Learning with neighbours

Synthese 183 (S1):87-109 (2011)
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?
Keywords Multi-agent system  Division of pragmatic labour  Signaling games  Learning dynamics  Communication strategies  Simulation of an evolutionary process
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DOI 10.1007/s11229-011-9980-y
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Michael Franke (2013). Game Theoretic Pragmatics. Philosophy Compass 8 (3):269-284.
Michael Franke (2016). The Evolution of Compositionality in Signaling Games. Journal of Logic, Language and Information 25 (3-4):355-377.

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