Learning How to Vote with Principles: Axiomatic Insights Into the Collective Decisions of Neural Networks

Levin Hornischer () and Zoi Terzopoulou ()
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Levin Hornischer: LMU - Ludwig Maximilian University [Munich] = Ludwig Maximilians Universität München
Zoi Terzopoulou: GATE Lyon Saint-Étienne - Groupe d'Analyse et de Théorie Economique Lyon - Saint-Etienne - UL2 - Université Lumière - Lyon 2 - UJM - Université Jean Monnet - Saint-Étienne - EM - EMLyon Business School - CNRS - Centre National de la Recherche Scientifique

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Abstract: Can neural networks be applied in voting theory, while satisfying the need for transparency in collective decisions? We propose axiomatic deep voting: a framework to build and evaluate neural networks that aggregate preferences, using the wellestablished axiomatic method of voting theory. Our findings are: (1) Neural networks, despite being highly accurate, often fail to align with the core axioms of voting rules, revealing a disconnect between mimicking outcomes and reasoning. ( 2) Training with axiom-specific data does not enhance alignment with those axioms. (3) By solely optimizing axiom satisfaction, neural networks can synthesize new voting rules that often surpass and substantially differ from existing ones. This offers insights for both fields: For AI, important concepts like bias and value-alignment are studied in a mathematically rigorous way; for voting theory, new areas of the space of voting rules are explored.

Keywords: Voting theory; Neural networks (search for similar items in EconPapers)
Date: 2025-08-05
New Economics Papers: this item is included in nep-cdm
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Published in Journal of Artificial Intelligence Research, 2025, 83 (25), ⟨10.1613/jair.1.18890⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-05395413

DOI: 10.1613/jair.1.18890

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