I feel like MIRI perhaps mispositioned FDT (their variant of UDT) as a clear advancement in decision theory, whereas maybe they could have attracted more attention/interest from academic philosophy if the framing was instead that the UDT line of thinking shows that decision theory is just more deeply puzzling than anyone had previously realized. Instead of one major open problem (Newcomb's, or EDT vs CDT) now we have a whole bunch more. I'm really not sure at this point whether UDT is even on the right track, but it does seem clear that there are some thorny issues in decision theory that not many people were previously thinking about:

  1. Indexical values are not reflectively consistent. UDT "solves" this problem by implicitly assuming (via the type signature of its utility function) that the agent doesn't have indexical values. But humans seemingly do have indexical values, so what to do about that?
  2. The commitment races problem extends into logical time, and it's not clear how to make the most obvious idea of logical updatelessness work.
  3. UDT says that what we normally think of as different approaches to anthropic reasoning are really different preferences, which seems to sidestep the problem. But is that actually right, and if so where are these preferences supposed to come from?
  4. 2TDT-1CDT - If there's a population of mostly TDT/UDT agents and few CDT agents (and nobody knows who the CDT agents are) and they're randomly paired up to play one-shot PD, then the CDT agents do better. What does this imply?
  5. Game theory under the UDT line of thinking is generally more confusing than anything CDT agents have to deal with.
  6. UDT assumes that the agent has access to its own source code and inputs as symbol strings, so it can potentially reason about logical correlations between its own decisions and other agents' as well defined mathematical problems. But humans don't have this, so how are humans supposed to reason about such correlations?
  7. Logical conditionals vs counterfactuals, how should these be defined and do the definitions actually lead to reasonable decisions when plugged into logical decision theory?

These are just the major problems that I was trying to solve (or hoping for others to solve) before I mostly stopped working on decision theory and switched my attention to metaphilosophy. (It's been a while so I'm not certain the list is complete.) As far as I know nobody has found definitive solutions to any of these problems yet, and most are wide open.

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The way I see it, all of these problems are reducible to (i) understanding what's up with the monotonicity principle in infra-Bayesian physicalism and (ii) completing a new and yet unpublished research direction (working title: "infra-Bayesian haggling") which shows that IB agents converge to Pareto efficient outcomes[1]. So, I wouldn't call them "wide open".

  1. ^

    Sometimes, but there are assumptions, see child comment for more details.

Even items 1, 3, 4, and 6 are covered by your research agenda? If so, can you quickly sketch what you expect the solutions to look like?

I'll start with Problem 4 because that's the one where I feel closest to the solution. In your 3-player Prisoner's Dilemma, infra-Bayesian hagglers[1] (IBH agents) don't necessarily play CCC. Depending on their priors, they might converge to CCC or CCD or other Pareto-efficient outcome[2]. Naturally, if the first two agents have identical priors then e.g. DCC is impossible, but CCD still is. Whereas, if all 3 have the same prior they will necessarily converge to CCC. Moreover, there is no "best choice of prior": different choices do better in different situations.

You might think this non-uniqueness is evidence of some deficiency of the theory. However, I argue that it's unavoidable. For example, it's obvious that any sane decision theory will play "swerve" in a chicken game against a rock that says "straight". If there was an ideal decision theory X that lead to a unique outcome in every game, the outcome of X playing chicken against X would be symmetric (e.g. flipping a shared coin to decide who goes straight and who swerves, which is indeed what happens for symmetric IBH[3]). This leads to the paradox that the rock is better than X in this case. Moreover, it should really be no surprise that different priors are incomparable, since this is the case even when considering a single learning agent: the higher a particular environment is in your prior, the better you will do on it.

Problems 1,3,6 are all related to infra-Bayesian physicalism (IBP).

For Problem 1, notice that IBP agents are already allowed some sort of "indexical" values. Indeed, in section 3 of the original article we describe agents that only care about their own observations. However, these agents are not truly purely indexical, because when multiple copies co-exist, they all value each other symmetrically. In itself, I don't think this implies the model doesn't describe human values. Indeed, it is always sensible to precommit to care about your copies, so to the extent you don't do it, it's a failure of rationality. The situation seems comparable with hyperbolic time discount: both are value disagreements between copies of you (in the time discount case, these are copies at different times, in the anthropic case, these are copies that co-exist in space). Such a value disagreement might be a true description of human psychology, but rational agents should be able to resolve it via internal negotiations, converging to a fully coherent agent.

