Author name cluster

Jobst Heitzig

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

3 papers

2 author rows

AAAI Conference 2026 Conference Paper

The River Voting Method

Michelle Döring
Markus Brill
Jobst Heitzig

We introduce River, a novel Condorcet-consistent voting method that is based on pairwise majority margins and can be seen as a simplified variation of Tideman's Ranked Pairs method. River is simple to explain, simple to compute even "by hand," and gives rise to an easy-to-interpret certificate in the form of a directed tree. Like Ranked Pairs and Schulze's Beat Path method, River is a refinement of the Split Cycle method and shares with those many desirable properties, including independence of clones. Unlike the other three methods, River satisfies a strong form of resistance to agenda-manipulation that is known as independence of Pareto-dominated alternatives.

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ICML Conference 2024 Conference Paper

Position: Social Choice Should Guide AI Alignment in Dealing with Diverse Human Feedback

Vincent Conitzer
Rachel Freedman
Jobst Heitzig
Wesley H. Holliday
Bob M. Jacobs
Nathan O. Lambert
Milan Mossé
Eric Pacuit

Foundation models such as GPT-4 are fine-tuned to avoid unsafe or otherwise problematic behavior, such as helping to commit crimes or producing racist text. One approach to fine-tuning, called reinforcement learning from human feedback, learns from humans’ expressed preferences over multiple outputs. Another approach is constitutional AI, in which the input from humans is a list of high-level principles. But how do we deal with potentially diverging input from humans? How can we aggregate the input into consistent data about “collective” preferences or otherwise use it to make collective choices about model behavior? In this paper, we argue that the field of social choice is well positioned to address these questions, and we discuss ways forward for this agenda, drawing on discussions in a recent workshop on Social Choice for AI Ethics and Safety held in Berkeley, CA, USA in December 2023.

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AAMAS Conference 2023 Conference Paper

The Rule-Tool-User Nexus in Digital Collective Decisions

Zoi Terzopoulou
Marijn A. Keijzer
Gogulapati Sreedurga
Jobst Heitzig

Collective decision making is experiencing a digital revolution. Online platforms offer to spread information, help groups make better decisions, incentivize people to exchange arguments, and force policy makers to take into account the public opinion. Social choice theory, a sub-discipline of economics, typically analyzes collective decisions, but rather overlooks the multitude of coalescent elements playing a role in them. To ensure that digital democracy is effective and scientifically grounded, we offer an original view of collective decisions as complex systems, and propose to study the systems’ components in parallel with the interactions between them. We identify three eminent components: the individual agents in a group, i. e. , some users of a platform, the voting rule that determines the final collective decisions, and the tools via which the users practically engage with a platform. The success of digital democracy relies on interdisciplinary and cross-methodological research. We indicate several paths in this direction.

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