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Sifting the Signal from the Noise

Herrmann, Daniel A. and VanDrunen, Jacob (2022) Sifting the Signal from the Noise. British Journal for the Philosophy of Science. ISSN 1464-3537


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Signalling games are useful for understanding how language emerges. In the standard models the dynamics in some sense already knows what the signals are, even if they do not yet have meaning. In this paper we relax this assumption, and develop a simple model we call an `attention game' in which agents have to learn which feature in their environment is the signal. We demonstrate that simple reinforcement learning agents can still learn to coordinate in contexts in which (i) the agents do not already know what the signal is and (ii) the other features in the agents' environment are uncorrelated with the signal. Furthermore, we show that, in cases in which other features are correlated with the signal, there is a surprising trade-off between learning what the signal is, and success in action. We show that the mutual information between a signal and a feature plays a key role in governing the accuracy and attention of the agent.

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Item Type: Published Article or Volume
Herrmann, Daniel A.daherrma@uci.edu0000-0001-5322-1921
Keywords: signaling games game theory self-assembling games information theory
Subjects: General Issues > Game Theory
Depositing User: Mr. Daniel A. Herrmann
Date Deposited: 15 May 2022 03:49
Last Modified: 15 May 2022 03:49
Item ID: 20617
Journal or Publication Title: British Journal for the Philosophy of Science
Official URL:
DOI or Unique Handle:
Subjects: General Issues > Game Theory
Date: 11 May 2022
ISSN: 1464-3537

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