However, IBP also seems to implies the monotonicity problem, which is a much more serious problem, if we want the model to be applicable to humans. The main possible solutions I see are:

  1. Find some alternative bridge transform which is not downwards closed but still well-behaved and therefore doesn't imply a monotonicity principle. That wouldn't be terribly surprising, because we don't have an axiomatic derivation of the bridge transform yet: it's just the only natural object we found so far which satisfies all desiderata.
  2. Just admit humans are not IBP agents. Instead, we might model them e.g. as cartesian IBRL agents. Maybe there is a richer taxonomy of intermediate possibilities between pure cartesianism and pure physicalism. Notice that this doesn't mean UDT is completely inapplicable to humans: cartesian IBRL already shows UDT-ish behavior in learnable pseudocausal Newcombian problems and arguably multi-agent scenarios as well (IBH). Cartesian IBRL might depart from UDT in scenarios such as fully acausal trade (i.e. trading with worlds where the agent never existed).
    1. This possibility is not necessarily free of bizarre implications. I suspect that cartesian agents always end up believing in some sort of simulation hypothesis (due to reasons such as  Christiano 2016). Arguably, they should ultimately converge to IBP-like behavior via trade with their simulators. What this looks like in humans, I dare not speculate.
  3. Swallow some bizarre philosophical bullet to reconcile human values with the monotonicity principle. The main example is, accept that worst-than-death qualia don't matter, or maybe don't exist (e.g. people that apparently experience them are temporarily zombies) and that among several copies of you, only the best-off copies matters. I don't like this solution at all, but I still feel compelled to keep a (very skeptical) eye on it for now.

For Problem 3, IBP agents have perfectly well-defined behavior in anthropic situations. The only "small" issue is that this behavior is quite bizarre. The implications depend, again, on how you deal with monotonicity principle.

If we accept Solution 1 above, we might end up with a situation where anthropics devolves to preferences again. Indeed, that would be the case if we allowed arbitrary non-monotonic loss functions. However, it's possible that the alternative bridge transform would impose a different effective constraint on the loss function, which would solve anthropics in some well-defined way which is more palatable than monotonicity.

If we accept Solution 2, then anthropics seems at first glance "epiphenomenal": you can learn the correct anthropic theory empirically, by observing which copy you are, but the laws of physics don't necessarily dictate it. However, under 2a anthropics is dictated by the simulators, or by some process of bargaining with the simulators.

If we accept Solution 3... Well, then we just have to accept how IBP does anthropics off-the-bat.

For Problem 6, it again depends on the solution to monotonocity.

Under Solutions 1 & 3, we might posit that humans do have something like "access to source code" on the unconscious level. Indeed, it seems plausible that you have some intuitive notion of what kind of mind should be considered "you". Alternatively (or in addition), it's possible that there is a version of the IBP formalism which allows uncertainty over your own source code.

Under Solution 2 there is no problem: cartesian IBRL doesn't require access to your own source code.

  1. ^

    I'm saying "infra-Bayesian hagglers" rather than "infra-Bayesian agents" because I haven't yet nailed the natural conditions a learning-algorithm needs to satisfy to enable IBH. I know some examples that do, but e.g. just satisfying an IB regret bound is insufficient. But, this should be thought of as a placeholder for some (hopefully) naturalized agent desiderata.

  2. ^

    It's not always Pareto efficient, see child comment for more details.

  3. ^

    What if there is no shared coin? I claim that, effectively, there always is. In a repeated game, you can e.g. use the parity of time as the "coin". In a one-shot game, you can use the parity of logical time (which can be formalized using metacognitive IB agents).

I don't understand your ideas in detail (am interested but don't have the time/ability/inclination to dig into the mathematical details), but from the informal writeups/reviews/critiques I've seen of your overall approach, as well as my sense from reading this comment of how far away you are from a full solution to the problems I listed in the OP, I'm still comfortable sticking with "most are wide open". :)

On the object level, maybe we can just focus on Problem 4 for now. What do you think actually happens in a 2IBH-1CDT game? Presumably CDT still plays D, and what do the IBH agents do? And how does that imply that the puzzle is resolved?

As a reminder, the puzzle I see is that this problem shows that a CDT agent doesn't necessarily want to become more UDT-like, and for seemingly good reason, so on what basis can we say that UDT is a clear advancement in decision theory? If CDT agents similarly don't want to become more IBH-like, isn't there the same puzzle? (Or do they?) This seems different from the playing chicken with a rock example, because a rock is not a decision theory so that example doesn't seem to offer the same puzzle.

ETA: Oh, I think you're saying that the CDT agent could turn into a IBH agent but with a different prior from the other IBH agents, that ends up allowing it to still play D while the other two still play C, so it's not made worse off by switching to IBH. Can you walk this through in more detail? How does the CDT agent choose what prior to use when switching to IBH, and how do the different priors actual imply a CCD outcome in the end?

...I'm still comfortable sticking with "most are wide open".

