Self-Sustaining Iterated Learning

Bernard Chazelle; Chu Wang

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arXiv · 1609.03960

Self-Sustaining Iterated Learning

Bernard Chazelle·Chu Wang

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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.

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math.OC cs.LG stat.ML

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BibTeX RIS

Bernard Chazelle, Chu Wang. 2016-09-13. Self-Sustaining Iterated Learning. https://arxiv.org/abs/1609.03960

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