Optimization Process

• Optimization Process 6 Nov 2025 19:42 UTC

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  in reply to: Optimization Process’s comment on: Op­ti­miza­tion Pro­cess’s Shortform

  (Tongue partly-but-not-entirely in cheek: if you painstakingly prepare a second box of gas with the exact same initial conditions as the first, it will have exactly the same temperature; corollary, if you painstakingly prepare a second person with the exact same initial conditions as the first, they will have exactly the same free will.)

• Optimization Process 6 Nov 2025 19:39 UTC

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  on: Op­ti­miza­tion Pro­cess’s Shortform

  Free will is like temperature: a useful tool for analyzing the behavior of certain systems which are too big and complicated to model in exact detail.

  If you know the positions and velocities of every atom in a box of gas, with enough compute you can predict its future to arbitrary precision; does it “have a temperature”? Irrelevant! Technically yes, I guess, but it’s sorta an epiphenomenon, screened off from reality by your exact knowledge of the initial conditions and your willingness to throw compute at your model. But if you’re less-than-perfectly omniscient, it might be more convenient to consider the box as having a “temperature” and model it more abstractly.

  Substitute “person+environment”/​”free will” for “box of gas”/​”temperature” and that’s all still true.

• Optimization Process 18 Oct 2025 0:39 UTC

  4 points

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  in reply to: Steven Byrnes’s comment on: [In­tu­itive self-mod­els] 1. Preliminaries

  I see! Thanks for the thoughtful response. I think my problem is caused by not having brought enough neuroscience and psychology textbooks to my armchair, leaving me in too-many-plausible-hypotheses-land, rather than your too-few-. I’ll take another stab at this sequence if/​when I collect more background knowledge!

• Optimization Process 17 Oct 2025 22:00 UTC

  6 points

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  on: [In­tu­itive self-mod­els] 1. Preliminaries

  I’ve read about half of this sequence, and it’s certainly the most palatable, well-founded-seeming discussion of consciousness I’ve ever encountered.

  But… I’ve kind of run aground on the question: how would I tell if this is true? (Or, you know, all models are false etc., but how would I tell if this is useful?)

  Three examples of how a theory can useful: “Hey, I came up with this new theory of blurtzian phenomena! …

  1. Make predictions: ”...The literature has catalogued 347 kinds of blurtz, but under this model, there should be at least two more, with the following characteristics: [...]”

  2. Distill: ”...The literature has catalogued 351 kinds of blurtz with various complicated characteristics, but under this model, all those complicated characteristics are pretty closely retrodicted by modeling each of the (3^3 choose 2) blurtzes as being the interaction of [...]”

  3. Babble: ”...The literature has a couple different models of blurtzes, all with various open questions. Here’s one more. It’s not obviously right, but it’s another promising direction to go.”

  This sequence doesn’t feel like (1) or (2) to me. Is it (3), or something else?

• Optimization Process 31 Aug 2025 23:16 UTC

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  on: Op­ti­miza­tion Pro­cess’s Shortform

  Heuristic: distrust any claim that’s much memetically fitter than its retraction would be. (Examples: “don’t take your vitamins with {food}, because it messes with {nutrient} uptake”; “Minnesota is much more humid than prior years because of global-warming-induced corn sweat”; “sharks are older than trees”; “the Great Wall of China is visible from LEO with the naked eye”)

• Optimization Process 7 Jun 2025 20:55 UTC

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  in reply to: IlyaShpitser’s comment on: His­tograms are to CDFs as cal­ibra­tion plots are to...

  It sounds like you’re assuming you have access to some “true” probability for each event; do I misunderstand? How would I determine the “true” probability of e.g. Harris winning the 2028 US presidency? Is it ⁰⁄₁ depending on the ultimate outcome?

• Optimization Process 7 Jun 2025 1:08 UTC

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  in reply to: Optimization Process’s comment on: His­tograms are to CDFs as cal­ibra­tion plots are to...

  (Hmm. Come to think of it, if the y-axis were in logits, the error bars might be ill-defined, since “all the predictions come true” would correspond to +inf logits.)

• Optimization Process 7 Jun 2025 1:05 UTC

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  in reply to: Drake Thomas’s comment on: His­tograms are to CDFs as cal­ibra­tion plots are to...

  Ah—I took every prediction with p<0.50 and flipped ’em, so that every prediction had p>=0.50, since I liked the suggestion “to represent the symmetry of predicting likely things will happen vs unlikely things won’t.”

  Thanks for the close attention!

• Optimization Process 6 Jun 2025 7:16 UTC

  6 points

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  in reply to: Zac Hatfield-Dodds’s comment on: His­tograms are to CDFs as cal­ibra­tion plots are to...

  I like the idea, but with n>100 points a histogram seems better, and for few points it’s hard to draw conclusions. e.g., I can’t work out an interpretation of the stdev lines that I find helpful.

  Nyeeeh, I see your point. I’m a sucker for mathematical elegance, and maybe in this case the emphasis is on “sucker.”

  I’d make the starting point p=0.5, and use logits for the x-axis; that’s a more natural representation of probability to me. Optionally reflect p<0.5 about the y-axis to represent the symmetry of predicting likely things will happen vs unlikely things won’t.

  

  (same predictions from my last graph, but reflected, and logitified)

  Hmm. This unflattering illuminates a deficiency of the “cumsum(prob—actual)” plot: in this plot, most of the rise happens in the 2-7dB range, not because that’s where the predictor is most overconfident, but because that’s where most of the predictions are. A problem that a normal calibration plot wouldn’t share!

