In the examples, sometimes the problem is people having different goals for the discussion, sometimes it is having different beliefs about what kinds of discussions work, and sometimes it might be about almost object-level beliefs. If “frame” refers to all of that, then it’s way too broad and not a useful concept. If your goal is to enumerate and classify the different goals and different beliefs people can have regarding discussions, that’s great, but possibly to broad to make any progress.
My own frustration with this topic is lack of real data. Apart from “FOOM Debate”, the conversations in your post are all fake. To continue your analogy in another comment, this is like doing zoology by only ever drawing cartoons of animals, without ever actually collecting or analyzing specimens. Good zoologists would collect many real discussions, annotate them, classify them, debate about those classifications, etc. They may also tamper with ongoing discussions. You may be doing some of that privately, but doing it publicly would be better. Unfortunately there seem to be norms against that.
Making long term predictions is hard. That’s a fundamental problem. Having proxies can be convenient, but it’s not going to tell you anything you don’t already know.
That’s what I think every time I hear “history repeats itself”. I wish Scott had considered the idea.
The biggest claim Turchin is making seems to be about the variance of the time intervals between “bad” periods. Random walk would imply that it is high, and “cycles” would imply that it is low.
For example, say I wanted to know how good/enjoyable a specific movie would be.
My point is that “goodness” is not a thing in the territory. At best it is a label for a set of specific measures (ratings, revenue, awards, etc). In that case, why not just work with those specific measures? Vague questions have the benefit of being short and easy to remember, but beyond that I see only problems. Motivated agents will do their best to interpret the vagueness in a way that suits them.
Is your goal to find a method to generate specific interpretations and procedures of measurement for vague properties like this one? Like a Shelling point for formalizing language? Why do you feel that can be done in a useful way? I’m asking for an intuition pump.
Can you be more explicit about your definition of “clearly”?
Certainly there is some vagueness, but it seems that we manage to live with it. I’m not proposing anything that prediction markets aren’t already doing.
“What is the relative effectiveness of AI safety research vs. bio risk research?”
If you had a precise definition of “effectiveness” this shouldn’t be a problem. E.g. if you had predictions for “will humans go extinct in the next 100 years?” and “will we go extinct in the next 100 years, if we invest 1M into AI risk research?” and “will we go extinct, if we invest 1M in bio risk research?”, then you should be able to make decisions with that. And these questions should work fine in existing forecasting platforms. Their long term and conditional nature are problems, of course, but I don’t think that can be helped.
“How much value has this organization created?”
That’s not a forecast. But if you asked “How much value will this organization create next year?” along with a clear measure of “value”, then again, I don’t see much of a problem. And, although clearly defining value can be tedious (and prone to errors), I don’t think that problem can be avoided. Different people value different things, that can’t be helped.
One solution attempt would be to have an “expert panel” assess these questions
Why would you do that? What’s wrong with the usual prediction markets? Of course, they’re expensive (require many participants), but I don’t think a group of experts can be made to work well without a market-like mechanism. Is your project about making such markets more efficient?
While it’s true that preferences are not immutable, the things that change them are not usually debate. Sure, some people can be made to believe that their preferences are inconsistent, but then they will only make the smallest correction needed to fix the problem. Also, sometimes debate will make someone claim to have changed their preferences, just to that they can avoid social pressures (e.g. “how dare you not care about starving children!”), but this may not reflect in their actions.
Regardless, my claim is that many (or most) people discount a lot, and that this would be stable under reflection. Otherwise we’d see more charity, more investment and more work on e.g. climate change.
Ok, that makes the real incentives quite different. Then, I suspect that these people are navigating facebook using the intuitions and strategies from the real world, without much consideration for the new digital environment.
Yes, and you answered that question well. But the reason I asked for alternative responses, was so that I could compare them to unsolicited recommendations from the anime-fan’s point of view (and find that unsolicited recommendations have lower effort or higher reward).
Also, I’m not asking “How did your friend want the world to be different”, I’m asking “What action could your friend have taken to avoid that particular response?”. The friend is a rational agent, he is able to consider alternative strategies, but he shouldn’t expect that other people will change their behavior when they have no personal incentive to do so.
What is the domain of U? What inputs does it take? In your papers you take a generic Markov Decision Process, but which one will you use here? How exactly do you model the real world? What is the set of states and the set of actions? Does the set of states include the internal state of the AI?
You may have been referring to this as “4. Issues of ontology”, but I don’t think the problem can be separated from your agenda. I don’t see how any progress can be made without answering these questions. Maybe your can start with naive answers, and to move on to something more realistic later. If so I’m interested in what those naive world models look like. And I’m suspicious of how well human preferences would translate onto such models.
Other AI construction methods could claim that the AI will learn the optimal world model, by interacting with the world, but I don’t think this solution can work for your agenda, since the U function is fixed from the start.
Discounting. There is no law of nature that can force me to care about preventing human extinction years from now, more than eating a tasty sandwich tomorrow. There is also no law that can force me to care about human extinction much more that about my own death.
