Quantifying machine influence over human forecasters.

ABSTRACT: Crowdsourcing human forecasts and machine learning models each show promise in predicting future geopolitical outcomes. Crowdsourcing increases accuracy by pooling knowledge, which mitigates individual errors. On the other hand, advances in machine learning have led to machine models that increase accuracy due to their ability to parameterize and adapt to changing environments. To capitalize on the unique advantages of each method, recent efforts have shown improvements by "hybridizing" forecasts-pairing human forecasters with machine models. This study analyzes the effectiveness of such a hybrid system. In a perfect world, independent reasoning by the forecasters combined with the analytic capabilities of the machine models should complement each other to arrive at an ultimately more accu

SUBMITTER: Abeliuk A 

PROVIDER: S-EPMC7524768 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature