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Paul M. Stieg

dblp:134/3998 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 2016
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Databases, data management, data science and information retrieval · 2Artificial intelligence and machine learning · 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

TopicWeightPapersLastEvidence papers
Computational finance and economics › market design
prediction markets
0.212013
Experience from hosting a corporate prediction market: benefits beyond the forecasts · KDD 2013
Computational finance and economics
market design
0.012013
Experience from hosting a corporate prediction market: benefits beyond the forecasts · KDD 2013
YearPublicationVenuePosition
2016 Feedback or Research: Separating Pre-purchase from Post-purchase Consumer Reviews
Alexander Kotov 0001, Aravind Mohan, Shiyong Lu, Paul M. Stieg
ECIR5
2013 Experience from hosting a corporate prediction market: benefits beyond the forecasts
abstract
Prediction 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
KDD2