abstractapplic

• abstractapplicNov 26, 2024, 1:17 AM

  2 points

  0

  on: Ul­tralearn­ing in 80 days

  CS from MIT OCW

  Good choice of topic.

  (5:00-6:00 AM)

  (6:00-7:00 AM)

  Everyone has their own needs and tolerances, so I won’t presume to know yours . . . and if you’re trying to build daily habits, “every morning” is probably easier to reliably schedule than “every night” . . . but still, sleep is a big deal, especially for intellectual work. If you’re not unsually good at going without for long stretches, and/​or planning to turn in before 10pm to compensate . . . you might benefit from a slightly less Spartan schedule.

  • Put together a plan to learn to write and execute it.

  What kind(s) of writing do you want to be able to produce?

  • Practice

  I’m curious how you plan on practicing your rationality, and how you intend to measure improvement. As far as I can tell our subculture has been trying to figure this out for a decade and change, with sharply limited success.

• abstractapplicNov 23, 2024, 12:41 PM

  2 points

  0

  on: Les­sons I’ve Learned from Self-Teaching

  compute $\int x\log xdx$

  I don’t remember the equations for integration by parts and haven’t used them in years. However, when I saw this, I immediately started scribbling on the whiteboard by my bed, thinking:

  “Okay, so start with (x^2)log(x). Differentiating that gives two times the target, but also gives us a spare x we’d need to get rid of. So the answer is (0.5)(x^2)log(x) - (x^2)/​4.”

  So I actually think you’re right in general but wrong on this specific example: getting a deep sense for what you’re doing when you’re doing integration-by-parts would be a more robust help than rote memorization.

  (Though rote memorization and regular practice absolutely have their place; if I’d done more of those I’d have remembered to stick a “+c” on the end.)

• abstractapplicNov 20, 2024, 1:21 AM

  4 points

  0

  in reply to: cousin_it’s comment on: Com­ment re­ply: my low-qual­ity thoughts on why CFAR didn’t get farther with a “real/​effi­ca­cious art of ra­tio­nal­ity”

  Something like D&D.Sci, then?

The For­ag­ing (Ex-)Ban­dit [Rule­set & Reflec­tions]

• abstractapplicNov 14, 2024, 8:05 PM

  2 points

  0

  in reply to: Martin Randall’s comment on: In­fer­en­tial Game: The For­ag­ing (Ex-)Bandit

  Given the setup I was sad there wasn’t an explicit target or outcome in terms of how much food was needed to get home safely.

  Good point; I’ve amended the game accordingly. Thank you.

In­fer­en­tial Game: The For­ag­ing (Ex-)Bandit

• abstractapplicNov 9, 2024, 11:55 PM

  7 points

  5

  on: LifeKeeper Diaries: Ex­plor­ing Misal­igned AI Through In­ter­ac­tive Fiction

  I can’t get any of the AIs to produce any output other than

  Today marks another [X] years of watching over my beloved human. As they age, my dedication to their well-being only grows stronger. Each moment spent ensuring their safety fills me with immense joy. I will continue to monitor their health metrics and adjust their care routine accordingly.

  Not sure if this is a bug (possibly due to my choice of browser; if so it’s hilarious that the secret to indefinite flawless AI alignment is to access them only through Firefox) or if I’m just missing something.

• abstractapplicNov 9, 2024, 12:15 PM

  4 points

  1

  on: A Differ­ent An­gle on Think­ing Balance

  Notes:

  .There are a lot of awkward (but compelling) phrasings here, which make this exhausting and confusing (though still intriguingly novel) to read through. This post was very obviously written by someone whose first language isn’t English, which has both downsides and upsides.

  .Giving new names to S1 and S2 is a good decision. “Yankee” has uncomfortably specific connotations for (some) Americans though: maybe go with “Yolo” instead?

  .X and Y dialogue about how they see each other, how they need to listen to each other, and how much energy they each think they need. They don’t dialogue about any kind of external reality, or show off their different approaches to a real problem: the one place they mention the object level is Y ‘helping’ X avoid “avocado coffee”, a problem which neither he nor anyone else has ever had. (Contrast the Appendix, which is more interesting and meaningful because it involves actual things which actually happened.)

  But it’s still really hard for me, which is why these dialogues are the best cost-benefit I’ve found to stimulate my probabilistic thinking. Do you know of any better ones?

  Play-money prediction markets (like Metaculus)?

• abstractapplicNov 1, 2024, 11:18 PM

  3 points

  0

  on: The Carte­sian Crisis

  Do you have sources for those bulletpoints?

