Manipulative Elicitation -- A New Attack on Elections with Incomplete Preferences

TL;DR

This paper reveals a new vulnerability in election manipulation called manipulative elicitation, where partial preference elicitation can be exploited to alter election outcomes, and shows that computing such manipulations is often polynomial-time solvable.

Contribution

It introduces manipulative elicitation as a novel attack in partial preferences and analyzes its computational complexity across various voting rules, highlighting a fundamental vulnerability.

Findings

Manipulative elicitation can change election winners using partial preferences.

Computationally, manipulative elicitation is polynomial-time solvable for many voting rules.

Adding minimum comparison constraints makes manipulative elicitation NP-complete.

Abstract

Lu and Boutilier proposed a novel approach based on "minimax regret" to use classical score based voting rules in the setting where preferences can be any partial (instead of complete) orders over the set of alternatives. We show here that such an approach is vulnerable to a new kind of manipulation which was not present in the classical (where preferences are complete orders) world of voting. We call this attack "manipulative elicitation." More specifically, it may be possible to (partially) elicit the preferences of the agents in a way that makes some distinguished alternative win the election who may not be a winner if we elicit every preference completely. More alarmingly, we show that the related computational task is polynomial time solvable for a large class of voting rules which includes all scoring rules, maximin, Copeland$^\alpha$ for every $\alpha\in[0,1]$, simplified Bucklin voting rules, etc. We then show that introducing a parameter per pair of alternatives which specifies the minimum number of partial preferences where this pair of alternatives must be comparable makes the related computational task of manipulative elicitation \NPC for all common voting rules including a class of scoring rules which includes the plurality, $k$-approval, $k$-veto, veto, and Borda voting rules, maximin, Copeland$^\alpha$ for every $\alpha\in[0,1]$, and simplified Bucklin voting rules. Hence, in this work, we discover a fundamental vulnerability in using minimax regret based approach in partial preferential setting and propose a novel way to tackle it.