The HFC program is developing and testing hybrid geopolitical forecasting systems. These systems integrate human and machine forecasting components to create maximally accurate, flexible, and scalable forecasting capabilities.
Human-generated forecasts may be subject to cognitive biases and/or scalability limits. Machine-generated (i.e., statistical, computational) forecasting approaches may be more scalable and data-driven, but are often ill-suited to render forecasts for idiosyncratic or newly emerging geopolitical issues. Hybrid approaches hold promise for combining the strengths of these two approaches while mitigating their individual weaknesses.
Performers are developing systems that integrate human and machine forecasting contributions in novel ways. These systems are competing in a multi-year competition to identify approaches that may enable the Intelligence Community (IC) to radically improve the accuracy and timeliness of geopolitical forecasts.
The "IC" began exploring crowdsourced forecasting in the mid-2000’s to enhance existing methods of analytic judgment.
A research effort called Aggregate Contingent Estimation (ACE) was created out of the U.S. Intelligence Community's advanced research group, IARPA, to test if crowds with access to publicly available news under various conditions could be accurate making predictions.
The positive results from ACE led to the creation of multiple projects to try and better predict the future using various crowdsourcing methods.
HFC is launched to try and combine human and AI-based predictions in new ways to make even more accurate predictions.