Regret Minimization in Decision-Making

The chapter explores the concept of minimizing regret in decision-making processes and contrasts it with the possibility of achieving low regret in certain scenarios. It discusses the setting of a single agent learning to make choices that maximize rewards in a myopic setting and introduces the concept of regret minimization as a criterion for performance evaluation.

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Imagine a world where there are many powerful AI systems, working at cross purposes. You could suppose that different governments use AIs to manage their militaries, or simply that many powerful AIs have their own wills. At any rate, it seems valuable for them to be able to cooperatively work together and minimize pointless conflict. How do we ensure that AIs behave this way - and what do we need to learn about how rational agents interact to make that more clear? In this episode, I'll be speaking with Caspar Oesterheld about some of his research on this very topic.

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Episode art by Hamish Doodles: hamishdoodles.com

Topics we discuss, and timestamps:

- 0:00:34 - Cooperative AI

- 0:06:21 - Cooperative AI vs standard game theory

- 0:19:45 - Do we need cooperative AI if we get alignment?

- 0:29:29 - Cooperative AI and agent foundations

- 0:34:59 - A Theory of Bounded Inductive Rationality

- 0:50:05 - Why it matters

- 0:53:55 - How the theory works

- 1:01:38 - Relationship to logical inductors