![](data:image/png;base64,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)
This gives (with ). Hence, according to , the blackbeards’ credence of T1 should be the same as their credence of T2, which is wrong.
A broader definition of the reference class will give the correct result. Suppose all observer-moments in Blackbeards and Whitebeards are included in the same reference class (figure 10):
![](data:image/png;base64,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)
This gives . That is, observer-moments that find that they have black beards obtain some reason to think that T1 is true.
This establishes boundaries for how the reference class can be defined. The reference class to which an observer-moment belongs consists of those and only those observer-moments that are relevantly similar to . We have just demonstrated that observer-moments can be relevantly similar even if they are subjectively distinguishable. And we saw earlier that if we reject the paradoxical recommendations in Adam & Eve, Quantum Joe and UN++ that follow from using the universal reference class definition then we also must maintain that not all observer-moments are relevantly similar. We thus have ways of testing a proposed reference class definition. On the one hand, we may not want it to be so permissive as to give counterintuitive results in Adam & Eve et al. (Scylla). On the other hand, it must not be so stringent as to make cosmological theorizing impossible because of the freak-observer problem (Charybdis). A maximally attractive reference class definition would seem to be one that steers clear of both these extremes.
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