# Self-Sustaining Iterated Learning

arXiv: https://arxiv.org/abs/1609.03960 · HTML: https://mlsanity.com/paper/1609.03960/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Sep 2016.

## Built on by (most cited first, 1 of 1)

- [Quantifying Human Priors over Social and Navigation Networks](https://mlsanity.com/paper/2402.18651/) (2024)

## Abstract (arXiv)

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.