Better Collective Decisions via Uncertainty Reduction
Abstract
We consider an agent community wishing to decide on several binary issues by means of issue-by-issue majority voting. For each issue and each agent, one of the two options is better than the other. However, some of the agents may be confused about some of the issues, in which case they may vote for the option that is objectively worse for them. A benevolent external party wants to help the agents to make better decisions, i.e., select the majority-preferred option for as many issues as possible. This party may have one of the following tools at its disposal: (1) educating some of the agents, so as to enable them to vote correctly on all issues, (2) appointing a subset of highly competent agents to make decisions on behalf of the entire group, or (3) guiding the agents on how to delegate their votes to other agents, in a way that is consistent with the agents' opinions. For each of these tools, we study the complexity of the decision problem faced by this external party, obtaining both NP-hardness results and fixed-parameter tractability results.
Cite
Text
Alouf-Heffetz et al. "Better Collective Decisions via Uncertainty Reduction." International Joint Conference on Artificial Intelligence, 2022. doi:10.24963/IJCAI.2022/4Markdown
[Alouf-Heffetz et al. "Better Collective Decisions via Uncertainty Reduction." International Joint Conference on Artificial Intelligence, 2022.](https://mlanthology.org/ijcai/2022/aloufheffetz2022ijcai-better/) doi:10.24963/IJCAI.2022/4BibTeX
@inproceedings{aloufheffetz2022ijcai-better,
title = {{Better Collective Decisions via Uncertainty Reduction}},
author = {Alouf-Heffetz, Shiri and Bulteau, Laurent and Elkind, Edith and Talmon, Nimrod and Teh, Nicholas},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2022},
pages = {24-30},
doi = {10.24963/IJCAI.2022/4},
url = {https://mlanthology.org/ijcai/2022/aloufheffetz2022ijcai-better/}
}