Who are the current moderators?

Observational data doesn't allow one to distinguish correlation and causation.

No? If I observe a hammer striking a nail and the nail sinking into the wooden plank, is anyone going to argue that it's mere correlation and not causation?

Observational data doesn't always allow one one to distinguish correlation and causation.

I am also a bit confused since you're talking about learning values but your example is not about values but about a causal relationship.

As the old joke goes, Alzheimer's is the best illness, there is no pain and each morning you get lots of interesting news.

But note that improving the model would result in less pleasant experiences of wonder, but also in less unpleasant experiences of disappointment. Basically you reduce your variance, but it's not obvious to me that you imperfect model necessarily has a pessimistic bias.

There are other emotional reactions which should register as confusion but don't.

Imagine a smart person who sees asphalt being deposited to pave a road. "How disgusting," they think. "Surely our civilization can think of something better than this." They spend a few minutes ruminating on various solutions for road construction and maintenance that would obviously be better than asphalt and then get distracted and never think about it again.

They thus manage to never realize that asphalt is a fantastic solution to this problem, that stacks of PhDs have been written on asphalt chemistry and thermal processes, that it's a highly optimized, cheap, self-healing material, that it's the most economical solution by leaps and bounds. All they noticed was disgust based purely on error and ignorance.

Any thought of the form "That's stupid, I can easily see a better way" should qualify as confusion.

Model selection is an interesting process, because of overfitting: when you add parameters, are you better modelling the intrinsic noise or a fundamental information that is interesting to you?
This is the overarching question, and I find interesting that it depends on the existence of a meta-model, which is usually very implicit.

A thing already known to computer scientists, but still useful to remember: as per Kleene's normal form theorem, a universal Turing machine is a primitive recursive function.
Meaning that if an angel gives you the encoding of a program you only need recursion, and not unbounded search, to run it.

Sounds like Convict Conditioning to me.

I haven't read it myself, but some friends have praised the book and the exercises included.

It's amazing how the "noncentral fallacy" is rooted deeply into human psychology.
Using "weird" to escape the gravitational pull of a word it's interesting, and suggests a general strategy: Martin Luther King was an heroic criminal, abortion is an ethic murder, etc.
Mmh, better but not the best.

'Weird' seems to work well only for self-identification, though.

I have a question for LWers who are non-native English speakers.

I am working on a software system for linguistically sophisticated analysis of English text. At the core of the system is a sentence parser. Unlike most other research in NLP, a central goal of my work is to develop linguistic knowledge and then build that knowledge into the parser. For example, my system knows that the verb ask connects strongly to subjectized infinitive phrases ("I asked him to take out the trash"), unlike most other verbs.

The system also has a nice parse visualization tool, which shows the grammatical structure of an input sentence. You can check it out here.

This work began as a research project and I am trying to figure out a way to commercialize it. One of my ideas is to use the system as a tool for helping students to learn English. Students could submit confusing sentences to the system and observe the parse tree, allowing them to understand the grammatical structure. They could also submit their own written sentences to the system, as a way of checking their grammar. Teachers of ESL students might also ask them to submit their class papers to the parser to check for obvious mistakes (apparently there are many people who can communicate well in spoken English but whose written English is full of mistakes).

I would also write up a series of articles about subtle points of English grammar, such as phrasal verbs, argument structure, verb tense, and so on. Students could then read the articles and experiment with using the relevant grammar patterns in the parser.

Does this sound like a plausible product that people would want to use? Are there products already on the market that do something similar? (I am aware of Grammarly, but it doesn't appear to show parse trees).

I think you should make a distinction if the different behaviours comes from different circumstances or not.
If their environment is always the same, then I think the only to have what you ask is if the system has a hidden, very specific parameter, that says "when X and Y and Z happens, zig instead of zagging".
Otherwise, if the model is slightly chaotic, then an important alteration to the environment might provoke very different behaviour.

For the first type of agent, think of two Markov chains almost identical, only one has a very improbable arc to a stable subnet that doesn't exists in the other chain.
For the second type, think of two similar strange attractors, that have different behaviours away from the stable parameters. They will be approximately identical in the same zone and be very different away from that zone.