All of lambdaepsilon's Comments + Replies

I disagree.

You can approximate OKC as a two-person game: Weird = Honest, Polished=Dishonest. U(WW)=+3/+3, U(WP)=0/+4, U(PP)=0/0, then you have the usual Prisoner-Dilemma payoff (motivation: being honest will generate more long-term utility).

This is a bad approximation, as OkCupid is a multi-player-game, so it's more complicated than the classical 2-player Prisoner's Dilemma. That's where the tragedy of the commons comes in. In an environment where nearly everyone plays defect-bot, a lot of utility is destroyed. But, tit-for-tat players have

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Yes, I completely agree with the weaker formulation "irreducible using only THESE means", like e.g. Polynomials, MPTs, First-Order Logic etc.

I think the answer to the question "are there irreducibly complex statistical models?" is yes.

I agree that there are some sources of irreducible complexity, like 'truely random' events.

To me, the field of cognition does not pattern-match to 'irrecducibly complex', but more to 'We don't have good models. Yet, growth mindset'. So, unless you have some patterns where you can prove that they are irrreducible, I will stick with my priors I guess. The example you gave me,

For a very simple example, if you're tr

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3sarahconstantin
Ah, how you think about that example helps clarify. I wasn't even thinking about the possibility of an AI that could "learn" the analytic form of Weierstrass function, I was thinking about the fact that trying to fit a polynomial to it would be arbitrarily hard. Obviously "not modelable by ANY means" is a much stronger claim than "if you use THESE means, then your model needs a lot of epicycles to be close to accurate." (Analyst's mindset vs. computer scientist's mindset; the computer scientist's typical class of "possible algorithms" is way broader. I'm more used to thinking like an analyst.) I think you and I are pretty close to agreement at this point.

These are heuristic descriptions; these essays don’t make explicit how to test whether a model is interpretable or not. I think it probably has something to do with model size; is the model reducible to one with fewer parameters, or not?

If you use e.g. the Akaike Information Criterion for model evaluation, you get around the size problem in theory. Model size is then something you score explicitely.

Personally, I still have intuitive problems with this approach though: many phenomenological theories in physics are easier to interpret than Quantum Mechanics

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8sarahconstantin
Also, WOW I had no idea Arbital's writing was so good. (The Solomonov Inductor link.) In case anyone else didn't click the first time, it's not just a definition, it's a dialogue, probably by Eliezer, and it's super cool.
4sarahconstantin
So, it's usually possible to create "adversarial examples" -- datasets that are so "perverse" that they resist accurate prediction by simple models and actually require lots of variables. (For a very simple example, if you're trying to fit a continuous curve based on a finite number of data points, you can make the problem arbitrarily hard with functions that are nowhere differentiable.) I'm not being that rigorous here, but I think the answer to the question "are there irreducibly complex statistical models?" is yes. You can make models such that any simplification has a large accuracy cost. Are there irreducibly complex statistical models that humans or animals use in real life? That's a different and harder question, and my answer there is more like "I don't know, but I could believe so."

I am highly suspicious of any explanation that includes the claim "then every understood this, and so they behaved differently"

I agree. Can you point to where the author uses this in his model?

(I have no strong opinion on the model, I mostly pattern-matched when I read the call for more models)

I like your models. Do you think you can make them into quantitative ones?

Here are some caveats:

Model 1: Escalating Asks and Rewards

  • I have experienced several communites with a 'not me'-mentality, where people are on bord for the fun, but do not want any work. This is strong evidence against universality of Model 1.

  • On the other hand, I have experinced communities where this escalation happened and people told stories of how important their work was. So I definitely think this happens, but I can't think of a good predictor for whon or when mo

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Here's an informal model I saw a while ago, with some similarities to Model 2:

"Subcultures were the main creative cultural force from roughly 1975 to 2000, when they stopped working. Why?

One reason—among several—is that as soon as subcultures start getting really interesting, they get invaded by muggles, who ruin them. Subcultures have a predictable lifecycle, in which popularity causes death. Eventually—around 2000—everyone understood this, and gave up hoping some subculture could somehow escape this dynamic."

https://meaningness.com/geeks-mops-sociopaths

4Chris_Leong
I suspect that subcultures tend to increase when there is widespread dissatisfaction with mainstream society. Exiting mainstream culture to live in your own bubble has a high cost, but much less of a cost if you dislike it.
3habryka
I am highly suspicious of any explanation that includes the claim "then every understood this, and so they behaved differently". I don't think it's impossible for population-wide updates and observations to occur, but this seems like a complex enough phenomenon that I would highly doubt a majority of the population could, even implicitly, come to agreement on it.

I think this is strongly connected to the Typical Mind Fallacy.

I did a quick inventory of distortions that I recognize often [I live in a leftist techy-academic bubble full of socially and sexually permissive people].

Other people's properties that I overestimate because of my bubble:

  • Intelligence

  • Comptence (Imposter-Syndrome)

  • Knowledge (expecting short inferential distances)

  • Physical fitness

  • Available amount of leisure

  • Susceptibility to arguments and evidence

Stuff that is (relatively) easy for people in my bubble, but seems to be hard outside:

  • Reading a nove

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