Same person as <a href="https://www.lesserwrong.com/users/nostalgebraist2point0">nostalgebraist2point0</a>, but now I have my account back.
Thanks, the floor/ceiling distinction is helpful.
I think "ceilings as they exist in reality" is my main interest in this post. Specifically, I'm interested in the following:
In more detail: it's one thing to be able to assess quick heuristics, and it's another (and better) one to be able to assess quick heuristics quickly. It's possible (maybe) to imagine a convenient situation where the theory of each "speed class" among fast decisions is compressible enough to distill down to something which can be run in that speed class and still provide useful guidance. In this case there's a possibility for the theory to tell us why our behavior as a whole is justified, by explaining how our choices are "about as good as can be hoped for" during necessarily fast/simple activity that can't possibly meet our more powerful and familiar notions of decision rationality.
However, if we can't do this, it seems like we face an exploding backlog of justification needs: every application of a fast heuristic now requires a slow justification pass, but we're constantly applying fast heuristics and there's no room for the slow pass to catch up. So maybe a stronger agent could justify what we do, but we couldn't.
I expect helpful theories here to involve distilling-into-fast-enough-rules on a fundamental level, so that "an impractically slow but working version of the theory" is actually a contradiction in terms.
But it seems like the core strategy--be both doing object-level cognition and meta-level cognition about how you're doing object-level cognitive--is basically the same.
It remains unclear to me whether the right way to find these meta-strategies is something like "start at the impractical ideal and rescue what you can" or "start with something that works and build new features"; it seems like modern computational Bayesian methods look more like the former than the latter.
I'd argue that there's usually a causal arrow from practical lore to impractical ideals first, even if the ideals also influence practice at a later stage. Occam's Razor came before Solomonoff; "change your mind when you see surprising new evidence" came before formal Bayes. The "core strategy" you refer to sounds like "do both exploration and exploitation," which is the sort of idea I'd imagine goes back millennia (albeit not in those exact terms).
One of my goals in writing this post was to formalize the feeling I get, when I think about an idealized theory of this kind, that it's a "redundant step" added on top of something that already does all the work by itself -- like taking a decision theory and appending the rule "take the actions this theory says to take." But rather than being transparently vacuous, like that example, they are vacuous in a more hidden way, and the redundant steps they add tend to resemble legitimately good ideas familiar from practical experience.
Consider the following (ridiculous) theory of rationality: "do the most rational thing, and also, remember to stay hydrated :)". In a certain inane sense, most rational behavior "conforms to" this theory, since the theory parasitizes on whatever existing notion of rationality you had, and staying hydrated is generally a good idea and thus does not tend to conflict with rationality. And whenever staying hydrated is a good idea, one could imagine pointing to this theory and saying "see, there's the hydration theory of rationality at work again." But, of course, none of this should actually count in the "hydration theory's" favor: all the real work is hidden in the first step ("do the most rational thing"), and insofar as hydration is rational, there's no need to specify it explicitly. This doesn't quite map onto the R/S schema, but captures the way in which I think these theories tend to confuse people.
If the more serious ideals we're talking about are like the "hydration theory," we'd expect them to have the appearance of explaining existing practical methods, and of retrospectively explaining the success of new methods, while not being very useful for generating any new methods. And this seems generally true to me: there's a lot of ensemble-like or regularization-like stuff in ML that can be interpreted as Bayesian averaging/updating over some base space of models, but most of the excitement in ML is in these base spaces. We didn't get neural networks from Bayesian first principles.
OTOH, doing a minimax search of the game tree for some bounded number of moves, then applying a simple board-evaluation heuristic at the leaf nodes, is a pretty decent algorithm in practice.
I've written previously about this kind of argument -- see here (scroll down to the non-blockquoted text). tl;dr we can often describe the same optimum in multiple ways, with each way giving us a different series that approximates the optimum in the limit. Whether any one series does well or poorly when truncated to N terms can't be explained by saying "it's a truncation of the optimum," since they all are; these truncations properties are facts about the different series, not about the optimum. I illustrate with different series expansions for π.
Furthermore, it seems like there's a pattern where, the more general the algorithmic problem you want to solve is, the more your solution is compelled to resemble some sort of brute-force search.
You may be right, and there are interesting conversations to be had about when solutions will tend to look like search and when they won't. But this doesn't feel like it really addresses my argument, which is not about "what kind of algorithm should you use" but about the weirdness of the injunction to optimize over a space containing every procedure you could ever do, including all of the optimization procedures you could ever do. There is a logical / definitional weirdness here that can't be resolved by arguments about what sorts of (logically / definitionally unproblematic) algorithms are good or bad in what domains.
This post feels quite similar to things I have written in the past to justify my lack of enthusiasm about idealizations like AIXI and logically-omniscient Bayes. But I would go further: I think that grappling with embeddedness properly will inevitably make theories of this general type irrelevant or useless, so that "a theory like this, except for embedded agents" is not a thing that we can reasonably want. To specify what I mean, I'll use this paragraph as a jumping-off point:
Embedded agents don’t have the luxury of stepping outside of the universe to think about how to think. What we would like would be a theory of rational belief for situated agents which provides foundations that are similarly as strong as the foundations Bayesianism provides for dualistic agents.
Most "theories of rational belief" I have encountered -- including Bayesianism in the sense I think is meant here -- are framed at the level of an evaluator outside the universe, and have essentially no content when we try to transfer them to individual embedded agents. This is because these theories tend to be derived in the following way:
For example, in Solomonoff, S is defined by computability while R is allowed to be uncomputable. In the LIA construction, S is defined by polytime complexity while R is allowed to run slower than polytime. In logically-omniscient Bayes, finite sets of hypotheses can be manipulated in a finite universe but the full Boolean algebra over hypotheses generally cannot.
I hope the framework I've just introduced helps clarify what I find unpromising about these theories. By construction, any agent you can actually design and run is a single element of S (a "practical strategy"), so every fact about rationality that can be incorporated into agent design gets "hidden inside" the individual s∈S, and the only things you can learn from the "ideal theory" R are things which can't fit into a practical strategy.
For example, suppose (reasonably) that model averaging and complexity penalties are broadly good ideas that lead to good results. But all of the model averaging and complexity penalization that can be done computably happens inside some Turing machine or other, at the level "below" Solomonoff. Thus Solomonoff only tells you about the extra advantage you can get by doing these things uncomputably. Any kind of nice Bayesian average over Turing machines that can happen computably is (of course) just another Turing machine.
This also explains why I find it misleading to say that good practical strategies constitute "approximations to" an ideal theory of this type. Of course, since R just says to follow the best strategies in S, if you are following a very good strategy in S your behavior will tend to be close to that of R. But this cannot be attributed to any of the searching over S that R does, since you are not doing a search over S; you are executing a single member of S and ignoring the others. Any searching that can be done practically collapses down to a single practical strategy, and any that doesn't is not practical. Concretely, this talk of approximations is like saying that a very successful chess player "approximates" the rule "consult all possible chess players, then weight their moves by past performance." Yes, the skilled player will play similarly to this rule, but they are not following it, not even approximately! They are only themselves, not any other player.
Any theory of ideal rationality that wants to be a guide for embedded agents will have to be constrained in the same ways the agents are. But theories of ideal rationality usually get all of their content by going to a level above the agents they judge. So this new theory would have to be a very different sort of thing.