Learning to Signal in a Dynamic World

British Journal for the Philosophy of Science 65 (4):797-820 (2014)
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Abstract

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. 1 Introduction2 Dynamic Signalling Games with Reinforcement Learning2.1 Introducing new states2.2 Swapping state–action pairs3 Discounting the Past3.1 Learning to signal in a dynamic world3.2 An unexpected outcome: linguistic drift4 ConclusionAppendix: A Markov Chain Analysis

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Jason Alexander
London School of Economics

References found in this work

Convention: A Philosophical Study.David Kellogg Lewis - 1969 - Cambridge, MA, USA: Wiley-Blackwell.
Evolution of the Social Contract.Brian Skyrms - 1997 - Philosophy 72 (282):604-606.
Evolution of the Social Contract.Brian Skyrms - 1999 - Philosophical and Phenomenological Research 59 (1):229-236.
Signals.Brian Skyrms - 2008 - Philosophy of Science 75 (5):489-500.

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