58 research outputs found
Computational Aspects of Multi-Winner Approval Voting
We study computational aspects of three prominent voting rules that use
approval ballots to elect multiple winners. These rules are satisfaction
approval voting, proportional approval voting, and reweighted approval voting.
We first show that computing the winner for proportional approval voting is
NP-hard, closing a long standing open problem. As none of the rules are
strategyproof, even for dichotomous preferences, we study various strategic
aspects of the rules. In particular, we examine the computational complexity of
computing a best response for both a single agent and a group of agents. In
many settings, we show that it is NP-hard for an agent or agents to compute how
best to vote given a fixed set of approval ballots from the other agents
A Framework for Approval-based Budgeting Methods
We define and study a general framework for approval-based budgeting methods
and compare certain methods within this framework by their axiomatic and
computational properties. Furthermore, we visualize their behavior on certain
Euclidean distributions and analyze them experimentally
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