Doing what has worked well in the past leads to evidential decision theory

Abstract

Because counterfactuals are untestable, decision theories may be viewed as untestable as well. However, that does not stop one from using a simple learning procedure, often called the law of effect, for a series of Newcomb-like problems: when faced with a Newcomb-like problem, do what has worked well — i.e. what has been succeeded by high rewards or utilities — in past problems of a similar structure. This short note shows that adopting such a learning procedure results in evidential decision theory (EDT). While the result is trivial to prove, it serves two purposes. First, it contributes to understanding EDT: people often perceive the characterisation of EDT as doing what has worked well in the past as surprising or counter-intuitive. Second, it contributes to our understanding of how simple decision-making policies such as those used in artificial intelligence behave in Newcomb-like problems, and how to implement specific decision theories within standard artificial intelligence frameworks.