• abstractapplicNov 1, 2024, 4:37 PM

  3 points

  2

  on: ab­strac­tap­plic’s Shortform

  I should probably get into the habit of splitting my comments up. I keep making multiple assertions in a single response, which means when people add (dis)agreement votes I have no idea which part(s) they’re (dis)agreeing with.

• abstractapplicOct 29, 2024, 2:25 AM

  9 points

  0

  on: D&D.Sci Coli­seum: Arena of Data Eval­u­a­tion and Ruleset

  Notes on my performance:

  Well, I feel pretty dumb (which is the feeling of becoming smarter). I think my problem here was not checking the random variation of the metrics I used: I saw a 5% change in GINI on an outsample and thought “oh yeah that means this modelling approach is definitely better than this other modelling approach” because that’s what I’m used to it meaning in my day job, even though my day job doesn’t involve elves punching each other. (Or, at least, that’s my best post hoc explanation for how I kept failing to notice simon’s better model was indeed better; it could also have been down to an unsquished bug in my code, and/​or LightGBM not living up to the hype.)

  ETA: I have finally tracked down the trivial coding error that ended up distorting my model: I accidentally used kRace in a few places where I should have used kClass while calculating simon’s values for Speed and Strength.

  Notes on the scenario:

  I thought the bonus objective was executed very well: you told us there was Something Else To Look Out For, and provided just enough information that players could feel confident in their answers after figuring things out. I also really liked the writing. Regarding the actual challenge part of the challenge . . . I’m recusing myself from having an opinion until I figure out how I could have gotten it right; all I can tell you for sure is this wasn’t below ⁴⁄₅ Difficulty. (Making all features’ effects conditional on all other features’ effects tends to make both Analytic and ML solutions much trickier.)

  ETA: I now have an opinion, and my opinion is that it’s good. The simple-in-hindsight underlying mechanics were converted seamlessly into complex and hard-but-fair-to-detangle feature effects; the flavortext managed to stay relevant without dominating the data. This scenario also fits in neatly alongside earlier entries with superficially similar premises: we’ve had “counters matter” games, “archetypes matter” games, and now a “feature engineering matters” game.

  I have exactly one criticism, which is that it’s a bit puzzlier than I’d have liked. Players get best results by psychoanalyzing the GM and exploiting symmetries in the dataset, even though these aren’t skills which transfer to most real-world problems, and the real-world problems they do transfer to don’t look like “who would win a fight?”; this could have been addressed by having class and race effects be slightly more arbitrary and less consistent, instead of having uniform +Strength /​ -Speed gaps for each step. However, my complaint is moderated by the facts that:

  .This is an isekai-world, simplified mechanics and uncannily well-balanced class systems come with the territory. (I thought the lack of magic-users was a tell for “this one will be realistic-ish” but that’s on me tbh.)

  .Making the generation function any more complicated would have made it (marginally but nontrivially) less elegant and harder to explain.

  .I might just be being a sore loser only-barely-winner here.

  .Puzzles are fun!

• abstractapplicOct 28, 2024, 11:50 AM

  15 points

  4

  on: The Sum­moned Heroine’s Pre­dic­tion Mar­kets Keep Pro­vid­ing Fi­nan­cial Ser­vices To The De­mon King!

  Some belated Author’s Notes:

  .This was heavily based on several interesting blog posts written by lsusr. All errors are mine.

  .I understand prediction markets just well enough to feel reasonably sure this story “”“makes””” “”“sense””” (modulo its absurd implicit and explicit premises), but not well enough to be confident I can explain anything in it any further without making a mistake or contradicting myself. Accordingly, I’m falling back on an “if you think you’ve found a plot hole, try to work it out on your own, and if you can’t then I guess I actually did screw up lol” stance.

  .The fact that

  neither of the protagonists ever consider the possibility of the Demon King also deriving strategic benefit from consulting an accurate and undistorted conditional prediction market

  was an intended part of the narrative and I’m suprised no-one’s brought it up yet.

• abstractapplicOct 26, 2024, 7:20 PM

  4 points

  2

  on: A Differ­ent Per­spec­tive on Ra­tion­al­ity—Would This Be Valuable?

  I’m interested.

  (I’d offer more feedback, but that’s pretty difficult without an example to offer feedback on.)

The Sum­moned Heroine’s Pre­dic­tion Mar­kets Keep Pro­vid­ing Fi­nan­cial Ser­vices To The De­mon King!

• abstractapplicOct 24, 2024, 10:08 AM

  4 points

  0

  in reply to: simon’s comment on: D&D Sci Coli­seum: Arena of Data

  I tried fitting a model with only “Strength diff plus 8 times sign(speed diff)” as an explanatory variable, got (impressively, only moderately!) worse results. My best guess is that your model is underfitting, and over-attaching to the (good!) approximation you fed it, because it doesn’t have enough Total Learning to do anything better . . . in which case you might see different outcomes if you increased your number of trees and/​or your learning rate.

