We can model a rock either as having no preferences, but we can also model it as having arbitrary preferences--including the appropriate payout matrix for a given game--and zero ability to optimize the world to achieve them. We observe the same thing either way.
Given that we observe the same thing no matter how we model the rock, I'm not sure what that proves.
I don't know enough math and I don't know if this is important, but in the hopes that it helps someone figure something out that they otherwise might not, I'm posting it.
In Soares & Fallenstein (2015), the authors describe the following problem:
More precisely: two agents A and B must choose integers m and n with 0 ≤ m, n ≤ 10, and if m + n ≤ 10, then A receives a payoff of m dollars and B receives a payoff of n dollars, and if m + n > 10, then each agent receives a payoff of zero dollars. B has perfect predictive accuracy and A knows that B has perfect predictive accuracy.
Consider a variant of the aforementioned decision problem in which the same two agents A and B must choose integers m and n with 0 ≤ m, n ≤ 3; if m + n ≤ 3, then {A, B} receives a payoff of {m, n} dollars; if m + n > 3, then {A, B} receives a payoff of zero dollars. This variant is similar to a variant of the Prisoner's Dilemma with a slightly modified payoff matrix:
Likewise, A reasons as follows:
And B:
I figure it's good to have multiple takes on a problem if possible, and that this particular take might be especially valuable, what with all of the attention that seems to get put on the Prisoner's Dilemma and its variants.