Rank Maximal Equal Contribution: A Probabilistic Social Choice Function
Abstract
When aggregating preferences of agents via voting, two desirable goals are to incentivize agents to participate in the voting process and then identify outcomes that are Pareto efficient. We consider participation as formalized by Brandl, Brandt, and Hofbauer (2015) based on the stochastic dominance (SD) relation. We formulate a new rule called RMEC (Rank Maximal Equal Contribution) that is polynomial-time computable, ex post efficient and satisfies the strongest notion of participation. It also satisfies many other desirable fairness properties. The rule suggests a general approach to achieving very strong participation, ex post efficiency and fairness.
Cite
Text
Aziz et al. "Rank Maximal Equal Contribution: A Probabilistic Social Choice Function." AAAI Conference on Artificial Intelligence, 2018. doi:10.1609/AAAI.V32I1.11448Markdown
[Aziz et al. "Rank Maximal Equal Contribution: A Probabilistic Social Choice Function." AAAI Conference on Artificial Intelligence, 2018.](https://mlanthology.org/aaai/2018/aziz2018aaai-rank/) doi:10.1609/AAAI.V32I1.11448BibTeX
@inproceedings{aziz2018aaai-rank,
title = {{Rank Maximal Equal Contribution: A Probabilistic Social Choice Function}},
author = {Aziz, Haris and Luo, Pang and Rizkallah, Christine},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2018},
pages = {910-916},
doi = {10.1609/AAAI.V32I1.11448},
url = {https://mlanthology.org/aaai/2018/aziz2018aaai-rank/}
}