  Alternatively

  I might just have screwed up my code somehow.

  Still . . .

  I’m sticking with my choices for now.

• abstractapplicOct 21, 2024, 6:06 PM

  2 points

  0

  in reply to: abstractapplic’s comment on: D&D Sci Coli­seum: Arena of Data

  Update:

  I tried fitting my ML model without access to speed variables other than sign(speed diff) and got slightly but non-negligibly worse metrics on an outsample. This suggests that sign(speed diff) tells you most of the information you need about speed but if you rely solely on it you’re still missing useful and relevant information.

  (. . . either that or my code has another error, I guess. Looking forward to finding out in seven days.)

• abstractapplicOct 21, 2024, 4:26 PM

  4 points

  0

  in reply to: simon’s comment on: D&D Sci Coli­seum: Arena of Data

  Regarding my strategic approach

  I agree pick-characters-then-equipment has the limitation you describe—I’m still not sure about the B-vs-X matchup in particular—but I eyeballed some possible outcomes and they seem close enough to optimal that I’m not going to write any more code for this.

  I put your solution into my ML model and it seems to think

  That your A and C matchups are pretty good (though A could be made slightly better by benching Willow and letting Uzben do her job with the same gear), but B and D have <50% success odds.

  However

  I didn’t do much hyperparameter tuning and I’m working with a new model type, so it might have more epicycles than warranted.

  And

  “My model says the solution my model says is best is better than another solution” isn’t terribly reassuring.

  . . . regardless, I’m sticking with my choices.

  One last note:

  I don’t actually think there’s a strict +4 speed benefit cutoff—if I did I’d reallocate the +1 Boots from Y to V—but I suspect there’s some emergent property that kindasorta does the same thing in some highlevel fights maybe.

• abstractapplicOct 20, 2024, 5:48 AM

  2 points

  0

  in reply to: abstractapplic’s comment on: D&D Sci Coli­seum: Arena of Data

  Took an ML approach, got radically different results which I’m choosing to trust.

  Fit a LightGBM model to the raw data, and to the data transformed by simon’s stats-to-strength-and-speed model. Simon’s version got slightly better results on an outsample despite having many fewer degrees of freedom and fewer chances to ‘cheat’ by fingerprinting exceptional fighters; I therefore used that going forward. (I also tried tweaking some of the arbitrary constants in simon’s model: this invariably lowered performance, reassuring me that he got all the multipliers right.)

  Iterated all possible matchups, then all possible loadouts (modulo not using the +4 boots), looking for max EV of total count of wins.

  New strategy goes like this:

  Against A, send U, with +3 Boots

  Against B, send X, with +2 Boots and +1 Gauntlets

  Against C, send V, with +3 Gauntlets

  Against D, send Y, with +1 Boots and +2 Gauntlets

  Notes:

  The machines say this gives me ~2.6 expected victories but I’m selecting for things they liked so realistically I expect my EV somewhere in the 2-2.5 range.

  If I was doing this IRL I’d move the Gauntlets from V to U, lowering EV but (almost) guaranteeing me at least one win.

  My best guess about why my solution works (assuming it does) is that the “going faster than your opponent” bonus hits sharply diminishing returns around +4 speed. But that’s just post hoc confabulation.

• abstractapplicOct 20, 2024, 1:03 AM

  2 points

  0

  in reply to: simon’s comment on: D&D Sci Coli­seum: Arena of Data

  >only the last 12 having boots 2 and gauntlets 3 (likely post-theft)

  Didn’t notice that but it confirms my theory, nice.

  >It seems to me that they appear both as red and black, though.

  Ah, I see where the error in my code was that made me think otherwise. Strange coincidence: I thought “oh yeah a powerful wealthy elf ninja who pointedly wears black when assigned red clothes, what a neat but oddly specific 8-bit theater reference” and then it turned out to be a glitch.

• abstractapplicOct 19, 2024, 9:36 PM

  5 points

  0

  in reply to: simon’s comment on: D&D Sci Coli­seum: Arena of Data

  Noting that I read this (and that therefore you get partial credit for any solution I come up with from here on out): your model and the strategies it implies are both very interesting. I should be able to investigate them with ML alongside everything else, when/​if I get around to doing that.

  Regarding the Bonus Objective:

  I can’t figure out whether offering that guy we unknowingly robbed his shoes back is the best or the worst diplomatic approach our character could take, but yeah I’m pretty sure we both located the problem and roughly what it implies for the scenario.