Too Long; Didn't Read
This paper is available on arxiv under CC 4.0 license. Authors: Kiriaki Frangias, Andrew Lin, Ellen Vitercik, Manolis Zampetakis. We evaluate the impact of our algorithm on the principal’s utility compared to when she chooses not to incentivize agents. We also evaluate the efficacy of the algorithm in returning correct pairwise comparisons.
EScholar: Electronic Academic Papers for Scholars
@escholar
We publish the best academic work (that's too often lost to peer reviews & the TA's desk) to the global tech community
STORY’S CREDIBILITY
Academic Research Paper
Part of HackerNoon's growing list of open-source research papers, promoting free access to academic material.
L O A D I N G
. . . comments & more!
About Author
EScholar: Electronic Academic Papers for Scholars@escholar
We publish the best academic work (that's too often lost to peer reviews & the TA's desk) to the global tech community