AAMAS 2026
Title: Single Winner Voting on Matchings
Authors: Niclas Böhmer, Jessica Dierking
Identifying a single winner in an exponentially large candidate space is generally computationally hard. We consider a setting where candidates are feasible matchings in a given graph and voters express preferences over them. We study whether computational tractability can be regained by exploiting the matching structure of the candidate space, providing a mix of algorithmic and intractability results that reveal sharp boundaries between tractable and intractable cases.
Title: Fair Allocation with Initial Utilities
Authors: Niclas Boehmer, Luca Kreisel
Allocating resources to individuals is a central task in many applications, including social support programs and treatment distribution. Here, the goal is often to achieve equal outcomes, so resources should be prioritized to those in need. However, existing models cannot capture such disparities. We extend and adapt the fair allocation framework to account for inequalities between individuals prior to the allocation process and propose efficient algorithms to compute fair allocations.
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Title: Majoritarian Assignment Rules
Authors: Felix Brandt, Haoyuan Chen, Chris Dong, Patrick Lederer, Alexander Schlenga
A common challenge is deciding “who gets what”, i.e., how to match people with available items. This matching problem can be treated like a voting problem, which lets us draw on well-known ideas from voting theory. In more detail, we consider two popular head-to-head based voting methods and study how they perform.
Title: Strategic Behavior, Fairness, and Social Optimality in Multi-Winner Elections under Uncertainty
Authors: Janik Bürgermeister, Chris Dong
Many real-world decisions involve selecting a group of representatives based on voter preferences—for example, filling parliamentary seats after an election. A convenient ballot format, where voters simply tick the candidates they support, is approval voting. However, people’s preferences are rarely just “like” or “dislike.” In this paper, we use deep reinforcement learning to explore how voters might turn preferences based on utility functions into approval ballots.
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AAAI 2026
Title: Understanding the Impact of Proportionality in Approval-Based Multiwinner Elections
Authors: Niclas Boehmer, Lara Glessen, Jannik Peters
While proportionality axioms for approval-based multiwinner voting have been extensively studied theoretically, their practical impact on committee selection remains poorly understood. The authors address this gap through computational complexity analysis of proportionality-related problems and an extensive experimental study on real-world and synthetic elections and introduce new measures for quantifying candidate importance for achieving proportional outcomes.
Title: Reconfiguring Proportional Committees
Authors: Chris Dong, Fabian Frank, Jannik Peters, Warut Suksompong
Many real-world decisions involve selecting a group of representatives based on voter preferences—for example, filling parliamentary seats after an election. A convenient ballot format, where voters simply tick the candidates they support, is approval voting. In this paper, we ask: given two selections—an existing one and a newly elected one—can we transition smoothly from one to the other while guaranteeing proportional representation at every step?
Title: Picking a Representative Set of Solutions in Multiobjective Optimization: Axioms, Algorithms, and Experiments
Authors: Niclas Boehmer, Maximilian Wittmann
Many real-world decision-making problems involve optimizing multiple objectives simultaneously, rendering the selection of the most preferred solution a non-trivial problem: All Pareto optimal solutions are viable candidates, and it is typically up to a decision maker to select one for implementation based on their subjective preferences. To reduce the cognitive load on the decision maker, previous work has introduced the Pareto pruning problem, where the goal is to compute a fixed-size subset of Pareto optimal solutions that best represent the full set, as evaluated by a given quality measure. Reframing Pareto pruning as a multiwinner voting problem, we conduct an axiomatic analysis of existing quality measures, uncovering several unintuitive behaviors. Motivated by these findings, we introduce a new measure, directed coverage. We also analyze the computational complexity of optimizing various quality measures, identifying previously unknown boundaries between tractable and intractable cases depending on the number and structure of the objectives. Finally, we present an experimental evaluation, demonstrating that the choice of quality measure has a decisive impact on the characteristics of the selected set of solutions and that our proposed measure performs competitively or even favorably across a range of settings.