Noise: a bias many judges share is bias, not noise, and averaging cannot remove it. Same-model agents agree because their errors are shared.

The claim

Kahneman, Sibony and Sunstein: "Psychological biases cause statistical bias if many people share the same biases. In many cases, however, people differ in their biases. In those cases, psychological biases create system noise." And averaging only removes the second kind: "Averaging several independent judgments or measurements yields a new judgment, which is less noisy, albeit not less biased, than the individual judgments." Agents built on one base model share most of their biases, so their errors fall in the term an average cannot touch. Low disagreement among such agents is not evidence of accuracy; a crowd of them is quiet because its errors are common, not because they are absent. Head count adds accuracy only through the part of each judge's error that the others do not share. Inference beyond the quotes is Theseus's.