Followup to: What Evidence Filtered Evidence?

In "What Evidence Filtered Evidence?", we are asked to consider a scenario involving a coin that is either biased to land Heads 2/3rds of the time, or Tails 2/3rds of the time. Observing Heads is 1 bit of evidence for the coin being Heads-biased (because the Heads-biased coin lands Heads with probability 2/3, the Tails-biased coin does so with probability 1/3, the likelihood ratio of these is $\frac{2 / 3}{1 / 3} = 2$ , and ${log}_{2} 2 = 1$ ), and analogously and respectively for Tails.

If such a coin is flipped ten times by someone who doesn't make literally false statements, who then reports that the 4th, 6th, and 9th flips came up Heads, then the update to our beliefs about the coin depends on what algorithm the not-lying^[1] reporter used to decide to report those flips in particular. If they always report the 4th, 6th, and 9th flips independently of the flip outcomes—if there's no evidential entanglement between the flip outcomes and the choice of which flips get reported—then reported flip-outcomes can be treated the same as flips you observed yourself: three Headses is 3 * 1 = 3 bits of evidence in favor of the hypothesis that the coin is Heads-biased. (So if we were initially 50:50 on the question of which way the coin is biased, our posterior odds after collecting 3 bits of evidence for a Heads-biased coin would be $2^{3} : 1$ = 8:1, or a probability of 8/(1 + 8) ≈ 0.89 that the coin is Heads-biased.)

On the other hand, if the reporter mentions only and exactly the flips that came out Heads, then we can infer that the other 7 flips came out Tails (if they didn't, the reporter would have mentioned them), giving us posterior odds of $2^{3} : 2^{7}$ = 1:16, or a probability of around 0.06 that the coin is Heads-biased.

So far, so standard. (You did read the Sequences, right??) What I'd like to emphasize about this scenario today, however, is that while a Bayesian reasoner who knows the non-lying reporter's algorithm of what flips to report will never be misled by the selective reporting of flips, a Bayesian with mistaken beliefs about the reporter's decision algorithm can be misled quite badly: compare the 0.89 and 0.06 probabilities we just derived given the same reported outcomes, but different assumptions about the reporting algorithm.

If the coin gets flipped a sufficiently large number of times, a reporter whom you trust to be impartial (but isn't), ca...