An autocomplete model for elections

@misc{duchin2026autocomplete,
  title = {{An autocomplete model for elections}},
  author = {Moon Duchin and Kristopher Tapp},
  howpublished = {arXiv},
  year = {2026},
  month = oct,
  doi = {10.48550/arxiv.2610.33035},
  eprint = {2610.33035},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2610.33035},
  note = {CC BY 4.0},
}

TY  - UNPB
TI  - An autocomplete model for elections
AU  - Moon Duchin
AU  - Kristopher Tapp
PY  - 2026
DA  - 2026/10/14
PB  - arXiv
DO  - 10.48550/arxiv.2610.33035
UR  - https://arxiv.org/abs/2610.33035
AB  - We consider two linked problems for ranked choice elections: building a compact summary of a preference profile P, and generating synthetic profiles that resemble P in key ways. We introduce a Markov model based on which candidate names follow which on observed ballots and record this in a succession matrix on candidates together with a start/stop symbol. This provides the basis for new ``autocomplete'' methods for generating synthetic profiles, with ballot length that can be tuned to a target. We compare succession-based generation to parallel use of summaries built from positional frequencies and head-to-head comparisons. To execute this comparison, we introduce new descriptive statistics of profiles that allow new tests of whether synthetic profiles are realistic. In experiments on Scottish ranked-choice profiles, succession-based generation captures held-out ballot structure well, performs especially strongly when completing partial ballots, and often preserves election outcomes, outperforming the alternatives on all counts. Finally, we study the dynamics of iterating the train-and-generate process.
ER  -