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This uses logical inductors of distinctly different strengths. I wonder if there’s some kind of convexity result for logical inductors which can see each other? Suppose traders in \(\mathbb{P}_n\) have access to \(\mathbb{P}'_n\) and vice versa. Or perhaps just assume that the markets cannot be arbitrarily exploited by such traders. Then, are linear combinations also logical inductors?


by Vadim Kosoy 5 days ago | link

This is somewhat related to what I wrote about here. If you consider only what I call convex gamblers/traders and fix some weighting (“prior”) over the gamblers then there is a natural convex set of dominant forecasters (for each history, it is the set of minima of some convex function on \(\Delta\mathcal{O}^\omega\).)


The differences between this and UDT2:

  1. This is something we can define precisely, whereas UDT2 isn’t.
  2. Rather than being totally updateless, this is just mostly updateless, with the parameter \(f\) determining how updateless it is.

I don’t think there’s a problem this gets right which we’d expect UDT2 to get wrong.

If we’re using the version of logical induction where the belief jumps to 100% as soon as something gets proved, then a weighty trader who believes crossing the bridge is good will just get knocked out immediately if the theorem prover starts proving that crossing is bad (which helps that step inside the Löbian proof go through). (I’d be surprised if the analysis turns out much different for the kind of LI which merely rapidly comes to believe things which get proved, but I can see how that distinction might block the proof.) But certainly it would be good to check this more thoroughly.


Looking “at the very beginning” won’t work – the beliefs of the initial state of the logical inductor won’t be good enough to sensibly detect these things and decide what to do about them.

While ignoring the coin is OK as special-case reasoning, I don’t think everything falls nicely into the bucket of “information you want to ignore” vs “information you want to update on”. The more general concept which captures both is to ask “how do I want to react to thin information, in terms of my action?” – which is of course the idea of policy selection.


At present, I think the main problem of logical updatelessness is something like: how can we make a principled trade-off between thinking longer to make a better decision, vs thinking less long so that we exert more logical control on the environment?

For example, in Agent Simulates Predictor, an agent who thinks for a short amount of time and then decides on a policy for how to respond to any conclusions which it comes to after thinking longer can decide “If I think longer, and see a proof that the predictor thinks I two-box, I can invalidate that proof by one-boxing. Adopting this policy makes the predictor less likely to find such a proof.” (I’m speculating; I haven’t actually written up a thing which does this, yet, but I think it would work.) An agent who thinks longer before making a decision can’t see this possibility because it has already proved that the predictor predicts two-boxing, so from the perspective of having thought longer, there doesn’t appear to be a way to invalidate the prediction – being predicted to two-box is just a fact, not a thing the agent has control over.

Similarly, in Prisoner’s Dilemma, an agent who hasn’t thought too long can adopt a strategy of first thinking longer and then doing whatever it predicts the other agent to do. This is a pretty good strategy, because it makes it so that the other agent’s best strategy is to cooperate. However, you have to think for long enough to find this particular strategy, but short enough that the hypotheticals which show that the strategy is a good idea aren’t closed off yet.

So, I think there is less conflict between UDT and bounded reasoning than you are implying. However, it’s far from clear how to negotiate the trade-offs sanely.

(However, in both cases, you still want to spend as long a time thinking as you can afford; it’s just that you want to make the policy decision, about how to use the conclusions of that thinking, as early as they can be made while remaining sensible.)


I think the point I was making here was a bit less clear than I wanted it to be. I was saying that, if you use predictable exploration on actions rather than policies, then you only get to see what happens when you predictably take a certain action. This is good for learning pure equilibria in games, but doesn’t give information which would help the agent reach the right mixed equilibria when randomized actions should be preferred; and indeed, it doesn’t seem like such an agent would reach the right mixed equilibria.

I believe the “predictable exploration on policies” approach solves agent-simulates-predictor just fine, along with other problems (including counterfactual mugging) which require “some degree of updatelessness” without requiring the full reflective stability which we want from updatelessness.


Thinking about this more, I think there’s an important disanalogy between trying to make policy decisions with earlier market states vs smaller proof-searches.

In Agent Simulates Predictor, we can use an earlier market state to decide our policy, because the earlier market state can trust the predictor to make the right predictions, even if the predictor is using a more powerful logic (since logical inductors can learn to boundedly trust more powerful logics).

However, with proof-based DTs, no analogous move is possible.

