Identifying Expertise to Extract the Wisdom of Crowds

By David V. Budescu, Eva Chen. Management Sciences, 2015

344 citations recorded by the source corpus.

Subfields: Economics and Econometrics, General Decision Sciences, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/a61410ac6df46aed3b952b5e75699e0e3baada30

How researchers see this paper

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

In Economics and Econometrics

1. Nikolaos Kourentzes
2. Fotios Petropoulos
3. Yanfei Kang
4. George Athanasopoulos
5. Rob J Hyndman
6. Richard Bonneau
7. Feng Li
8. Xiaoqian Wang

In General Decision Sciences

1. Rob J Hyndman
2. Feng Li
3. Xiaoqian Wang
4. Drazen Prelec
5. Yanfei Kang

In Management Science and Operations Research

1. Fotios Petropoulos
2. Rob J Hyndman
3. Nikolaos Kourentzes
4. George Athanasopoulos
5. Yanfei Kang
6. Feng Li
7. Xiaoqian Wang
8. Drazen Prelec

Citations in the corpus

• Wisdom or Madness? Comparing Crowds with Expert Evaluation in Funding the Arts (2016, unknown): “Indeed, a substantial amount of research has gone into examining the best method of combining the predictions of many individuals as accurately as possible (Budescu & Chen, 2014; Larrick & Soll, 2012; Makridakis & Winkler, 1983).”
• A solution to the single-question crowd wisdom problem (2017, background): “(4) Their estimate of the percentage of art professionals and of MIT students”
• Forecasting with temporal hierarchies (2017, background): “The literature has also focused on combining forecasts from different sources, particularly from human experts with different available information (see Ashton and Ashton, 1985; Goodwin, 2000; Lamberson and Page, 2012; Budescu and Chen, 2014).”
• Forecast combinations: An over 50-year review (2022, methodology): “For interval forecasts, six heuristics have been outlined: (1) simple average, (2) median, (3) envelope, (4) interior trimming, (5) exterior trimming, and (6) probability averaging of endpoints (Park and Budescu, 2015; Gaba et al., 2017).”
• Moderating with the Mob: Evaluating the Efficacy of Real-Time Crowdsourced Fact-Checking (2021, background): “…provides evidence for the efficacy of aggregating non-expert responses in domains ranging from forecasting political and economic events (Budescu and Chen 2015; Griffiths and Tenenbaum 2006) to predicting sporting outcomes and weather trends (Herzog and Hertwig 2011; Hueffer et al.…”