2020 AAAI AAAI 2020

Can We Predict the Election Outcome from Sampled Votes?

Abstract

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.

The Questioner
🌉 Interdisciplinary Bridge — Artificial Intelligence and Machine Learning and Mathematics & Optimization
🧭 Keyword Pioneer — preference sampling
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Data Science & Analytics, Deep Learning, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Security & Privacy