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Learning to signal in a dynamic world

Alexander, J McKenzie (2014) Learning to signal in a dynamic world. British Journal for the Philosophy of Science, 65 (4). 797 – 820. ISSN 0007-0882

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Identification Number: 10.1093/bjps/axt044


Sender–receiver games, first introduced by David Lewis ( [1969] ), 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. ( [2012] ) showed that reinforcement learning, combined with forgetting, frequently produced both efficient and minimal signalling systems. In this article, 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)
Date Deposited: 09 Aug 2012 07:25
Last Modified: 20 Oct 2021 01:58

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