mfb comments on Sneaky Strategies for TDT - Less Wrong
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Here are the variants which make no explicit mention of TDT anywhere in the problem statement. It seems a real strain to describe either of them as unfair to TDT. Yet TDT will be outperformed on them by CDT; unless it resolves never to allow itself to be outperformed on any problem (in TDT über alles fashion)
Problem 1: Omega (who experience has shown is always truthful) presents the usual two boxes A and B and announces the following. "Before you entered the room, I selected an agent at random from the following distribution over all full source-codes for decision theory agents (insert distribution). I then simulated the result of presenting this exact problem to that agent. I won't tell you what the agent decided, but I will tell you that if the agent two-boxed then I put nothing in Box B, whereas if the agent one-boxed then I put big Value-B in Box B. Regardless of how the simulated agent decided, I put small Value-A in Box A. Now please choose your box or boxes."
Problem 2: Our ever-reliable Omega now presents ten boxes, numbered from 1 to 10, and announces the following. "Exactly one of these boxes contains $1 million; the others contain nothing. You must take exactly one box to win the money; if you try to take more than one, then you won't be allowed to keep any winnings. Before you entered the room, I ran multiple simulations of this problem as presented to different agents, sampled uniformly from different possible future universes according to their relative numbers, with the universes themselves sampled from my best projections of the future. I determined the box which the agents were least likely to take. If there were several such boxes tied for equal-lowest probability, then I just selected one of them, the one labelled with the smallest number. I then placed $1 million in the selected box. Please choose your box."
Assuming that the space of possible agents is large enough: For each individual version of a TDT agent, the best way is to two-box. The advantage of TDT is the possibility to improve the expected value for a whole range of agents (including cooperation with other TDT agents in the prisoners dilemma). CDT agents happen to profit from that, and they profit even more than TDT agents. Does TDT maximize the expected value for the whole distribution of agents? In that case, it is still optimal in that respect.
Problem 2 is sensitive to changes of arbitrary size: Assume that the space of TDT agents takes one box with probability 10%+epsilon and some other with 10%-epsilon. While the expectation value is the same within O(epsilon), the money is now in some other box and CDT would have to calculate that with the same precision. Apart from the experimental issue, I think this gives some theoretical challenges as well.