A forecast is only useful if you know how much to trust it. Models now produce predictions at scale, and almost none of them arrive with confidence you can take at face value. Laplace is our attempt at closing that gap.
Two problems stand in the way, and we work on both: aggregating resolved forecasting data across markets, history, and expert judgment, and understanding the internal mechanisms that produce calibrated belief.
The two tighten each other. Better data sharpens calibration, and understanding why a model is calibrated tells us which data to collect next.
If this works, the result is not simply better forecasts. It is that judgment can be spent on choosing which questions matter, rather than on second-guessing the answers.
Laplace Research was co-founded by Arya Somu, Chief Scientist, and Bruce Nshuti Hirwa, Chief Technology Officer. Reach us at arya@laplaceresearch.org and bruce@laplaceresearch.org.