Contests for Predictive Algorithms: Ensembling, Interdependency, and Optimal Rewards Design

Simple item page
dc.contributor.authorAhsen, Eren
dc.contributor.authorAyvaci, Mehmet
dc.contributor.authorRaghunathan, Srinivasan
dc.contributor.authorSubramanyam, Ramanath
dc.date.accessioned2023-12-26T18:49:07Z
dc.date.available2023-12-26T18:49:07Z
dc.date.issued2024-01-03
dc.identifier.doi10.24251/HICSS.2024.742
dc.identifier.isbn978-0-9981331-7-1
dc.identifier.othere08ab5fe-89a0-47fa-b6db-f1b11177bc8f
dc.identifier.urihttps://hdl.handle.net/10125/107127
dc.language.isoeng
dc.relation.ispartofProceedings of the 57th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectDigital Innovation, Transformation, and Entrepreneurship
dc.subjectalgorithms
dc.subjectartifical intelligence
dc.subjectcompetition design
dc.subjectcrowdsourcing contests
dc.subjectensemble
dc.titleContests for Predictive Algorithms: Ensembling, Interdependency, and Optimal Rewards Design
dc.typeConference Paper
dc.type.dcmiText
dcterms.abstractWith the increased availability of data and widespread use of AI/ML technologies, firms are increasingly using crowdsourcing contests to acquire predictive algorithms for business use. A unique feature of these contests is that the designer can create an ensemble of submitted algorithms and improve upon the predictive accuracy (or quality) of individual algorithms. Given this departure from conventional contests, we ask how the optimal rewarding schemes should be in the presence of ensembling and interdependent algorithms. To answer this question, we develop a stylistic model of a contest for a predictive algorithm with two participants. As opposed to the single best contribution typically sought out in conventional contests and the winner-takes-all reward scheme, we find that both the winner and the runner-up could receive rewards in contests for algorithms, depending on whether the ensembled algorithms substitute or complement each other as determined by the algorithm interdependency. We show that the degree of substitution or complementarity can fundamentally alter the structure of the optimal reward scheme. We highlight our results using a real-world algorithm contest for predicting breast cancer using digital mammograms, we demonstrate the practical applicability of our framework.
dcterms.extent10 pages
prism.startingpage6171

Files

Original bundle

Now showing 1 - 1 of 1
Thumbnail Image
Name:
0604.pdf
Size:
379.84 KB
Format:
Adobe Portable Document Format
Download

Collections

Digital Innovation, Transformation, and Entrepreneurship