The Asymptotic Cost of Complexity

Fuente: arXiv
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Auteur principal: Cripps, Martin W
Format: Preprint
Publié: 2024
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author Cripps, Martin W
author_facet Cripps, Martin W
contents We propose a measure of learning efficiency for non-finite state spaces. We characterize the complexity of a learning problem by the metric entropy of its state space. We then describe how learning efficiency is determined by this measure of complexity. This is, then, applied to two models where agents learn high-dimensional states.
format Preprint
id arxiv_https___arxiv_org_abs_2408_14949
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle The Asymptotic Cost of Complexity
Cripps, Martin W
Theoretical Economics
Information Theory
We propose a measure of learning efficiency for non-finite state spaces. We characterize the complexity of a learning problem by the metric entropy of its state space. We then describe how learning efficiency is determined by this measure of complexity. This is, then, applied to two models where agents learn high-dimensional states.
title The Asymptotic Cost of Complexity
topic Theoretical Economics
Information Theory
url https://arxiv.org/abs/2408.14949