Large language models can be used to extract forecasting questions from pre‑registration documents and produce quantitative forecasts of experimental treatment effects. Those forecasts (when they are shown to be reliably better than naive baselines) could be used to triage which studies to fund, replicate, or scale, shifting scarce research and policy resources toward projects with high uncertainty and upside.
— If credible, AI‑based forecasting could reshape research funding, replication priorities, and policy reliance on new results — altering incentives across academia, funders, and regulators.
Tyler Cowen
2026.10.29
100% relevant
Eva Vivalt’s dataset of forecasts elicited from LLMs for 889 treatment effects (AEAs RCT Registry / Open Science Framework) with reported error metrics (median abs error 0.054 SD) demonstrates feasibility of this triage use case.
← Back to all ideas