Hybrid Forecasting Competition (HFC)
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The HFC program sought to develop and test hybrid geopolitical forecasting systems. These systems integrated 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 developed systems that integrated human and machine forecasting contributions in novel ways. These systems competed in a multi-year competition to identify approaches that may enable the Intelligence Community to radically improve the accuracy and timeliness of geopolitical forecasts.
- Human judgment
- Machine learning
- Decision making
- Human/machine interfaces
- Text analysis
To access ACE program-related publications, please enter the following into a Google Scholar search query: "2017-17061500006 OR 2017-17072100002 OR 2017-17071900005"