Jordan Despanie is a technology and security policy fellow at RAND with interests spanning biosecurity, artificial intelligence, open-source intelligence, and foreign policy. Before joining RAND, he was part of an elite group of professional crowdsourced forecasters recognized for accurate track records. The RAND Forecasting Initiative asked Jordan to share insights on how he achieved top-tier forecaster status to inform its approach toward recruiting and training forecasters across RAND.

Q: How did you get started with crowdsourced forecasting and how did you get so good at it?

A: My forecasting odyssey began with PredictIt, a prediction market where up to $800 can be deposited to buy or sell contracts revolving around domestic and international outcomes. Although small in scale, this tangible capital investment had the net effect of aligning the level of risk I'd willingly take on, especially if my preferred outcome proved wrong, with my confidence in the research (e.g., polling data, news flow) I was able to uncover.

Following the announcement of PredictIt's legal challenges by the CFTC, however, I sought out crowdsourced forecasting platforms that had questions focused on international relations, given my longstanding passion for foreign policy issues across the Middle East and Southeast and East Asia which never had the opportunity to be expressed through my academic training as a life scientist.

This led me to INFER Public (now RFI) in November 2022, which I saw as a superb platform for achieving various aims: exercising and expanding my knowledge of foreign policy in an evidence-based manner; practicing policy writing by formulating rationales with a government stakeholder audience in mind; and building rapport with other forecasters to stress test my own thinking via dialectical dialogues.

>> Read the full interview on RAND's blog