The mathematics, hands on
Two interactive demos of the rating models behind QIW ranking
QIW does more than answer questions — it puts candidates into a defensible order. Two Bayesian rating models do that work: instead of a score, each candidate carries a distribution — an estimate and how unsure we are about it. That is hard to explain and easy to show. These two demos show it: verdicts arrive year by year, the distributions move and narrow, and the ordering settles — or does not.
Watch the 20-minute story“From Elo to Evidence” — the film behind these demos (20 min).Both pages run on invented data. Publication counts, dossiers, the “true” quality and the judgements are all simulated; the drug targets are there as a recognisable example, and what the pages say about them is not a literature review. Only the mathematics is the same: those equations run in QIW over real dossiers assembled from sourced evidence and over verdicts from real LLM judges.
The judge sees two dossiers and names the stronger one. Every verdict moves both distributions — an uncertain candidate a long way, a well-evidenced one barely at all. It also shows why a duel between two apparent equals is the most informative call you can buy.
The judge gets K dossiers at once and orders them. One call therefore buys K(K−1)/2 pairwise constraints — far cheaper, but each dossier is touched less often. The K slider makes that trade immediately visible.
Both demos come in English, German, French and Spanish — switcher at the top right.
Further reading
The equations follow Weng & Lin (2011), extended with a precision that grows with the evidence available. The base model is checked numerically against openskill.py. The formulas are on the demo pages themselves, with the citation.