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The diagram thing is weird, I explicitly added it back in when I posted.
Regardless, I somehow ended up posting this on the wrong account, reposted in the correct account.
The drafting software I was using represented headings that way (as I said this wasn't written on LW). I've fixed the headings.
I think this is actually a general pattern that happens in most knowledge worker careers, not only late in careers. Certainly when I was a career coach one of the key things I did to help people move up in their careers was to help them move a level up in their thinking.
I think one of the reasons that the particular meta-level up move that you're talking about happens late in careers is that at that point people who are at the top of their careers basically don't have another meta-level up they can move to understand their field - they've already made that move. So the only meta-level they can move to next is to apply the move to itself.
There are people who have done this in a variety of fields but they seem to be largely niche. One reason that this may be is that most medicine literally has magic pills that allow you to apply each of the solutions, whereas most other effective process models require a deep understanding of each step to apply them.
For instance TRIZ is an attempt to create a clear process model for "How to Come up With Creative Solutions as an Engineer. " THIS is one process model for TRIZ, and each step in that process model would expand to a diagram that's at least as complicated.
This is an example of one teeny tiny part of the "How to Actually Do Self Help" process model in my head, and each part itself requires deep understanding and background to make any sense.
Boyd wrote about the OODA loop in his late 40's but never seemed to make the next meta level jump up to trying to instill the kind of reasoning that generated the OODA loop (or EM theory for that matter) pedagogically.
This is exactly what he did with "The Discourse on Winning and Losing"
Boyd is one of my favorite examples of a great process modeler and meta-level thinker because he did it at every level of his career:
Process modelling of why he was such a good fighter-pilot led to EM theory.
Process modelling of why EM theory worked led to OODA loop.
Process Modelling of how he kept doing great process modelling (including the OODA loop) led to "Destruction and Creation" and Process modelling of how the OODA loop worked led to "A Discourse on Winning and Losing"
Is it possible to make something a terminal value? If so, how?
Agree, this was my thought as well.
There seems to be a weird need in this community to over argue obvious conclusions.
This whole post seems to boil down to:
- You are altruistic and smart.
- You want more altruistic and smart people.
- Therefore, you should propagate your genes.
Similar to the recent "Dragon Army Baracks", which seems to boil down to:
- We want an effective group organization.
- Most effective groups seem to be hierarchical with a clear leader.
- Therefore, it might make sense for us to try being hierarchical with a clear leader..
I mean, I get that there's a lot of mental models that led to these conclusions, and you want to share the mental models as well... but it seems like separating out the teaching of the mental models and the arguments themselves into separate pieces of content might make sense.
The link no longer works... what is the book?
Are you tracking your calibration with something like prediction book? You may be generally calibrated And this could have just been an instance of a low probability event happening
You're making a lot of assumptions here about what other people think.
I like Gleb's content, and think that people who criticize his methods have a point, but also at times veer away from consequentialism into virtue ethics.
So if I have a 1 in 60 million chance of being the decisive vote, and 1,000,000 other voters who also voted for the same candidate could also be seen as the "decisive vote", wouldn't that mean that my EV was 640,000/1,000,000 = .64 cents?
Intuitively it seems like 640,000 for voting is way overvalued compared to some other actions, and this diffusion of responsibility argument seems to make some sort of sense.
You want some sort of adaptive or sequential design (right?), so the optimal design not being terribly helpful is not surprising: they're more intended for fixed up-front designing of experiments.
So after looking at the problem I'm actually working on, I realize an adaptive/sequential design isn't really what I'm after.
What I really want is a fractional factorial model that takes a prior (and minimizes regret between information learned and cumulative score). It seems like the goal of multi-armed bandit is to do exactly that, but I only want to do it once, assuming a fixed prior which doesn't update over time.
Do you think your monte-carlo Bayesian experimental design is the best way to do this, or can I utilize some of the insights from Thompson sampling to make this process a bit less computationally expensive (which is important for my particular use case)?
Agents based on lookup tables.
Let's say I have a set of students, and a set of learning materials for an upcoming test. My goal is to run an experiment to see which learning materials are correlated with better scores on the test via multiple linear regression. I'm also going to make the simplifying assumption that the effects of the learning materials are independent.
I'm looking for an experimental protocol with the following conditions:
I want to be able to give each student as many learning materials as possible. I don't want a simple RCT, but a factorial experiment where students get many materials and the statistics tease out the linear regression.
I have a prior about which learning materials will do better, I'd like to utilize this prior by originally distributing these materials to more students.
(Bonus) Students are constantly entering this class, I'd love to be able to do some multi-armed bandit thingy where as I get more data I continually change this prior.
I've looked at most of the links going from https://en.wikipedia.org/wiki/Optimal_design but they mostly show the mathematical interpretation of each method, not a clear explanation of in which conditions you'd use that method.
Thanks!