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Using meta-predictions to identify experts in the crowd when past performance is unknown.
Marcellin Martinie1, Tom Wilkening2, Piers D L Howe1
1Melbourne School of Psychological Sciences, The University of Melbourne, Parkville, Victoria, Australia.
This study introduces a new method for improving probabilistic forecasts by using meta-predictions, outperforming existing approaches. It effectively leverages hidden expertise when traditional expert identification is not possible.
Area of Science:
- Decision Science
- Computational Social Science
- Behavioral Economics
Background:
- Improving probabilistic forecasts is crucial for decision-making.
- Leveraging expert forecasts requires identifying forecaster performance on past questions.
- Identifying expertise is challenging when historical performance data is unavailable.
Purpose of the Study:
- To propose a novel algorithm for aggregating probabilistic forecasts.
- To utilize forecasters' meta-predictions about others' predictions.
- To address limitations in identifying and leveraging expertise in forecasting.
Main Methods:
- Developed a novel algorithm for probabilistic forecast aggregation.
- Incorporated forecasters' meta-predictions into the aggregation process.
- Tested an extremised version of the algorithm against existing methods.
Main Results:
- The proposed algorithm significantly outperformed current forecasting approaches.
- Performance was evaluated on 500 diverse binary decision problems.
- The algorithm demonstrated superior accuracy in leveraging latent expertise.
Conclusions:
- Meta-predictions offer a powerful tool for enhancing probabilistic forecasting.
- The novel algorithm effectively identifies and utilizes hidden expertise.
- This approach is valuable in scenarios where traditional expertise identification is infeasible.
