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abramdemski
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11abramdemski's Shortform
Ω
5y
Ω
68
Pointing at Normativity
Implications of Logical Induction
Partial Agency
Alternate Alignment Ideas
Filtered Evidence, Filtered Arguments
CDT=EDT?
Embedded Agency
Hufflepuff Cynicism
Punctuation & Quotation Conventions
abramdemski17h20

Does that include a dislike of """scare quotes"""?

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Turing-Complete vs Turing-Universal
abramdemski2d30

Reply here.

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Turing-Complete vs Turing-Universal
abramdemski2d20

Reply here.

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Consciousness as a Distributed Ponzi Scheme
abramdemski4d20

My reply to this went long, so I made it into a post.

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Consciousness as a Distributed Ponzi Scheme
abramdemski5d20

I'm hoping to reply more thoroughly later, but here's a quick one: I am curious how you would further clarify the distinction between terminal goals vs instrumental goals. Naively, a terminal goal is one which does not require justification. However, you appear to be marking this way of speaking as a "category error". Could you spell out your preferred system in more detail? Personally, I find the distinction between terminal and instrumental goals to be tenable but unnecessary; details of my way of thinking are in my post An Orthodox Case Against Utility Functions.

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Condensation
abramdemski6dΩ330
  1. Sam's theory gives some evidence for  broad commonality between the data-structures different minds would use to represent the same problem (because it proves this happens under some conditions).
  2. Sam's theory gives a framework for proving useful things about these structures.
  3. Sam's theory of interpretability comes from a kind of usefulness, so there's at least some hope here.
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Condensation
abramdemski6dΩ220

The optimal condensation is not (typically) 1 book per question. Instead, it typically recovers the meaningful latents which you'd want to write down to model the problem. Really, the right thing to do is to work examples to get an intuition for what happens. Sam does some of this in his paper.

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Condensation
abramdemski6dΩ330

Fixed!

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Intentionality
abramdemski8d20

On the model I mentioned, it would (in part) be a function of fit between explicit goals and implicit goals.

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Geometric UDT
abramdemski9dΩ220

Perhaps I'm still not understanding you, but here is my current interpretation of what you are saying:

  • The (expected utility) argument that it is valuable for us to get the ASI to entangle its values with ours relies on the assumption of non-nosy-ness.
    • That is: since we are uncertain which values are ours, but whichever thing we value, we're just as happy to impose that thing on versions of ourselves which do not value that thing, we don't see any increase in expected value from Geometric UDT.

I see this line of reasoning as insisting on taking max-expected-utility according to your explicit model of your values (including your value uncertainty), even when you have an option which you can prove is higher expected utility according to your true values (whatever they are).

My argument has a somewhat frequentist flavor: I'm postulating true values (similar to postulating a true population frequency), and then looking for guarantees with respect to them (somewhat similar to looking for an unbiased estimator). Perhaps that is why you're finding it so counter-intuitive?

The crux of the issue seems to be whether we should always maximize our explicit estimate of expected utility, vs taking actions which we know are better with respect to our true values despite not knowing which values those are. One way to justify the latter would be via Knightian value uncertainty (ie infrabayesian value uncertainty), although that hasn't been the argument I've been trying to make. I'm wondering if a more thoroughly geometric-rationality perspective would provide another sort of justification.

But the argument I'm trying to make here is closer to just: but you know Geometric UDT is better according to your true values, whatever they are!

== earlier draft reply for more context on my thinking ==

Perhaps I'm just not understanding your argument here, and you need to spell it out in more detail? My current interpretation is that you are interpreting "care about both worlds equally" as "care about rainbows and puppies equally" rather than "if I care about rainbows, then I equally want more rainbows in the (real) rainbow-world and the (counterfactual) puppy-world; if I care about puppies, then I equally want more puppies in the (real) puppy-world and the (counterfactual) rainbow-world."

A value hypothesis is a nosy neighbor if[1] it wants the same things for you whether it is your true values or not. So what's being asserted here (your "third if" as I'm understanding it) is that we are confident we've got that kind of relationship with ourselves -- we don't want "our values to be satisfied, whatever they are" -- rather, whatever our values are, we want them to be satisfied across universes, even in counterfactual universes where we have different values.

Maximizing rainbows maximizes the expected value given our value uncertainty, but it is a catastrophe in the case that we are indeed puppy-loving. Moreover, it is an avoidable catastrophe; ...

... and now I think I see your point?

The idea that it is valuable for us to get the ASI to entangle its values with ours relies on an assumption of non-nosyness. 

There is a different way to justify this assumption, 

  1. ^

    (but not "only if"; there are other ways to be a nosy neighbor)

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19Punctuation & Quotation Conventions
18h
10
25Lambda Calculus Prior
2d
3
20Questioning Computationalism
2d
4
29Turing-Complete vs Turing-Universal
3d
5
26Teleosemantics & Swampman
4d
5
33Consciousness as a Distributed Ponzi Scheme
6d
9
133Condensation
Ω
7d
Ω
13
38Myopia Mythology
Ω
8d
Ω
3
48Comparing Payor & Löb
Ω
8d
Ω
1
15Liberation Clippy
9d
2
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Timeless Decision Theory
9 months ago
(+1874/-8)
Updateless Decision Theory
a year ago
(+1886/-205)
Updateless Decision Theory
a year ago
(+6406/-2176)
Problem of Old Evidence
a year ago
(+4678/-10)
Problem of Old Evidence
a year ago
(+3397/-24)
Good Regulator Theorems
2 years ago
(+239)
Commitment Races
3 years ago
Agent Simulates Predictor
3 years ago
Distributional Shifts
3 years ago
Distributional Shifts
3 years ago
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