_This summary is more editorialized than most._ This post critiques Functional Decision Theory (FDT). I’m not going to go into detail, but I think the arguments basically fall into two camps. First, there are situations in which there is no uncertainty about the consequences of actions, and yet FDT chooses actions that do not have the highest utility, because of their impact on counterfactual worlds which “could have happened” (but ultimately, the agent is just leaving utility on the table). Second, FDT relies on the ability to tell when someone is “running an algorithm that is similar to you”, or is “logically correlated with you”. But there’s no such crisp concept, and this leads to all sorts of problems with FDT as a decision theory.
Planned opinion:
Like Buck from MIRI , I feel like I understand these objections and disagree with them. On the first argument, I agree with Abram that a decision should be evaluated based on how well the agent performs with respect to the probability distribution used to define the problem; FDT only performs badly if you evaluate on a decision problem produced by conditioning on a highly improbable event. On the second class or arguments, I certainly agree that there isn’t (yet) a crisp concept for “logical similarity”; however, I would be shocked if the _intuitive concept_ of logical similarity was not relevant in the general way that FDT suggests. If your goal is to hardcode FDT into an AI agent, or your goal is to write down a decision theory that in principle (e.g. with infinite computation) defines the correct action, then it’s certainly a problem that we have no crisp definition yet. However, FDT can still be useful for getting more clarity on how one ought to reason, without providing a full definition.
Planned summary for the Alignment Newsletter:
Planned opinion: