Strategic Abstention Based on Preference Extensions: Positive Results and Computer-Generated Impossibilities

Abstract

Voting rules allow multiple agents to aggregate their preferences in order to reach joint decisions. A common flaw of some voting rules, known as the no-show paradox, is that agents may obtain a more preferred outcome by abstaining from an election. We study strategic abstention for set-valued voting rules based on Kelly's and Fishburn's preference extensions. Our contribution is twofold. First, we show that, whenever there are at least five alternatives and seven agents, every Pareto-optimal majoritarian voting rule suffers from the no-show paradox with respect to Fishburn's extension. This is achieved by reducing the statement to a finite - yet very large - problem, which is encoded as a formula in propositional logic and then shown to be unsatisfiable by a SAT solver. We also provide a human-readable proof which we extracted from a minimal unsatisfiable core of the formula. Secondly, we prove that every voting rule that satisfies two natural conditions cannot be manipulated by strategic abstention with respect to Kelly's extension and give examples of well-known Pareto-optimal majoritarian voting rules that meet these requirements.

Cite

Text

Brandl et al. "Strategic Abstention Based on Preference Extensions: Positive Results and Computer-Generated Impossibilities." Journal of Artificial Intelligence Research, 2019. doi:10.1613/JAIR.1.11876

Markdown

[Brandl et al. "Strategic Abstention Based on Preference Extensions: Positive Results and Computer-Generated Impossibilities." Journal of Artificial Intelligence Research, 2019.](https://mlanthology.org/jair/2019/brandl2019jair-strategic/) doi:10.1613/JAIR.1.11876

BibTeX

@article{brandl2019jair-strategic,
  title     = {{Strategic Abstention Based on Preference Extensions: Positive Results and Computer-Generated Impossibilities}},
  author    = {Brandl, Florian and Brandt, Felix and Geist, Christian and Hofbauer, Johannes},
  journal   = {Journal of Artificial Intelligence Research},
  year      = {2019},
  pages     = {1031-1056},
  doi       = {10.1613/JAIR.1.11876},
  volume    = {66},
  url       = {https://mlanthology.org/jair/2019/brandl2019jair-strategic/}
}