One can model Incubator using even if we assume that there are two subjectively distinguishable states that the blackbearded observer might be in after learning about his beard color. In order to do that, one has to expand our representation of the problem by considering a more fine-grained partition of the possibilities involved. To be concrete, let us suppose that the blackbearded observer might or might not experience a pain in his little toe during the stage where he knows he has a black beard. If he knew that this pain would occur only if the coin fell Tails (say) then the problem would be trivial; so let’s suppose that he doesn’t have know of any correlation between having the pain and the outcome of the coin toss. We then have four possible worlds to consider (figure 7):
![](data:image/png;base64,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)
The possible worlds w1-w4 represent the following possibilities:
w1: Heads and the late blackbeard has no little-toe pain.
w2: Heads and the late blackbeard has a little-toe pain.
w3: Tails and the late blackbeard has a little-toe pain.
w4: Tails and the late blackbeard has no little-toe pain.
We can assume that the observer-moments share the prior P(wi) = 1/4 (for i = 1,2,3,4). Let h be the hypothesis that the coin fell heads, and e the information available to an observer-moment that knows it has a black beard and pain in the little toe. By , the reference class for such an observer-moment is
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