Abstract
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?
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Abbreviations
- DOPL:
-
Division of pragmatic labour
- BL:
-
Belief learning
- NE:
-
Nash equilibrium
- RD:
-
Replicator dynamics
- RL:
-
Reinforcement learning
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Mühlenbernd, R. Learning with neighbours. Synthese 183 (Suppl 1), 87–109 (2011). https://doi.org/10.1007/s11229-011-9980-y
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DOI: https://doi.org/10.1007/s11229-011-9980-y