License: Creative Commons Attribution 3.0 Unported license (CC-BY 3.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.ITCS.2017.17
URN: urn:nbn:de:0030-drops-81711
URL: https://drops.dagstuhl.de/opus/volltexte/2017/8171/
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Chazelle, Bernard ; Wang, Chu

Self-Sustaining Iterated Learning

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LIPIcs-ITCS-2017-17.pdf (0.7 MB)


Abstract

An important result from psycholinguistics (Griffiths & Kalish, 2005) states that no language can be learned iteratively by rational agents in a self-sustaining manner. We show how to modify the learning process slightly in order to achieve self-sustainability. Our work is in two parts. First, we characterize iterated learnability in geometric terms and show how a slight, steady increase in the lengths of the training sessions ensures self-sustainability for any discrete language class. In the second part, we tackle the nondiscrete case and investigate self-sustainability for iterated linear regression. We discuss the implications of our findings to issues of non-equilibrium dynamics in natural algorithms.

BibTeX - Entry

@InProceedings{chazelle_et_al:LIPIcs:2017:8171,
  author =	{Bernard Chazelle and Chu Wang},
  title =	{{Self-Sustaining Iterated Learning}},
  booktitle =	{8th Innovations in Theoretical Computer Science Conference (ITCS 2017)},
  pages =	{17:1--17:17},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-029-3},
  ISSN =	{1868-8969},
  year =	{2017},
  volume =	{67},
  editor =	{Christos H. Papadimitriou},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{http://drops.dagstuhl.de/opus/volltexte/2017/8171},
  URN =		{urn:nbn:de:0030-drops-81711},
  doi =		{10.4230/LIPIcs.ITCS.2017.17},
  annote =	{Keywords: Iterated learning, language evolution, iterated Bayesian linear regression, non-equilibrium dynamics}
}

Keywords: Iterated learning, language evolution, iterated Bayesian linear regression, non-equilibrium dynamics
Collection: 8th Innovations in Theoretical Computer Science Conference (ITCS 2017)
Issue Date: 2017
Date of publication: 28.11.2017


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