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DOI: 10.4230/LIPIcs.ITCS.2017.17
URN: urn:nbn:de:0030-drops-81711
URL: https://drops.dagstuhl.de/opus/volltexte/2017/8171/
Chazelle, Bernard ; Wang, Chu
Self-Sustaining Iterated Learning
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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 | |
| Seminar: | 8th Innovations in Theoretical Computer Science Conference (ITCS 2017) | |
| Issue date: | 2017 | |
| Date of publication: | 28.11.2017 |
