Lennart Heim is an associate information scientist at RAND whose research focuses on the computational resources required to support the development of advanced AI. Before joining RAND, he was part of an elite group of "pro" forecasters recognized for accurate track records. The RAND Forecasting Initiative asked Lennart to share insights on how he achieved top-tier forecaster status to inform our approach toward recruiting and training forecasters across RAND to participate in our new platform (RFI).
Q: How did you get started with crowdsourced forecasting and how did you get so good at it?
A: It started with my interest in developing evidence-based policy recommendations. Someone told me about Philip Tetlock and the superforecasters. I realized that crowdsourced forecasting was one tool in the evidence-based policy research toolkit. I went to a calibration workshop—which helps forecasters correct bias and sampling error, for example— and understood better how to apply the method toward finding good answers. We worked through questions like "how many cows are there on the planet?" A well-calibrated group will get pretty close to the right answer. Then I started forecasting with CSET-Foretell (which became INFER-Pub and is now RFI). CSET-Foretell was looking for forecasting ambassadors so in that role I met with a group of people every two weeks to work on developing forecasts together.