A Hybrid Prediction Market Design for Scientific Replication Forecasting

Abstract

Scientists increasingly worry that many high-profile experiments cannot be reproduced—a “replication crisis” that erodes confidence and drains research funds. Prediction markets can harness collective insight about which results will replicate, but conventional designs suffer from thin liquidity, slow payouts, and limited expert engagement. We introduce a hybrid replication market that blends three automated-market-maker (AMM) models, reputation-based staking, and staged payouts to solve these problems. Universities or grant agencies that sponsor a market can choose from three built-in pricing rules that keep the odds meaningful even when only a few people are trading. Thus, our new and innovative design will offer funders a practical triage tool for replication efforts and help researchers gauge the robustness of new findings—all within a market that is both liquid and intellectually rewarding.