HCBE Faculty Articles

ORCID

0000-0002-8158-4647, 0000-0003-1883-3169, 0000-0001-9613-0317, 0000-0003-0136-2145, 0000-0001-8138-367X, 0000-0002-5411-923X, 0000-0002-6561-6186,

Document Type

Article

Publication Title

AI Magazine

ISSN

0738-4602

Publication Date

3-29-2023

Abstract/Excerpt

Sound decision-making relies on accurate prediction for tangible outcomes ranging from military conflict to disease outbreaks. To improve crowdsourced forecasting accuracy, we developed SAGE, a hybrid forecasting system that combines human and machine generated forecasts. The system provides a platform where users can interact with machine models and thus anchor their judgments on an objective benchmark. The system also aggregates human and machine forecasts weighting both for propinquity and based on assessed skill while adjusting for overconfidence. We present results from the Hybrid Forecasting Competition (HFC)—larger than comparable forecasting tournaments—including 1085 users forecasting 398 real-world forecasting problems over 8 months. Our main result is that the hybrid system generated more accurate forecasts compared to a human-only baseline, which had no machine generated predictions. We found that skilled forecasters who had access to machine-generated forecasts outperformed those who only viewed historical data. We also demonstrated the inclusion of machine-generated forecasts in our aggregation algorithms improved performance, both in terms of accuracy and scalability. This suggests that hybrid forecasting systems, which potentially require fewer human resources, can be a viable approach for maintaining a competitive level of accuracy over a larger number of forecasting questions.

DOI

10.1002/aaai.12085

Volume

44

First Page

112

Last Page

128

Comments

ACKNOWLEDGMENTS

The authors would like to thank Seth Goldstein, Peter Haglich, Rob Hartman, Daniel Horn, and Steven Rieber for their helpful feedback during the HFC program. This research is based upon work supported in part by the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA), via 2017-17071900005. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of ODNI, IARPA, or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for governmental purposes notwithstanding any copyright annotation therein.

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Download
Peer Reviewed

Share

COinS