Learning with neighbours

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?

[1]  Regine Eckardt,et al.  Variation, Selection, Development: Probing the Evolutionary Model of Language Change , 2008 .

[2]  Jeffrey Barrett,et al.  The role of forgetting in the evolution and learning of language , 2009, J. Exp. Theor. Artif. Intell..

[3]  Kevin J. S. Zollman Talking to Neighbors: The Evolution of Regional Meaning* , 2005, Philosophy of Science.

[4]  Robert van Rooy,et al.  Evolution of Conventional Meaning and Conversational Principles , 2004, Synthese.

[5]  Gerhard Jäger,et al.  Evolutionary stability conditions for signaling games with costly signals. , 2008, Journal of theoretical biology.

[6]  R.A.M. van Rooij Evolution of conventional meaning and conversational principles , 2004 .

[7]  Brian Skyrms,et al.  Evolution of signalling systems with multiple senders and receivers , 2008, Philosophical Transactions of the Royal Society B: Biological Sciences.

[8]  H. Grice Logic and conversation , 1975 .

[9]  S. Levinson Presumptive Meanings: The theory of generalized conversational implicature , 2001 .

[10]  Prashant Parikh,et al.  Communication and strategic inference , 1991 .

[11]  A. Roth,et al.  Learning in Extensive-Form Games: Experimental Data and Simple Dynamic Models in the Intermediate Term* , 1995 .

[12]  Reinhard Blutner,et al.  Some Aspects of Optimality in Natural Language Interpretation , 2000, J. Semant..

[13]  Gerhard Jäger,et al.  An Introduction to Game Theory for Linguists , 2006 .

[14]  Kevin J. S. Zollman,et al.  Signaling Games - Dynamics of Evolution and Learning , 2011, Language, Games, and Evolution.

[15]  Henk Zeevat,et al.  Optimality-theoretic pragmatics , 2009 .

[16]  Elliott O. Wagner,et al.  Communication and Structured Correlation , 2009 .

[17]  H. Peyton Young,et al.  Stochastic Evolutionary Game Dynamics , 1990 .

[18]  Simon M. Huttegger Signals: Evolution, Learning and InformationBy Brian Skyrms , 2011 .

[19]  Robert van Rooy Evolution of Conventional Meaning and Conversational Principles , 2004 .

[20]  Gerhard Schaden Say Hello to Markedness , 2007 .

[21]  Territoire Urbain,et al.  Convention , 1955, Hidden Nature.

[22]  P. Taylor,et al.  Evolutionarily Stable Strategies and Game Dynamics , 1978 .

[23]  O. H. Brownlee,et al.  ACTIVITY ANALYSIS OF PRODUCTION AND ALLOCATION , 1952 .

[24]  Robert van Rooij,et al.  Evolutionary motivations for semantic universals , 2008 .

[25]  Robert van Rooy,et al.  SIGNALLING GAMES SELECT HORN STRATEGIES , 2004 .

[26]  Michael Franke,et al.  Bidirectional Optimization from Reasoning and Learning in Games , 2012, J. Log. Lang. Inf..