An Asymptotically Optimal VCG Redistribution Mechanism for the Public Project Problem
Abstract
We study the classic public project problem, where a group of agents need to decide whether or not to build a non-excludable public project. We focus on efficient, strategy-proof, and weakly budget-balanced mechanisms (VCG redistribution mechanisms). Our aim is to maximize the worst-case efficiency ratio --- the worst-case ratio between the achieved total utility and the first-best maximum total utility. Previous studies have identified the optimal mechanism for 3 agents. It was also conjectured that the worst-case efficiency ratio approaches 1 asymptotically as the number of agents approaches infinity. Unfortunately, no optimal mechanisms have been identified for cases with more than 3 agents. We propose an asymptotically optimal mechanism, which achieves a worst-case efficiency ratio of 1, under a minor technical assumption: we assume the agents' valuations are rational numbers with bounded denominators. We also show that if the agents' valuations are drawn from identical and independent distributions, our mechanism's efficiency ratio equals 1 with probability approaching 1 asymptotically. Our results significantly improve on previous results. The best previously known asymptotic worst-case efficiency ratio is 0.102. For non-asymptotic cases, our mechanisms also achieve better ratios than all previous results.
Cite
Text
Guo. "An Asymptotically Optimal VCG Redistribution Mechanism for the Public Project Problem." International Joint Conference on Artificial Intelligence, 2019. doi:10.24963/IJCAI.2019/45Markdown
[Guo. "An Asymptotically Optimal VCG Redistribution Mechanism for the Public Project Problem." International Joint Conference on Artificial Intelligence, 2019.](https://mlanthology.org/ijcai/2019/guo2019ijcai-asymptotically/) doi:10.24963/IJCAI.2019/45BibTeX
@inproceedings{guo2019ijcai-asymptotically,
title = {{An Asymptotically Optimal VCG Redistribution Mechanism for the Public Project Problem}},
author = {Guo, Mingyu},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2019},
pages = {315-321},
doi = {10.24963/IJCAI.2019/45},
url = {https://mlanthology.org/ijcai/2019/guo2019ijcai-asymptotically/}
}