I'm writing this for blog day at MSFP. I thought about a lot of things here like category theory, the 1-2-3 conjecture and Paul Christiano's agenda. I want to start by thanking everyone for having me and saying I had a really good time. At this point I intend to go back to thinking about the stuff I was thinking about before MSFP (random matrix theory). But I learned a lot and I'm sure some of it will come to be useful. This blog is about (my confusion of) decision theory.
Before the workshop I hadn't read much besides Eliezer's paper on FDT and my impression was that it was mostly a good way of thinking about making decisions and at least represented progress over EDT and CDT. After thinking more carefully about some canonical thought experiments I'm no longer sure. I suspect many of the concrete thoughts which follow will be wrong in ways that illustrate very bad intuitions. In particular I think I am implicitly guided by non-example number 5 of an aim of decision theory in Wei Dai's post on the purposes of decision theory. I welcome any corrections or insights in the comments.
The Problem of Decision Theory
First I'll talk about what I think decision theory is trying to solve. Basically I think decision theory is the theory of how one should decide on an action after one already understands: The actions available, the possible outcomes of actions, the probabilities of those outcomes and the desirability of those outcomes. In particular the answers to the listed questions are only adjacent to decision theory. I sort of think answering all of those questions is in fact harder than the question posed by decision theory. Before doing any reading I would have naively expected that the problem of decision theory, as stated here, was trivial but after pulling on some edge cases I see there is room for a lot of creative and reasonable disagreement.
A lot of the actual work in decision theory is the construction of scenarios in which ideal behavior is debatable or unclear. People choose their own philosophical positions on what is rational in these hairy situations and then construct general procedures for making decisions which they believe behave rationally in a wide class of problems. These constructions are a concrete version of formulating properties one would expect an ideal decision theory to have.
One such property is that an ideal decision theory shouldn't choose to self modify in some wide vaguely defined class of "fair" problems. An obviously unfair problem would be one in which the overseer gives CDT $10 and any other agent $0. One of my biggest open questions in decision theory is where this line between fair and unfair problems should lie. At this point I am not convinced any problem where agents in the environment have access to our decision theory's source code or copies of our agent are fair problems. But my impression from hearing and reading what people talk about is that this is a heretical position.
Let's discuss Newcomb's problem in detail. In this problem there are two boxes one of which you know contains a dollar. In the other box an entity predicting your action may or may not put a million dollars. They put a million dollars if and only if they predict you will only take one box. What do you do if the predictor is 99 percent accurate? How about if it is perfectly accurate? What if you can see the content of the boxes before you make your decision?
An aside on why Newcomb's problem seems important: It is sort of like a prisoner's dilemma. To see the analogy imagine you're playing a classical prisoner's dilemma against a player who can reliably predict your action and then chooses to match it. Newcomb's problem seems important because prisoner's dilemmas seem like simplifications of situations which really do occur in real life. The tragedy of prisoner dilemmas is that game theory suggests you should defect but the real world seems like it would be better if people cooperated.
Newcomb's problem is weird to think about because the predictor and agent's behaviors are logically connected but not causally. That is, if you tell me what the agent does or what the predictor predicts as an outside observer I can guess what the other does with high probability. But once the predictor predicts the agent could still take either option and flip flopping won't flip flop the predictor. Still one may argue you should one box because being a one boxer going into the problem means you will likely get more utility. I disagree with this view and see Newcomb's problem as punishing rational agents.
If Newcomb's problem is ubiquitous and one imagines an agent walking down the street constantly being Newcombed it is indeed unfortunate if they are doomed to two box. They'll end up with far fewer dollars. But this thought experiment is missing an important part of real world detail in my view. How the predictors predict the agents behavior. There are three possibilities:
- The predictors have a sophisticated understanding of the agent's inner workings and use it to simulate the agent to high fidelity.
- The predictors have seen many agents like our agent doing problems like this problem and use this to compute a probability of our agent's choice and compare it to a decision threshold.
- The predictor has been following the behavior of our agent and uses this history to assign its future behavior a probability.
In the third bullet the agent should one box if they predict they are likely to be Newcombed often. In the second bullet they should one box if they predict that members of their population will be Newcombed often and they derive more utility from the extra dollars their population will get than the extra dollar they could get for themselves. I have already stated I see the first bullet as an unfair problem.
Mind Reading isn't Cool
My big complaint with mind reading is that there just isn't any mind reading. All my understandings of how people behave comes from observing how they behave in general, how the human I'm trying to understand behaves specifically, whatever they have explicitly told me about their intentions and whatever self knowledge I have I believe is applicable to all humans. Nowhere in the current world do people have to make decisions under the condition of being accurately simulated.
Why then do people develop so much decision theory intended to be robust in the presence of external simulators? I suppose its because there's an expectation that this will be a major problem in the future which should be solved philosophically before it is practically important. Mind reading could become important to humans if mind surveillance because possible and deployed. I don't think such a thing is possible in the near term or likely even in the fullness of time. But I also can't think of any insurmountable physical obstructions so maybe I'm too optimistic.
Mind reading is relevant to AI safety because whatever AGI is created will likely be a program on a computer somewhere which could reason its program stack is fully transparent or its creators are holding copies of it for predictions.
Having written that last paragraph I suddenly understand why decision theory in the AI community is the way it is. I guess I wasn't properly engaging with the premises of the thought experiment. If one actually did tell me I was about to do a Newcomb experiment I would still two box because knowing I was in the real world I wouldn't really believe that an accurate predictor would be deployed against me. But an AI can be practically simulated and what's more can reason that it is just a program run by a creator that could have created many copies of it.
I'm going to post this anyway since it's blog-day and not important-quality-writing day but I'm not sure this blog has much of a purpose anymore.
This may read like I'm already explicitly guided by the false purpose Wei Dai warned against. My understanding is that the goal is to understand ideal decision making. Just not for the purposes of implementation. ↩︎
I don't really know anything but I imagine the game theory of reputation is well developed ↩︎