| paper | On the Distortion of Single Winner Elections with Aligned Candidates |
| authors | — |
| venue | AAMAS 2023 |
| filed under | voting · distortion |
| judged by | gpt-5.6-terra / high (triple__gpt-5.6-terra__high__ctx2r8-rejudge1) |
| judge confidence | high |
| authors would recognise it | no |
The supplied paper contains no numbered theorem, lemma, corollary, or proposition asserting a computational-complexity result. Its unnumbered distortion guarantees do not satisfy the required computational anchor criterion, so no paper-grounded mirror can pass bit (a).
fails bit a — no named computational result to mirror
The supplied text contains only unnumbered distortion and performance claims; no qualifying named computational result is covered.
This three-page JAAMAS-track text contains no numbered Theorem, Lemma, Corollary, or Proposition, and no named computational-complexity result (P/NP-hardness/FPT/etc.) to anchor a continuization case as required. Its asserted tight distortion bounds and majority-based algorithms are presented only as unnumbered narrative claims; it explicitly directs readers to the separate full version, reference [15], for details.
So I cannot honestly supply an anchor of the stipulated form or quote a result number “as printed.” On the supplied paper alone, the requested affirmative, anchor-based case is therefore not available.
The negative case succeeds on the stipulated object: this supplied text contains no named computational result to continuize. It has no numbered theorem, lemma, corollary, or proposition, and does not assert a complexity classification or formulate an optimization problem over an explicitly encoded electorate. Its contributions are unnumbered distortion characterizations and tight performance guarantees for a fixed, simple majority-based rule; the proofs and formal statements are deferred to the separate full version.
Accordingly, there are no affirmative anchors to defeat: the proponent correctly identifies none. More importantly, importing a continuous society here would not mirror a named result of this paper. One could define a distribution over ordinal rankings, or even over latent spatial locations, and ask for worst-case distortion under aligned candidates. But that would be a new distributional/robust metric-voting model, not a continuization of an identified computational problem in this text. The paper’s population is already used only through aggregate social cost and majority support, while its real uncertainty is the unobserved cardinal geometry; replacing voters by mass does not turn its stated claims into the programme’s target—complexity of bribery, control, robustness, or an exponentially represented optimization problem.
There may well be a worthwhile continuous-population research question adjacent to metric distortion. But on the supplied paper, that is an externally invented direction, not a paper-grounded continuous mirror. Under the required anchor-based standard, this paper should not be greenlit.
The adversarial triple: the proponent anchors on up to three named results; the opponent sees that case and must defeat every anchor; the judge decides which case convinced it. These are the pipeline’s own outputs, generated by tools/triple_run.py — no human edited them. The paper’s own text is not reproduced here beyond the quoted statement above.