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Communicative Bottlenecks Lead to Maximal Information Transfer

LaCroix, Travis (2020) Communicative Bottlenecks Lead to Maximal Information Transfer. Journal of Experimental and Theoretical Artificial Intelligence.

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Abstract

This paper presents new analytic and numerical analysis of signalling games that give rise to informational bottlenecks—that is to say, signalling games with more state/act pairs than available signals to communicate information about the world. I show via simulation that agents learning to coordinate tend to favour partitions of nature which provide maximal information transfer. This is true despite the fact that nothing from an initial analysis of the stability properties of the underlying signalling game suggests that this should be the case. As a first pass to explain this, I note that the underlying structure of our model favours maximal information transfer in regard to the simple combinatorial properties of how the agents might partition nature into kinds. However, I suggest that this does not perfectly capture the empirical results; thus, several open questions remain.


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Item Type: Published Article or Volume
Creators:
CreatorsEmailORCID
LaCroix, Travistlacroix@uci.edu0000-0002-1724-3434
Keywords: signalling games, signals, signalling systems, information transfer, informational bottlenecks, reinforcement learning; emergent communication; sender-receiver games
Subjects: Specific Sciences > Biology > Evolutionary Theory
Specific Sciences > Complex Systems
Specific Sciences > Computation/Information
General Issues > Computer Simulation
Specific Sciences > Cognitive Science > Concepts and Representations
Specific Sciences > Cultural Evolution
General Issues > Game Theory
General Issues > Natural Kinds
Depositing User: Dr. Travis LaCroix
Date Deposited: 23 Jan 2020 01:44
Last Modified: 23 Jan 2020 01:44
Item ID: 16843
Journal or Publication Title: Journal of Experimental and Theoretical Artificial Intelligence
Official URL: http://dx.doi.org/10.1080/0952813X.2020.1716857
DOI or Unique Handle: https://doi.org/10.1080/0952813X.2020.1716857
Subjects: Specific Sciences > Biology > Evolutionary Theory
Specific Sciences > Complex Systems
Specific Sciences > Computation/Information
General Issues > Computer Simulation
Specific Sciences > Cognitive Science > Concepts and Representations
Specific Sciences > Cultural Evolution
General Issues > Game Theory
General Issues > Natural Kinds
Date: 2020
URI: https://philsci-archive-dev.library.pitt.edu/id/eprint/16843

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