# Evaluating the truth and the experts simultaneously

When evaluating an artwork, the guilt of a suspect or the quality of theoretical research, the usual procedure is to gather the opinions of a number of people and take some weighted average of these. There is no objective measure of the truth or the quality of the work. What weights should be assigned to different people’s opinions? Who should be counted an expert or knowledgeable witness?
A circular problem appears: the accurate witnesses are those who are close to the truth, and the truth is close to the average claim of the accurate witnesses. This can be modelled as a set of signals with unknown precision. Suppose the signals are normally distributed with mean equal to the truth (witnesses unbiased, just have poor memories). If the precisions were known, then these could be used as weights in the weighted average of the witness opinions, which would be an unbiased estimate of the truth with minimal variance. If the truth were known, then the distance of the opinion of a witness from it would measure the accuracy of that witness. But both precisions and the truth are unknown.
Simultaneously determining the precisions of the signals and the estimate of the truth may have many solutions. If there are two witnesses with different claims, we could assign the first witness infinite precision and the second finite, and estimate the truth to equal the opinion of the first witness. The truth is derived from the witnesses and the precisions are derived from the truth, so this is consistent. The same applies with witnesses switched.
A better solution takes a broader view and simultaneously estimates witness precisions and the truth. These form a vector of random variables. Put a prior probability distribution on this vector and use Bayes’ rule to update this distribution in response to the signals (the witness opinions).
The solution of course depends on the chosen prior. If one witness is assumed infinitely precise and the others finitely, then the updating rule keeps the infinite and finite precisions and estimates the truth to equal the opinion of the infinitely precise witness. The assumption of the prior seems unavoidable. At least it makes clear why the multiple solutions arise.