There are, of course, more technical disagreements to be had. Reasonable people could question how bad unaligned AI will be or how much progress is possible in this research. But unlike those questions, the reasons of discounting are not debatable.
I do things my way because I want to display my independence (not doing what others tell me) and intelligence (ability to come up with novel solutions), and because I would feel bored otherwise (this is a feature of how my brain works, I can’t help it).
“I feel independent and intelligent”, “other people see me as independent and intelligent”, “I feel bored” are all perfectly regular outcomes. They can be either terminal or instrumental goals. Either way, I disagree that these cases somehow don’t fit in the usual preference model. You’re only having this problem because you’re interpreting “outcome” in a very narrow way.
Yes. The latter seems to be what OP is asking about: “If one wanted it to not happen, how would one go about that?”. I assume OP is taking the perspective of his friends, who are annoyed by this behavior, rather than the perspective of the anime-fans, who don’t necessarily see anything wrong with the situation.
That sounds reasonable, but the proper thing is not usually the easy thing, and you’re not going to make people do the proper thing just by saying that it is proper.
If we want to talk about this as a problem in rationality, we should probably talk about social incentives, and possible alternative strategies for the anime-hater (you’re now talking about a better strategy for the anime-fan, but it’s not good to ask other people to solve your problems). Although I’m not sure to what extent this is a problem that needs solving.
And then the other person says “no thanks”, and you both stand in awkward silence? My point is that offering recommendations is a natural thing to say, even if not perfect, and it’s nice to have something to say. If you want to discourage unsolicited recommendations, then you need to propose a different trajectory for the conversation. Changing topic is hard, and simply going away is rude. People give unsolicited recommendations because it seems to be the best option available.
Sure, but it remains unclear what response the friend wanted from the other person. What better options are there? Should they just go away? Change topic? I’m looking for specific answers here.
a friend of mine observed that he couldn’t talk about how he didn’t like anime without a bunch of people rushing in to tell him that anime was actually good and recommending anime for him to watch
What response did your friend want? The reaction seems very natural to me (especially from anime fans). Note that your friend as at some point tried watching anime, and he has now chosen to talk about anime, which could easily mean that on some level he wants to like anime, or at least understand why others like it.
I got this big impossibility result
That’s a part of the disagreement. In the past you clearly thought that Occam’s razor was an “obvious” constraint that might work. Possibly you thought it was a unique such constraint. Then you found this result, and made a large update in the other direction. That’s why you say the result is big—rejecting a constraint that you already didn’t expect to work wouldn’t feel very significant.
On the other hand, I don’t think that Occam’s razor is unique such constraint. So when I see you reject it, I naturally ask “what about all the other obvious constraints that might work?”. To me this result reads like “0 didn’t solve our equation therefore the solution must be very hard”. I’m sure that you have strong arguments against many other approaches, but I haven’t seen them, and I don’t think the one in OP generalizes well.
I’d need to see these constraints explicitly formulated before I had any confidence in them.
This is a bit awkward. I’m sure that I’m not proposing anything that you haven’t already considered. And even if you show that this approach is wrong, I’d just try to put a band-aid on it. But here is an attempt:
First we’d need a data set of human behavior with both positive and negative examples (e.g. “I made a sandwitch”, “I didn’t stab myself”, etc). So it would be a set of tuples of state s, action a and +1 for positive examples, −1 for negative ones. This is not trivial to generate, especially it’s not clear how to pick negative examples, but here too I expect that the obvious solutions are all fine. By the way, I have no idea how the examples are formalized, that seems like a problem, but it’s not unique to this approach, so I’ll assume that it’s solved.
Next, given a pair (p, R), we would score it by adding up the following:
1. p(R) should accurately predict human behavior. So we want a count of p(R)(s)=a for positive cases and p(R)(s)!=a for negative cases.
2. R should also predict human behavior. So we want to sum R(s, a) for positive examples, minus the same sum for negative examples.
3. Regularization for p.
4. Regularization for R.
Here we are concerned about overfitting R, and don’t care about p as much, so terms 1 and 4 would get large weights, and terms 2, 3 would get smaller weights.
Finally we throw machine learning at the problem to maximize this score.
So it seems that there was progress in applied rationality and in AI. But that’s far from everything LW has talked about. What about more theoretical topics, general problems in philosophy, morality, etc? Do you feel than discussing some topics resulted in no progress and was a waste of time?
There’s some debate about which things are “improvements” as opposed to changes.
Important question. Does the debate actually exist, or is this a figure of speech?
1 is trivial, so yes. But I don’t agree with 2. Maybe the disagreement comes from “few” and “obvious”? To be clear, I count evaluating some simple statistic on a large data set as one constraint. I’m not so sure about “obvious”. It’s not yet clear to me that my simple constraints aren’t good enough. But if you say that more complex constraints would give us a lot more confidence, that’s reasonable.
From OP I understood that you want to throw out IRL entirely. e.g.
If we give up the assumption of human rationality—which we must—it seems we can’t say anything about the human reward function. So it seems IRL must fail.
seems like an unambiguous rejection of IRL and very different from
Our hope is that with some minimal assumptions about planner and reward we can infer the rest with enough data.