Kleros (@Kleros_io) “🍿 Can you scale a movie critic? That's the question behind Kleros Foresight's f” — TopicDigg

🍿 Can you scale a movie critic? That's the question behind Kleros Foresight's first experiment. 16 movies, 1 judge: Kleros CTO @clesaege. Judge Dredd, Mamma Mia, 12 Angry Men, Barbie... all in the same pool. For each film: "If Clément watches this, what percentile score will he give it?" Slide a prediction higher or lower than the crowd. Closer to reality, you profit. Off, you lose. The twist: Clément won't watch all 16. Only 5 get evaluated. The top 3 by market estimate (the crowd literally decides what's worth watching), plus 1 random and 1 Clément's choice. The other 11 redeem at neutral. No profit, no loss. This is "distilled human judgement" in action. One person's taste is the ground truth, but invoking it (watching + rating a film) is slow and expensive. So the market predicts across all 16, only 5 get verified, and accurate predictions earn. The result: a recommendation signal that scales without the critic needing to watch everything. Movies are session 1. The same architecture works anywhere expert judgement exists but doesn't scale: property appraisals, grant allocation, content curation. Built on @SeerPM and @GnosisChain.