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Lara Glessen

dblp:421/6816 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2026
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Theoretical computer science
1 paper
Algorithmic game theory and mechanism design · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Algorithmic game theory and mechanism design › social choice › computational social choice › multiwinner voting
approval-based committee voting
1.012026
Understanding the Impact of Proportionality in Approval-Based Multiwinner Elections · AAAI 2026
Algorithmic game theory and mechanism design › social choice › computational social choice
multiwinner voting
1.012026
Understanding the Impact of Proportionality in Approval-Based Multiwinner Elections · AAAI 2026
Algorithmic game theory and mechanism design
social choice
1.012026
Understanding the Impact of Proportionality in Approval-Based Multiwinner Elections · AAAI 2026
Algorithmic game theory and mechanism design › social choice › computational social choice
computational complexity of voting rules
0.312026
Understanding the Impact of Proportionality in Approval-Based Multiwinner Elections · AAAI 2026

Methods — techniques the papers use, named apart from their topics

measures of candidate importance · 1.0experimental study · 1.0
YearPublicationVenuePosition
2026 Understanding the Impact of Proportionality in Approval-Based Multiwinner Elections
abstract
Despite extensive theoretical research on proportionality in approval-based multiwinner voting, its impact on which committees and candidates can be selected in practice remains poorly understood. We address this gap by (i) analyzing the computational complexity of several natural problems related to the behavior of proportionality axioms, and (ii) conducting an extensive experimental study on both real-world and synthetic elections. Our findings reveal substantial variation in the restrictiveness of proportionality across instances, including previously unobserved high levels of restrictiveness in some real-world cases. We also introduce and evaluate new measures for quantifying a candidate’s importance for achieving proportional outcomes, which differ clearly from assessing candidate strength by approval score.
Niclas Boehmer, Lara Glessen, Jannik Peters 0001
AAAI2