# Wei_Dai comments on A problem with Timeless Decision Theory (TDT) - Less Wrong

36 04 February 2010 06:47PM

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Comment author: 05 February 2010 11:44:38AM *  2 points [-]

I don't see why this is outside of UDT's domain. It seems straightforward to model and solve the decision problem in UDT1. Here's the world program:

``````def P(color):
outcome = "die"
if Omega_Predict(S, "you're wrong") == color:
if S("") == color:
outcome = "live"
else:
if S("you're wrong") == color:
outcome = "live"
``````

Assuming a preference to maximize the occurrence of outcome="live" averaged over P("green") and P("red"), UDT1 would conclude that the optimal S returns a constant, either "green" or "red", and do that.

BTW, do you find this "world program" style analysis useful? I don't want to over-do them and get people annoyed. (I refrained from doing this for the problem described in Gary's post, since it doesn't mention UDT at all, and therefore I'm assuming you want to find a TDT-only solution.)

Comment author: 05 February 2010 03:07:55PM 2 points [-]

(I refrained from doing this for the problem described in Gary's post, since it doesn't mention UDT at all, and therefore I'm assuming you want to find a TDT-only solution.)

Yes, I was focusing on a specific difficulty in TDT, But I certainly have no objection to bringing UDT into the thread too. (I myself haven't yet gotten around to giving UDT the attention I think it deserves.)

Comment author: 05 February 2010 06:01:21PM *  0 points [-]

The world program I would use to model this scenario is:

``````def P(color):
if Omega_Predict(S, "you're wrong") == color:
outcome = "die"
else:
outcome = "live"
``````

The else branch seems unreachable, given color = S("your'e wrong) and the usual assumptions about Omega.

I don't understand what your nested if statements are modeling.

Comment author: 05 February 2010 07:18:28PM 1 point [-]

I was modeling what Eliezer wrote in the comment that I was responding to:

"Simulate telling the human that they got the answer wrong. If in this case they get the answer wrong, actually tell them that they get the answer wrong. Otherwise say nothing."

BTW, if you add a tab in front of each line of your program listing, it will get formatted correctly.

Comment author: 05 February 2010 07:28:46PM 1 point [-]

Ah, I see. Then it seems that you are really solving the problem of minimizing the probability that Omega presents this problem in the first place.

What about the scenario, where Omega uses the strategy: Simulate telling the human that they got the answer wrong. Define the resulting answer as wrong, and the other as right.

This is what I modeled.

BTW, if you add a tab in front of each line of your program listing, it will get formatted correctly.

Thanks. Is there an easier way to get a tab into the comment input box than copy paste from an outside editor?

Comment author: 05 February 2010 07:51:05PM *  1 point [-]

What about the scenario, where Omega uses the strategy: Simulate telling the human that they got the answer wrong. Define the resulting answer as wrong, and the other as right.

In that case it should be modeled like this:

``````def P(color):
wrong_color = Omega_Predict(S, "you're wrong")
if S("you're wrong") == wrong_color:
outcome = "die"
else:
outcome = "live"
``````

Thanks. Is there an easier way to get a tab into the comment input box than copy paste from an outside editor?

Not that I'm aware of.

Comment author: 05 February 2010 07:58:32PM 2 points [-]

Is there an easier way to get a tab into the comment input box than copy paste from an outside editor?

Not that I'm aware of.

Are you guys talking about getting code to indent properly? You can do that by typing four spaces in front of each line. Each quadruple of spaces produces a further indentation.

http://daringfireball.net/projects/markdown/syntax#precode

Comment author: 06 February 2010 07:06:19AM *  2 points [-]

Are you guys talking about getting code to indent properly? You can do that by typing four spaces in front of each line.

Spaces? Think of the wasted negentropy! I say we make tab the official Less Wrong indention symbol, and kick out anyone who disagrees. Who's with me? :-)

Comment author: 05 February 2010 08:06:54PM 0 points [-]

Hm, I think the difference in our model programs indicates something that I don't understand about UDT, like a wrong assumption that justified an optimization. But it seems they both produce the same result for P(S("you're wrong")), which is outcome="die" for all S.

Do you agree that this problem is, and should remain, unsolvable? (I understand "should remain unsolvable" to mean that any supposed solution must represent some sort of confusion about the problem.)

Comment author: 05 February 2010 08:23:23PM 0 points [-]

The input to P is supposed to contain the physical randomness in the problem, so P(S("you're wrong")) doesn't make sense to me. The idea is that both P("green") and P("red") get run, and we can think of them as different universes in a multiverse. Actually in this case I should have wrote "def P():" since there is no random correct color.

wrong assumption that justified an optimization

I'm not quite sure what you mean here, but in general I suggest just translating the decision problem directly into a world program without trying to optimize it.

Do you agree that this problem is, and should remain, unsolvable? (I understand "should remain unsolvable" to mean that any supposed solution must represent some sort of confusion about the problem.)

No, like I said, it seems pretty straightforward to solve in UDT. It's just that even in the optimal solution you still die.

Comment author: 05 February 2010 09:18:14PM 0 points [-]

The input to P is supposed to contain the physical randomness in the problem, so P(S("you're wrong")) doesn't make sense to me. The idea is that both P("green") and P("red") get run, and we can think of them as different universes in a multiverse. Actually in this case I should have wrote "def P():" since there is no random correct color.

Ok, now I understood why you wrote your program the way you did.

It's just that even in the optimal solution you still die.

By solve, I meant find a way to win. I think that after getting past different word use, we agree on the nature of the problem.