Self-Sustaining Iterated Learning
Optimization and Control
2016-09-14 v1 Machine Learning
Machine Learning
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.
Keywords
Cite
@article{arxiv.1609.03960,
title = {Self-Sustaining Iterated Learning},
author = {Bernard Chazelle and Chu Wang},
journal= {arXiv preprint arXiv:1609.03960},
year = {2016}
}