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Learning to signal in a dynamic world

Alexander, J McKenzie (2012) Learning to signal in a dynamic world. British Journal for the Philosophy of Science. ISSN 0007-0882 (Submitted)

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Sender-receiver games, first introduced by David Lewis in Convention, have received increased attention in recent years as a formal model for the emergence of communication. Skyrms (2010) showed that simple models of reinforcement learning often succeed in forming efficient, albeit not necessarily minimal, signalling systems for a large family of games. Later, Alexander et al. (2011) showed that reinforcement learning, combined with forgetting, frequently produced both efficient and minimal signalling systems. In this paper I define a dynamic sender-receiver game in which the state-action pairs are not held constant over time, and show that neither of these two models of learning learn to signal in this environment. However, a model of reinforcement learning with discounting of the past does learn to signal; it also gives rise to the phenomenon of linguistic drift.

Item Type: Article
Official URL:
Additional Information: © The Author
Divisions: Philosophy, Logic and Scientific Method
Subjects: B Philosophy. Psychology. Religion > B Philosophy (General)
Q Science > Q Science (General)
Sets: Departments > Philosophy, Logic and Scientific Method
Date Deposited: 09 Aug 2012 07:25
Last Modified: 10 Jan 2020 00:09

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