Markets whose resolution depends on on-chain or automatically verifiable data, needing no human oracle.
Cluster: Oracle & Resolution
Markets whose resolution depends on on-chain or automatically verifiable data, needing no human oracle.
Referenced in 4 articles
Seva Gunitsky argues that prediction markets create three distinct dangers for global conflict: military insider trading at scale, manipulation of outcome resolution, and propaganda advantages for autocratic regimes. The article walks through real cases including the Israeli Air Force betting scandal and the 'hair dryer problem' — cases that challenge the idealistic framing of prediction markets as truth machines.
Launch post for sdk.markets, a toolkit for creating parimutuel prediction markets on arbitrary questions, built on Base. Argues CLOB infrastructure does not fit thin community markets and that parimutuel pools are simpler and fairer when there is no natural counterparty. Details design choices that address parimutuel's classic 'wait and see' sniping problem (short answer windows, snapshot locking, DPM-style pricing) and three resolution modes: single admin, multi-admin consensus, and an AI oracle that resolves from arbitrary URLs.
Explains the SKC (Srinivasan, Karger, Chen) mechanism for prediction markets on unverifiable outcomes. Markets resolve using crowd consensus as the outcome, with delta-based scoring rewarding participants for moving markets toward final consensus. Enables markets for subjective questions lacking ground truth.
Proposes a mechanism for prediction markets where outcomes cannot be objectively verified. Uses the last reporter's prediction as a reference point, creating incentives for truthful reporting through negative cross-entropy payments. Proves truthful reporting is a perfect Bayesian equilibrium.