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.
CITATION STYLE
Micha, E., & Shah, N. (2020). Can we predict the election outcome from sampled votes? In AAAI 2020 - 34th AAAI Conference on Artificial Intelligence (pp. 2176–2183). AAAI press. https://doi.org/10.1609/aaai.v34i02.5593
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