Metric Distortion of Small-group Deliberation
Ashish Goel, Mohak Goyal, Kamesh Munagala

TL;DR
This paper analyzes how small-group deliberation in social choice models affects metric distortion, showing that small groups can significantly reduce distortion bounds and providing tight asymptotic bounds related to group size.
Contribution
It characterizes the distortion bounds for small-group deliberation models, demonstrating the impact of group size on reducing metric distortion in social choice.
Findings
Groups of size 3 reduce distortion below 3
Groups of size 4 break the 2.11 randomized lower bound
Asymptotic bounds show group size depends on 1/epsilon for near-optimal distortion
Abstract
We consider models for social choice where voters rank a set of choices (or alternatives) by deliberating in small groups of size at most , and these outcomes are aggregated by a social choice rule to find the winning alternative. We ground these models in the metric distortion framework, where the voters and alternatives are embedded in a latent metric space, with closer alternative being more desirable for a voter. We posit that the outcome of a small-group interaction optimally uses the voters' collective knowledge of the metric, either deterministically or probabilistically. We characterize the distortion of our deliberation models for small , showing that groups of size suffice to drive the distortion bound below the deterministic metric distortion lower bound of , and groups of size suffice to break the randomized lower bound of . We also show nearly…
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Taxonomy
TopicsSocial Media and Politics