 

Allow me to rephrase. The problems are open, that's fair enough. But, the gist of your post seems to be: "Since coming up with UDT, we ran into these problems, made no progress, and are apparently at a dead end. Therefore, UDT might have been the wrong turn entirely." On the other hand, my view is: Since coming up with those problems, we made a lot of progress on agent theory within the LTA, which has implications on those problems among other things, and so far this progress seems to only reinforce the idea that UDT is "morally" correct. That is, not that any of the old attempted formalizations of UDT is correct, but that the intuition behind UDT, and its recommendation in many specific scenarios, are largely justified.

ETA: Oh, I think you're saying that the CDT agent could turn into a IBH agent but with a different prior from the other IBH agents, that ends up allowing it to still play D while the other two still play C, so it's not made worse off by switching to IBH. Can you walk this through in more detail? How does the CDT agent choose what prior to use when switching to IBH, and how do the different priors actual imply a CCD outcome in the end?

While writing this part, I realized that some of my thinking about IBH was confused, and some of my previous claims were wrong. This is what happens when I'm overeager to share something half-baked. I apologize. In the following, I try to answer the question while also setting the record straight.

An IBH agent considers different infra-Bayesian hypotheses starting from the most optimistic ones (i.e. those that allow guaranteeing the most expected utility) and working its way down, until it finds something that works[1]. Such algorithms are known as "upper confidence bound" (UCB) in learning theory. When multiple IBH agents interact, they start with each trying to achieve its best possible payoff in the game[2], and gradually relax their demands, until some coalition reaches a payoff vector which is admissible for it to guarantee. This coalition then "locks" its strategy, while other agents continue lowering their demands until there is a new coalition among them, and so on.

Notice that the pace at which agents lower their demands might depend on their priors (by affecting how many hypotheses they have to cull at each level), their time discounts and maaaybe also other parameters of the learning algorithm. Some properties this process has:

  • Every agents always achieves at least its maximin payoff in the end. In particular, a zero-sum two-player game ends in a Nash equilibrium.
  • If there is a unique strongly Pareto-efficient payoff (such as in Hunting-the-Stag), the agents will converge there.
  • In a two-player game, if the agents are similar enough that it takes them about the same time to go from optimal payoff to maximin payoff, the outcome is strong Pareto-efficient. For example, in a Prisoner's Dilemma they will converge to player A cooperating and player B cooperating some of the time and possibly defecting some of the time, such that player A's payoff is still strictly better than DD. However, without any similarity assumption, they might instead converge to an outcome where one player is doing its maximin strategy and the other its best response to that. In a Prisoner's Dilemma, that would be DD[3].
  • In a symmetric two-player game, with very similar agents (which might still have independent random generators), they will converge to the symmetric Pareto efficient outcome. For example, in a Prisoner's Dilemma they will play CC, whereas in Chicken [version where flipping coin is better than both swerving] they will "flip a coin" (e.g. alternative) to decide who goes straight and who swerves. 
  • The previous bullet is not true with more than two players. There can be stochastic selection among several possible points of convergence, because there are games in which different mutually exclusive coalitions can form. Moreover, the outcome can fail to be Pareto efficient, even if the game is symmetric and the agents are identical (with independent random generators).
  • Specifically in Wei Dai's 3-player Prisoner's Dilemma, IBH among identical agents always produces CCC. IBH among arbitrarily different agents might produce CCD (if one player is very slow to lower its demands, while the other other two lower their demands in the same, faster, pace), or even DDD (if each of the players lowers its demands on its own very different timescale).

We can operationalize "CDT agent" as e.g. a learning algorithm satisfying an internal regret bound (see sections 4.4 and 7.4 in Cesa-Bianchi and Lugosi) and the process of self-modification as learning on two different timescales: a slow outer loop that chooses a learning algorithm for a quick inner loop (this is simplistic, but IMO still instructive). Such an agent would indeed choose IBH over CDT if playing a Prisoner's Dilemma (and would prefer an IBH variant that lowers its demands slowly enough to get more of the gains-of-trade but quickly enough to actually converge), whereas in the 3-player Prisoner's Dilemma there is at least some IBH variant that would be no worse than CDT.

If all players have metalearning in the outer loop, then we get dynamics similar to Chicken [version in which both swerving is better than flipping a coin[4]], where hard-bargaining (slower to lower demands) IBH corresponds to "straight" and soft-bargaining (quick to lower demands) IBH corresponds to "swerve". Chicken [this version] between two identical IBH agents results in both swerving. Chicken beween hard-IBH and soft-IBH results in hard-IBH getting a higher probability of going straight[5]. Chicken between two CDTs results in a correlated equilibrium, which might have some probability of crashing. Chicken between IBH and CDT... I'm actually not sure what happens off the top of my head, the analysis is not that trivial.

