pragmatic labour Signaling games Learning dynamics Communication strategies Simulation of an evolutionary process 



Division of pragmatic labour


Belief learning


Nash equilibrium


Replicator dynamics


Reinforcement learning


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Barrett J., Zollman K.J.S. (2009) The role of forgetting in the evolution of learning and language. Journal of Experimental and Theoretical Artificial Intelligence 21(4): 293–309CrossRefGoogle Scholar
  2. Benz, A., Jäger, G., & van Rooy, R. (2005). An introduction to game theory for linguists. In Game theory and pragmatics (pp. 1–82).Google Scholar
  3. Blutner, R., & Zeevat, H. (to appear). Optimality-theoretic pragmatics. In Semantics: An international handbook of natural language meaning.Google Scholar
  4. Brown G.W. (1951) Iterative solutions of games by fictitious play. In: Koopmans T.C (eds) Activity analysis of production and allocation. Wiley, New YorkGoogle Scholar
  5. Foster D., Young H. P. (1990) Stochastic evolutionary game dynamics. Theoretical Population Biology 38: 219–232CrossRefGoogle Scholar
  6. Franke, M., & Jäger, G. (to appear). Bidirectional optimization from reasoning and learning in games. Journal of Logic, Language and Information.Google Scholar
  7. Horn L. (1984) Towards a new taxonomy of pragmatic inference: Q-based and R-based implicature form, and use in context: Linguistic applications. Georgetown University Press, Washington, pp 11–42Google Scholar
  8. Huttegger S. M., Zollman K. J. S. (2011) Signaling games: Dynamics of evolution and learning language, games, and evolution. Springer, BerlinGoogle Scholar
  9. Jäger G. (2008) Evolutionary stability conditions for signaling games with costly signals. Journal of Theoretical Biology 253(1): 131–141CrossRefGoogle Scholar
  10. Jäger, G., & Ebert, C. (2009). Pragmatic rationalizibility. In Proceedings of Sinn and Bedeutung 13, SinSpeC. Working papers of the SFB 732 (Vol. 5, pp. 1–15).Google Scholar
  11. Levinson S. C. (2000) Presumptive meanings: The theory of generalized conversational implicature. MIT Press, Cambridge, MAGoogle Scholar
  12. Lewis D. (1969) Convention. Harvard University Press, CambridgeGoogle Scholar
  13. Parikh P. (1991) Communication and strategic inference. Linguistics and Philosophy 14: 473–513CrossRefGoogle Scholar
  14. Roth A. E., Erev I. (1995) Learning in extensive form games: Experimental data and simple dynamical models in the intermediate term. Games and Economic Behavior 8(1): 164–212CrossRefGoogle Scholar
  15. Schaden, G. (2008). Say hello to markedness. In Proceedings of DEAL II, LiP 28 (pp. 73–97).Google Scholar
  16. Skyrms B. (2009) Evolution of signalling systems with multiple senders and receivers. Philosophical Transactions of the Royal Society of London B 364: 771–779CrossRefGoogle Scholar
  17. Skyrms B. (2010) Signals: Evolution learning and information. Oxford University Press, OxfordGoogle Scholar
  18. Taylor P. D., Jonker L. (1978) Evolutionarily stable strategies and game dynamics. Mathematical Biosciences 40: 145–156CrossRefGoogle Scholar
  19. Tesar B., Smolensky P. (1998) Learnability in optimality theory. Linguistic Inquiry 29: 229–268CrossRefGoogle Scholar
  20. van Rooij R. (2008) Evolutionary motivations for semantic universals. In: Eckardt R., Jäger G., Veenstra T (eds) Variation, selection, development—probing the evolutionary model of language change. Mouton de Gruyter, Berlin, pp 103–142Google Scholar
  21. van Rooy R. (2004a) Signaling games select Horn strategies. Linguistics and Philosophy 27(4): 493–527CrossRefGoogle Scholar
  22. van Rooy R. (2004b) Evolution of conventional meaning and conversational principles. Synthese 139(2): 331–366CrossRefGoogle Scholar
  23. Wagner E. (2009) Communication and structured correlation. Erkenntnis 71: 377–393CrossRefGoogle Scholar
  24. Zollman K. J. S. (2005) Talking to neighbors: The evolution of regional meaning. Philosophy of Science 72: 69–85CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  1. 1.University of TübingenTübingenGermany

Personalised recommendations