I think we can go a step further and say that the basic Bayes treatment shown is mathematically incorrect. It assumes conditional independence and equal accurac

I think we can go a step further and say that the basic Bayes treatment shown is mathematically incorrect. It assumes conditional independence and equal accuracy between judges, which is almost certainly not the case here. If a full 10% of people who observe a car say that it is not blue, I strongly doubt their evaluations are independent. Rather, I would guess that most of them are making the same assessment, like "my culture doesn't distinguish blue and green" or "I am a person who does not consider cyan to be blue". So simply calculating odds based on 1000 conditionally-independent assessments isn't a valid approach. Less formally: I expect a car with 900 votes for 'blue' to be a different color than a car with 999 votes for 'blue'. Is each car blue in the binary sense we're talking about? Well, for that we'd need an objective standard of blue, which the problem quietly failed to set.