as Amazon product valuations, written with knowledge of previous customer’s evaluations) is given by @CeKrKo16. They analyze student-reported learning and find that “sequential voting systems can surface better content than systems that elicit independent votes”. A related paper, which deals with sequential evaluations, is @BeSc16 and considers the possibility of only revealing the mean of prior evaluations. This brings the suggestion of varying our model so that jurors know the prior unascribed vote (such as three votes for $A$ and one for $B$) without revealing how each prior juror individually voted. This model will be for our future work.

@BGKN12 consider two problems related to our work. First they consider the use of prior voting records of jurors to evaluate what we call ability. Also, they consider decision rules other than majority verdict and present one that work well in a certain context.

Simple results on sequential juries
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As a warm-up, this section serves the purpose of giving some simple and intuitive results for optimally reliable voting orders on heterogeneous sequential juries, without resort to the full model that we provided in the preceding section. We first consider *Condorcet jurors*, who are defined as receiving binary signals (for $A$ 