Wiki Contributions


The link is broken. I was only able to find the article here, with the wayback machine.

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.

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