  (A somewhat sloppy normal calibration plot for those predictions:

  

  Perhaps the y-axis should be be in logits too; but I wasn’t willing to figure out how to twiddle the error bars and deal with buckets where all/​none of the predictions came true.)

• Optimization Process 6 Jun 2025 1:27 UTC

  4 points

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  in reply to: Garrett Baker’s comment on: His­tograms are to CDFs as cal­ibra­tion plots are to...

  Random numbers! Code for the last figures.

His­tograms are to CDFs as cal­ibra­tion plots are to...

Seat­tle – ACX Mee­tups Every­where Spring 2025

• Optimization Process 2 Feb 2025 19:39 UTC

  9 points

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  on: Es­cape from Alder­aan I

  That all of physics was perfectly beautiful and symmetric except for hyperspace, artificial gravity, shields and a few weapon types.

  Oh, this is genius. I love this.

• Optimization Process 19 Jan 2025 22:24 UTC

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  in reply to: gentleunwashed’s comment on: The quan­tum red pill or: They lied to you, we live in the (den­sity) matrix

  Ahhh! Yes, this is very helpful! Thanks for the explanation.

• Optimization Process 17 Jan 2025 21:43 UTC

  2 points

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  on: The quan­tum red pill or: They lied to you, we live in the (den­sity) matrix

  Question: if I’m considering an isolated system (~= “the entire universe”), you say that I can swap between state-vector-format and matrix-format via

  $$|\varphi ⟩\leftrightarrow \rho =|\varphi ⟩⟨\varphi |$$

  . But later, you say...

  If $H_S$ is uncoupled to its environment (e.g. we are studying a carefully vacuum-isolated system), then we still have to replace the old state vector picture $|\varphi ⟩\in H$ by a (possibly rank $>1$) density matrix $\rho \in {\text{Op}}_H$…

  But if $\rho :=|\varphi ⟩⟨\varphi |$, how could it ever be rank>1?

  (Perhaps more generally: what does it mean when a state is represented as a rank>1 density matrix? Or: given that the space of possible $\rho$s is much larger than the space of possible $|\varphi ⟩$s, there are sometimes (always?) multiple $\rho$s that correspond to some particular $|\varphi ⟩$; what’s the significance of choosing one versus another to represent your system’s state?)

• Optimization Process 17 Jan 2025 21:19 UTC

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  in reply to: Dmitry Vaintrob’s comment on: Quan­tum with­out complication

  That is… a very interesting and attractive way of looking at it. I’ll chew on your longer post and respond there!

Quan­tum with­out complication

• Optimization Process 1 Dec 2024 20:54 UTC

  5 points

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  on: Mag­ni­tudes: Let’s Com­pre­hend the In­com­pre­hen­si­ble!

  I have an Anki deck in which I’ve half-heartedly accumulated important quantities. Here are mine! (I keep them all as log10(value in kilogram/​meter/​second/​dollar/​whatever seems natural), to make multiplication easy.)

  Quantity	Value
  Electron mass	-30
  Electron charge	-18.8
  Gravitational constant	-10.2
  Reduced Planck constant	-34
  Black body radiation peak wavelength	-2.5
  Mass of the earth	24.8
  Moon-Earth distance	8.6
  Earth-sun distance	11.2
  log10( 1 )	0
  log10( 2 )	0.3
  log10( 3 )	0.5
  log10( 4 )	0.6
  log10( 5 )	0.7
  log10( 6 )	0.8
  log10( 7 )	0.85
  log10( 8 )	0.9
  log10( 9 )	0.95
  Boltzmann constant	-22.9
  1 amu	-26.8
  1 mi	3.2
  1 in	-1.6
  Earth radius	6.8
  1 ft	-0.5
  1 lb	-0.3
  world population	10
  US federal budget 2023	12.8
  SWE wage (per sec)	-1.4
  Seattle min wage (per sec) 2024	-2.3
  1 hr	3.6
  1 work year	6.9
  1 year	7.5
  federal min wage (per sec)	-2.7
  1 acre	3.6

• Optimization Process 30 Nov 2024 21:11 UTC

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  in reply to: James Camacho’s comment on: [bounty $100] Why are there no in­ter­est­ing (1D, 2-state) quan­tum cel­lu­lar au­tomata?

  I thank you for your effort! I am currently missing a lot of the mathematical background necessary to make that post make sense, but I will revisit it if I find myself with the motivation to learn!

• Optimization Process 30 Nov 2024 20:55 UTC

  2 points

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  in reply to: Ben’s comment on: [bounty $100] Why are there no in­ter­est­ing (1D, 2-state) quan­tum cel­lu­lar au­tomata?

  This is a good point! I’ll send you $20 if you send me your PayPal/​Venmo/​ETH/​??? handle. (In my flailings, I’d stumbled upon this “fractional step” business, but I don’t think I thought about it as hard as it deserved.)

  How are you defining “basically equivalent”

  Nyeeeh, unfortunately, sort of “I know it when I see it.” It’s kinda neat being able to take a fractional step of a classical elementary CA, but I’m dissatisfied because… ah, because the long-run behavior of the fractional-step operator is basically indistinguishable from the long-run behavior of the corresponding classical CA.

  So, tentative operationalization of “basically equivalent”: $U$ is “basically equivalent” to a classical elementary CA if the long-run behavior of $U$ is very close to the long-run behavior of some $U_{classical}$, i.e., uh,

  $$\begin{array}{c}\forall n\in N,ϵ\in R^{+}:\\ \exists N>n,M\in N,U_{cl}:\\ |U^N-U_{cl}^M|<ϵ\end{array}$$

  ...but I can already think of at least one flaw in this operationalization, so, uh, I’m not sure. (Sorry! This being so fuzzy in my head is why I’m asking for help!)