 

  1. ^

    This is pretty similar to "modal UDT" (going from optimistic to pessimistic outcomes until you find a proof that some action can guarantee that outcome). I think that the analogy can be made stronger if the modal agent uses an increasingly strong proof system during the search, which IIRC was also considered before. The strength of the proof system then plays the role of "logical time", and the pacing of increasing the strength is analogous to the (inverse function of the) temporal pacing in which an IBH agent lowers its target payoff.

  2. ^

    Assuming that they start out already knowing the rules of the game. Otherwise, they might start from trying to achieve payoffs which are impossible even with the cooperation of other players. So, this is a good model if learning the rules is much faster than learning anything to do with the behavior of other players, which seems like a reasonable assumption in many cases.

  3. ^

    It is not that surprising that two sufficiently dissimilar agents can defect. After all, the original argument for superrational cooperation was: "if the other agent is similar to you, then it cooperates iff you cooperate".

  4. ^

    I wish we had good names for the two version of Chicken.

  5. ^

    This seems nicely reflectively consistent: soft/hard-IBH in the outer loop produces soft/hard-IBH respectively in the inner loop. However, two hard hard-IBH agents in the outer loop produce two soft-IBH agents in the inner loop. On the other hand, comparing absolute hardness between outer and inner loop seems not very meaningful, whereas comparing relative-between-players hardness between outer and inner loop is meaningful.

But, the gist of your post seems to be: "Since coming up with UDT, we ran into these problems, made no progress, and are apparently at a dead end. Therefore, UDT might have been the wrong turn entirely."

This is a bit stronger than how I would phrase it, but basically yes.

On the other hand, my view is: Since coming up with those problems, we made a lot of progress on agent theory within the LTA

I tend to be pretty skeptical of new ideas. (This backfired spectacularly once, when I didn't pay much attention to Satoshi when he contacted me about Bitcoin, but I think in general has served me well.) My experience with philosophical questions is that even when some approach looks a stone's throw away from a final solution to some problem, a bunch of new problems pop up and show that we're still quite far away. With an approach that is still as early as yours, I just think there's quite a good chance it doesn't work out in the end, or gets stuck somewhere on a hard problem. (Also some people who have digged into the details don't seem as optimistic that it is the right approach.) So I'm reluctant to decrease my probability of "UDT was a wrong turn" too much based on it.

The rest of your discussion about 2TDT-1CDT seems plausible to me, although of course depends on whether the math works out, doing something about monotonicity, and also a solution to the problem of how to choose one's IBH prior. (If the solution was something like "it's subjective/arbitrary" that would be pretty unsatisfying from my perspective.)

...the problem of how to choose one's IBH prior. (If the solution was something like "it's subjective/arbitrary" that would be pretty unsatisfying from my perspective.)

 

It seems clear to me that the prior is subjective. Like with Solomonoff induction, I expect there to exist something like the right asymptotic for the prior (i.e. an equivalence class of priors under the equivalence relation where  and  are equivalent when there exists some  s.t.  and ), but not a unique correct prior, just like there is no unique correct UTM. In fact, my arguments about IBH already rely on the asymptotic of the prior to some extent.

One way to view the non-uniqueness of the prior is through an evolutionary perspective: agents with prior  are likely to evolve/flourish in universes sampled from prior , while agents with prior  are likely to evolve/flourish in universes sampled from prior . No prior is superior across all universes: there's no free lunch.

For the purpose of AI alignment, the solution is some combination of (i) learn the user's prior and (ii) choose some intuitively appealing measure of description complexity, e.g. length of lambda-term (i is insufficient in itself because you need some ur-prior to learn the user's prior). The claim is, different reasonable choices in ii will lead to similar results.

Given all that, I'm not sure what's still unsatisfying. Is there any reason to believe something is missing in this picture?

a population of mostly TDT/UDT agents and few CDT agents (and nobody knows who the CDT agents are) and they're randomly paired up to play one-shot PD, then the CDT agents do better. What does this imply?


My current hot take is that this is not a serious problem for TDT/UDT. It's just a special case of the more general phenomenon that it's game-theoretically great to be in a position where people think they are correlated/entangled with you when actually you know they aren't. Analogous to how it's game-theoretically great to be in a position where you know you can get away with cheating and everyone else thinks they can't.

Indexical values are not reflectively consistent. UDT "solves" this problem by implicitly assuming (via the type signature of its utility function) that the agent doesn't have indexical values.


Nonindexical values aren't reflectively consistent either, if you are updateful. Right? 

Curated, both for the OP (which nicely lays out some open problems and provides some good links towards existing discussion) as well as the resulting discussion which has had a number of longtime contributors to LessWrong-descended decision theory weighing in.