Demo Data from SIGIR 2026 / reviewers in the wild / expert
Paul E. Moraal
dblp:134/4018
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2013
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1Databases, data management, data science and information retrieval · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Computational finance and economics · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Computational finance and economics › market design
prediction markets |
0.2 | 1 | 2013 | Experience from hosting a corporate prediction market: benefits beyond the forecasts · KDD 2013 |
Computational finance and economics
market design |
0.0 | 1 | 2013 | Experience from hosting a corporate prediction market: benefits beyond the forecasts · KDD 2013 |
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2013 | Experience from hosting a corporate prediction market: benefits beyond the forecastsabstractPrediction markets are virtual stock markets used to gain insight and forecast events by leveraging the wisdom of crowds. Popularly applied in the public to cultural questions (election results, box-office returns), they have recently been applied by corporations to leverage employee knowledge and forecast answers to business questions (sales volumes, products and features, release timing). Determining whether to run a prediction market requires practical experience that is rarely described. Thomas A. Montgomery, Paul M. Stieg, Michael J. Cavaretta, Paul E. Moraal |
KDD | 4 |