A solution to the single-question crowd wisdom problem

By Drazen Prelec, H. Seung, John McCoy. 2017

285 citations recorded by the source corpus.

Subfields: Computer Science Applications, Computer Vision and Pattern Recognition, Ocean Engineering, Evaluation & Benchmarks

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Science Applications

1. Angelica Chen
2. Philip S. Yu
3. Irina Danileiko
4. Michael D. Lee
5. Samuel R. Bowman
6. Julian Michael
7. Julie Vi
8. Ling Liu

In Computer Vision and Pattern Recognition

1. Samuel R. Bowman
2. Jason Phang
3. Philip S. Yu
4. Mohit Bansal
5. Jindong Wang
6. Nikita Nangia
7. Ari Holtzman
8. Alex Wang

In Ocean Engineering

1. Philip S. Yu
2. Irina Danileiko
3. Michael D. Lee
4. Julie Vi

In Evaluation & Benchmarks

1. Samuel R. Bowman
2. Jason Phang
3. Mohit Bansal
4. Nikita Nangia
5. Alex Wang
6. Yejin Choi
7. Angelica Chen
8. Luke S. Zettlemoyer

Citations in the corpus

• What Do NLP Researchers Believe? Results of the NLP Community Metasurvey (2022, background): “…measure which answers are more popular than expected, corresponding to the surprisingly popular answer selection rule for crowd wisdom proposed by Prelec et al. (2017), which better identifies correct answers than majority vote in the case of factual questions where the answer is only known by a…”
• Stratified Transfer Learning for Cross-domain Activity Recognition (2017, methodology): “To this end, majority voting technique is developed to exploit the knowledge from the crowd [39].”
• Testing the ability of the surprisingly popular method to predict NFL games (2018, methodology): “We thank Drazen Prelec and colleagues for supplying the raw data from Prelec et al. (2017).”