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Author: Hal Ashton

Affiliation: Computer Science, University College London, U.K.

Keyword(s): Reinforcement Learning, Goodhart’s Law, Campbell’s Law, Causal Inference, Cognitive Error.

Abstract: Campbell-Goodhart’s law relates to the causal inference error whereby decision-making agents aim to influence variables which are correlated to their goal objective but do not reliably cause it. This is a well known error in Economics and Political Science but not widely labelled in Artificial Intelligence research. Through a simple example, we show how off-the-shelf deep Reinforcement Learning (RL) algorithms are not necessarily immune to this cognitive error. The off-policy learning method is tricked, whilst the on-policy method is not. The practical implication is that naive application of RL to complex real life problems can result in the same types of policy errors that humans make. Great care should be taken around understanding the causal model that underpins a solution derived from Reinforcement Learning.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Ashton, H. (2021). Causal Campbell-Goodhart’s Law and Reinforcement Learning. In Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART; ISBN 978-989-758-484-8; ISSN 2184-433X, SciTePress, pages 67-73. DOI: 10.5220/0010197300670073

@conference{icaart21,
author={Hal Ashton.},
title={Causal Campbell-Goodhart’s Law and Reinforcement Learning},
booktitle={Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART},
year={2021},
pages={67-73},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010197300670073},
isbn={978-989-758-484-8},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART
TI - Causal Campbell-Goodhart’s Law and Reinforcement Learning
SN - 978-989-758-484-8
IS - 2184-433X
AU - Ashton, H.
PY - 2021
SP - 67
EP - 73
DO - 10.5220/0010197300670073
PB - SciTePress

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