3 citations · 24 across the 52 of their papers we have counts for
61 papers
What Is Lost in Post-Training? Default Collapse and the Loss of In-Context Steerability Across Diverse Perspectives
Jessica Dierking, Itai Shapira, Niclas Boehmer
AI models serving a heterogeneous population must act on the principles appropriate to each user and context. While post-training has been shown to narrow the views large language…
Where Should Society Draw the Line? A Social Choice Approach to Collective Consent
Chris Dong, Sonja Kraiczy, Rohit Vasishta +3
Society constantly has to determine the boundaries of what it deems acceptable, from legislative decisions to the guardrails governing autonomous systems. We initiate the axiomatic…
Approval-Based Multiwinner Voting with Candidate Qualities
Niclas Boehmer, Chris Dong, Luca Kreisel +1
We initiate the study of a new model of approval-based multiwinner voting in which each candidate carries an exogenous quality score, capturing, for instance, the reliability of th…
Fractional Pareto-Optimality in Multiwinner Voting
Patrick Becker, Niclas Boehmer, Fabian Frank +1
Efficiency in multiwinner voting is most naturally captured by Pareto-optimality (PO), yet this notion is computationally and structurally difficult to handle. We therefore study f…
The End Justifies the Mean: A Linear Ranking Rule for Proportional Sequential Decisions
Carmel Baharav, Niclas Boehmer, Bailey Flanigan +1
AI alignment and participatory design motivate a new democratic design problem: how to collectively choose a decision rule to use repeatedly. We study this problem for linear ranki…
Explanation Systems for Approval-Based Multiwinner Voting
Niclas Boehmer, Luca Kreisel, Jannik Peters
In approval-based multiwinner voting, voters express approval preferences over a set of candidates, and the goal is to return a winning committee. This model captures a broad range…
AI of the People, by the People, for the People: A Social Choice Approach to Collective Control of Artificial Intelligence
Paul Anton Bachmann, Niclas Boehmer, Lukas Daniel Klausner +1
With the growing adoption of AI systems, reasoning about how society can exert control over AI becomes an increasingly urgent problem. Existing work on democratic control largely f…
Computational Social Choice: Research & Development
Dorothea Baumeister, Ratip Emin Berker, Niclas Boehmer +7
Computational social choice (COMSOC) studies principled ways to aggregate conflicting individual preferences into collective decisions. In this paper, we call for an increased effo…
Fair Allocation with Initial Utilities
Niclas Boehmer, Luca Kreisel
The problem of allocating indivisible resources to agents arises in a wide range of domains, including treatment distribution and social support programs. An important goal in algo…
Single-Winner Voting on Matchings
Niclas Boehmer, Jessica Dierking
We introduce a single-winner perspective on voting on matchings, in which voters have preferences over possible matchings in a graph, and the goal is to select a single collectivel…
Picking a Representative Set of Solutions in Multiobjective Optimization: Axioms, Algorithms, and Experiments
Niclas Boehmer, Maximilian T. 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 Pa…
Understanding the Impact of Proportionality in Approval-Based Multiwinner Elections
Niclas Boehmer, Lara Glessen, Jannik Peters
Despite extensive theoretical research on proportionality in approval-based multiwinner voting, its impact on which committees and candidates can be selected in practice remains po…
Generative Social Choice: The Next Generation
Niclas Boehmer, Sara Fish, Ariel D. Procaccia
A key task in certain democratic processes is to produce a concise slate of statements that proportionally represents the full spectrum of user opinions. This task is similar to co…
Drawing a Map of Elections
Stanisław Szufa, Niclas Boehmer, Robert Bredereck +5
Our main contribution is the introduction of the map of elections framework. A map of elections consists of three main elements: (1) a dataset of elections (i.e., collections of or…
On Sequential Fault-Intolerant Process Planning
Andrzej Kaczmarczyk, Davin Choo, Niclas Boehmer +2
We propose and study a planning problem we call Sequential Fault-Intolerant Process Planning (SFIPP). SFIPP captures a reward structure common in many sequential multi-stage decisi…
Finite-Horizon Single-Pull Restless Bandits: An Efficient Index Policy For Scarce Resource Allocation
Guojun Xiong, Haichuan Wang, Yuqi Pan +4
Restless multi-armed bandits (RMABs) have been highly successful in optimizing sequential resource allocation across many domains. However, in many practical settings with highly s…
Towards Foundation-model-based Multiagent System to Accelerate AI for Social Impact
Yunfan Zhao, Niclas Boehmer, Aparna Taneja +1
AI for social impact (AI4SI) offers significant potential for addressing complex societal challenges in areas such as public health, agriculture, education, conservation, and publi…
Optimizing Vital Sign Monitoring in Resource-Constrained Maternal Care: An RL-Based Restless Bandit Approach
Niclas Boehmer, Yunfan Zhao, Guojun Xiong +5
Maternal mortality remains a significant global public health challenge. One promising approach to reducing maternal deaths occurring during facility-based childbirth is through ea…
Multiwinner Temporal Voting with Aversion to Change
Valentin Zech, Niclas Boehmer, Edith Elkind +1
We study two-stage committee elections where voters have dynamic preferences over candidates; at each stage, a committee is chosen under a given voting rule. We are interested in i…
Worst- and Average-Case Robustness of Stable Matchings: (Counting) Complexity and Experiments
Kimon Boehmer, Niclas Boehmer
Focusing on the bipartite Stable Marriage problem, we investigate different robustness measures related to stable matchings. We analyze the computational complexity of computing th…
Balancing Act: Prioritization Strategies for LLM-Designed Restless Bandit Rewards
Shresth Verma, Niclas Boehmer, Lingkai Kong +1
LLMs are increasingly used to design reward functions based on human preferences in Reinforcement Learning (RL). We focus on LLM-designed rewards for Restless Multi-Armed Bandits,…
Escape Sensing Games: Detection-vs-Evasion in Security Applications
Niclas Boehmer, Minbiao Han, Haifeng Xu +1
Traditional game-theoretic research for security applications primarily focuses on the allocation of external protection resources to defend targets. This work puts forward the stu…
Preliminary Study of the Impact of AI-Based Interventions on Health and Behavioral Outcomes in Maternal Health Programs
Arpan Dasgupta, Niclas Boehmer, Neha Madhiwalla +4
Automated voice calls are an effective method of delivering maternal and child health information to mothers in underserved communities. One method to fight dwindling listenership…
Evaluating the Effectiveness of Index-Based Treatment Allocation
Niclas Boehmer, Yash Nair, Sanket Shah +3
When resources are scarce, an allocation policy is needed to decide who receives a resource. This problem occurs, for instance, when allocating scarce medical resources and is ofte…