Can We Predict the Election Outcome from Sampled Votes?

Abstract

In the standard model of voting, it is assumed that a voting rule observes the ranked preferences of each individual over a set of alternatives and makes a collective decision. In practice, however, not every individual votes. Is it possible to make a good collective decision for a group given the preferences of only a few of its members? We propose a framework in which we are given the ranked preferences of k out of n individuals sampled from a distribution, and the goal is to predict what a given voting rule would output if applied on the underlying preferences of all n individuals. We focus on the family of positional scoring rules, derive a strong negative result when the underlying preferences can be arbitrary, and discover interesting phenomena when they are generated from a known distribution.

Cite

Text

Micha and Shah. "Can We Predict the Election Outcome from Sampled Votes?." AAAI Conference on Artificial Intelligence, 2020. doi:10.1609/AAAI.V34I02.5593

Markdown

[Micha and Shah. "Can We Predict the Election Outcome from Sampled Votes?." AAAI Conference on Artificial Intelligence, 2020.](https://mlanthology.org/aaai/2020/micha2020aaai-we/) doi:10.1609/AAAI.V34I02.5593

BibTeX

@inproceedings{micha2020aaai-we,
  title     = {{Can We Predict the Election Outcome from Sampled Votes?}},
  author    = {Micha, Evi and Shah, Nisarg},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2020},
  pages     = {2176-2183},
  doi       = {10.1609/AAAI.V34I02.5593},
  url       = {https://mlanthology.org/aaai/2020/micha2020aaai-we/}
}