Consider a version of Agent Simulates Predictor in which Omega searches for a proof that you one-box in PA+Con(PA); if one is found, Omega fills the $1m box. Otherwise, not. Omega has \(T_1\) time to think. The agent has \(T_2\) time to think, \(T_2 >> T_1\). The agent reasons in PA.

If the agent refused to use all its time, and only ran for \(T_0 << T_1\) time, but still had enough time to find interesting proofs, then it could reason as follows: “If I one-box, then there is a short proof that I one-box which Omega can find. So I get $1M.” It may not know if PA+Con(PA) is sound, but that doesn’t matter; the agent just has to ensure that there is a proof which Omega will find. It wouldn’t find any proofs leading to higher utility that this, so it would one-box and get $1M.

Unfortunately, I don’t see any way to harness the shorter proof-search to choose a policy which would get the $1M in this case but choose to think longer in other cases where that’s beneficial.

We might want the agent to reason: “If I stop and one-box right now, Omega will be able to prove that I one-box, and I’ll get $1M. If I wait longer, Omega won’t be able to prove what I do, so I’ll at most be able to get $100. So, I’ll stop now and one-box.” However, this reasoning would have to take place at a proof-length in which several things hold at once:

  • The agent can prove that it’s still “early” enough that its action would be provable to Omega if it acted now.
  • It’s “late” enough that the agent can see that Omega’s predictions are sound (IE, it can check that Omega doesn’t reach false results in the limited time it has). This allows the agent to see that it’ll never get money from both boxes.

It seems very unlikely that there is a proof length where these can both be true, due to bounded Löb.

For logical induction, on the other hand, there’s quite likely to be a window with analogous properties.


So I wound up with “predictable policy selection that forms links to stuff that would be useful to correlate with yourself, and cuts links to stuff that would be detrimental to have correlated with yourself”.


I’m reading this as “You want to make decisions as early as you can, because when you decide one of the things you can do is decide to put the decision off for later; but when you make a decision later, you can’t decide to put it earlier.”

And “logical time” here determines whether others can see your move when they decide to make theirs. You place yourself upstream of more things if you think less before deciding.

This runs directly into problem 1 of “how do you make sure you have good counterfactuals of what would happen if you had a certain pattern of logical links, if you aren’t acting unpredictably”, and maybe some other problems as well, but it feels philosophically appealing.

Here’s where I’m saying “just use the chicken rule again, in this stepped-back reasoning”. It likely re-introduces versions the same problems at the higher level, but perhaps iterating this process as many times as we can afford is in some sense the best we can do.


I agree, my intuition is that LLC asserts that the troll, and even CON(PA), is downstream. And, it seems to get into trouble because it treats it as downstream.

I also suspect that Troll Bridge will end up formally outside the realm where LLC can be justified by the desire to make ratifiability imply CDT=EDT. (I’m working on another post which will go into that more.)


I am much more likely to miss things posted to LessWrong2.0. I eagerly await this forum’s incorporation into LW. Until then, I’m also conflicted about where to post.


by Abram Demski 150 days ago | link | parent | on: Smoking Lesion Steelman

First of all, it seems to me that “updateless CDT” and “updateless EDT” are the same for agents with access to their own internal states immediately prior to the decision theory computation: on an appropriate causal graph such internal states would be the only nodes with arrows leading to the nodes “output of decision theory”, so if their value is known, then severing those arrows does not affect the computation for updating on an observation of the value of the “output of decision theory” node. So the counterfactual and conditional probability distributions are the same, and thus CDT and EDT are the same.

I don’t think “any appropriate causal graph” necessarily has the structure you suggest. (We don’t have a good idea for what causal graphs on logical uncertainty look like.) It’s plausible that your assertion is true, but not obvious.

(If the agent observes itself trying, it infers that it must have done so because it computed the probability as 99%, and thus the probability of success must be 99%.)

EDT isn’t nearly this bad. I think a lot of people have this idea that EDT goes around wagging tails of dogs to try to make the dogs happy. But, EDT doesn’t condition on the dog’s tail wagging: it conditions on personally wagging the dog’s tail, which has no a priori reason to be correlated with the dog’s happiness.

Similarly, EDT doesn’t just condition on “trying”: it conditions on everything it knows, including that it hasn’t yet performed the computation. The only equilibrium solution will be for the AI to run the computation every time except on exploration rounds. It sees that it does quite poorly on the exploration rounds where it tries without running the computation, so it never chooses to do that.







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