ICML 2025

Computing Voting Rules with Improvement Feedback
Evi Micha 1 Vasilis Varsamis 1
Abstract
Aggregating preferences under incomplete or con-
strained feedback is a fundamental problem in
social choice and related domains. While prior
work has established strong impossibility results
for pairwise comparisons, this paper extends the
inquiry to improvement feedback, where vot-
ers express incremental adjustments rather than
complete preferences. We provide a complete
characterization of the positional scoring rules
that can be computed given improvement feed-
back. Interestingly, while plurality is learnable un-
der improvement feedback—unlike with pairwise
feedback—strong impossibility results persist for
many other positional scoring rules. Furthermore,
we show that improvement feedback, unlike pair-
wise feedback, does not suffice for the computa-
tion of any Condorcet-consistent rule. We comple-
ment our theoretical findings with experimental
results, providing further insights into the prac-
tical implications of improvement feedback for
preference aggregation.
1. Introduction
Classical social choice theory assumes that voters provide
complete rankings of all candidates, which are then aggre-
gated by a voting rule to select a winner (Brandt et al., 2016).
However, this assumption becomes impractical in many real-
world scenarios involving a large number of candidates, as
voters may be unable or unwilling to rank all of them. For
example, in deliberation platforms like Polis (Small et al.,
2021), users provide pairwise comparisons over a limited
subset of opinions rather than full rankings. Similarly, in Re-
inforcement Learning from Human Feedback (RLHF)—a
methodology widely used to fine-tune large language mod-
els (LLMs)—feedback often takes the form of comparison
1Thomas Lord Department of Computer Science, University
of Southern California, Los Angeles, California, USA. Correspon-
dence to: Evi Micha <evi.micha@usc.edu>, Vasilis Varsamis
<varsamis@usc.edu>.
Proc...