Dynamic Proportional Rankings
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence. Main Track. Pages 261-267.
https://doi.org/10.24963/ijcai.2021/37
Keywords
- Agent-based and Multi-agent Systems: Computational Social Choice
- Agent-based and Multi-agent Systems: Voting
Abstract
Proportional ranking rules aggregate approval-style preferences of agents into a collective ranking such that groups of agents with similar preferences are adequately represented. Motivated by the application of live Q&A platforms, where submitted questions need to be ranked based on the interests of the audience, we study a dynamic extension of the proportional rankings setting. In our setting, the goal is to maintain the proportionality of a ranking when alternatives (i.e., questions)---not necessarily from the top of the ranking---get selected sequentially. We propose generalizations of well-known aggregation rules to this setting and study their monotonicity and proportionality properties. We also evaluate the performance of these rules experimentally, using realistic probabilistic assumptions on the selection procedure.
@inproceedings{ijcai2021p37,
title = {Dynamic Proportional Rankings},
author = {Israel, Jonas and Brill, Markus},
booktitle = {Proceedings of the Thirtieth International Joint Conference on
Artificial Intelligence, {IJCAI-21}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
editor = {Prof. Zhi-Hua Zhou},
pages = {261--267},
year = {2021},
month = {8},
note = {Main Track},
doi = {10.24963/ijcai.2021/37},
url = {https://doi.org/10.24963/ijcai.2021/37},
}
TY - CPAPER TI - Dynamic Proportional Rankings AU - Israel, Jonas AU - Brill, Markus T2 - Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21 PB - International Joint Conferences on Artificial Intelligence Organization PY - 2021 SP - 261 EP - 267 DO - 10.24963/ijcai.2021/37 UR - https://doi.org/10.24963/ijcai.2021/37 ER -Copied
Machine-readable versions of this paper, for tools and AI agents: