This page provides an overview of research at the intersection of the decision theory of Newcomb-like problems (including anthropics, naturalized induction, and other related topics) and artificial intelligence. Specifically, it lists papers, blog posts, etc. discussing the “implementation problem”: how do we implement decision theories in AIs? Conversely, what decision theories do different AI approaches implement?

• Albert and Heiner (2001): An Indirect-Evolution Approach to Newcomb’s Problem
• Meyer, Feldmaier and Shen (2016): Reinforcement Learning in Conflicting Environments for Autonomous Vehicles
• Caspar Oesterheld (2018): Doing what has worked well in the past leads to evidential decision theory
• Caspar Oesterheld (2018): Futarchy implements evidential decision theory
• Caspar Oesterheld (2018): The law of effect, randomization and Newcomb’s problem
• Caspar Oesterheld (2018): Goertzel’s GOLEM implements evidential decision theory applied to policy choice
• Caspar Oesterheld (2019): Approval-directed agency and the decision theory of Newcomb-like problems
• James Bell, Linda Linsefors, Caspar Oesterheld, Joar Skalse (2021): Reinforcement Learning in Newcomblike Environments