IJCAI 2022poster36 citations

I Will Have Order! Optimizing Orders for Fair Reviewer Assignment

Justin Payan, Yair Zick

Abstract

We study mechanisms that allocate reviewers to papers in a fair and efficient manner. We model reviewer assignment as an instance of a fair allocation problem, presenting an extension of the classic round-robin mechanism, called Reviewer Round Robin (RRR). Round-robin mechanisms are a standard tool to ensure envy-free up to one item (EF1) allocations. However, fairness often comes at the cost of decreased efficiency. To overcome this challenge, we carefully select an approximately optimal round-robin order. Applying a relaxation of submodularity, γ-weak submodularity, we show that greedily inserting papers into an order yields a (1+γ²)-approximation to the maximum welfare attainable by our round-robin mechanism under any order. Our Greedy Reviewer Round Robin (GRRR) approach outputs highly efficient EF1 allocations for three real conference datasets, offering comparable performance to state-of-the-art paper assignment methods in fairness, efficiency, and runtime, while providing the only EF1 guarantee.

Agent-based and Multi-agent Systems: Resource AllocationAgent-based and Multi-agent Systems: ApplicationsAgent-based and Multi-agent Systems: Computational Social ChoiceSearch: Combinatorial Search and Optimisation
BibTeX
@inproceedings{ijcai2022p63,
  title     = {I Will Have Order! Optimizing Orders for Fair Reviewer Assignment},
  author    = {Payan, Justin and Zick, Yair},
  booktitle = {Proceedings of the Thirty-First International Joint Conference on
               Artificial Intelligence, {IJCAI-22}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {Lud De Raedt},
  pages     = {440--446},
  year      = {2022},
  month     = {7},
  note      = {Main Track},
  doi       = {10.24963/ijcai.2022/63},
  url       = {https://doi.org/10.24963/ijcai.2022/63},
}
I Will Have Order! Optimizing Orders for Fair Reviewer Assignment · IJCAI 2022