Research

Ought is somewhere between a research lab and a startup.

We do conceptual and empirical research on supporting deliberation using machine learning, and share our findings openly.

This research is guided by a vision for tools and applications that we hope will eventually help millions of people think through the questions and choices they face every day.

We're incorporated as a non-profit, so we can afford to take the long view.

Projects

Our focus is on mechanisms for training ML algorithms to answer questions in cases where it's difficult or impossible to get empirical feedback on the quality of the answers.

Can we integrate model-based and judgmental forecasting so that we get the best of both worlds, automated forecasts that are sensitive to qualitative arguments?
Can we solve difficult problems by assembling small and mostly context-free contributions from individual agents who don't know the big picture?