| paper | Collecting, Classifying, Analyzing, and Using Real-World Ranking Data |
| authors | — |
| venue | AAMAS 2023 |
| filed under | frontier · tools-data |
| judged by | gpt-5.6-terra / high (triple__gpt-5.6-terra__high__ctx2r8-rejudge1) |
| judge confidence | high |
| authors would recognise it | no |
The paper contains no numbered theorem, lemma, corollary, or proposition establishing a computational complexity or algorithmic result of its own. Its discussion of Kemeny algorithms cites prior work, while its original contributions are empirical data collection, normalization, and descriptive analysis.
fails bit a — no named computational result to mirror
No named computational result of this paper is covered; its datasets may serve as empirical infrastructure for future continuized problems.
There is no eligible anchor in this paper. It contains no numbered Theorem, Lemma, Corollary, or Proposition asserting a computational complexity or algorithmic result; its contributions are data collection and empirical measurements. The parameterized Kemeny algorithms are reported in Section 5, but are cited to Betzler et al. [1], not stated as a named result of this paper.
So I cannot honestly give the requested continuous problem “per anchor.” A distribution over ranking types would be natural for re-running this paper’s descriptive statistics—Kemeny score per unit mass, Condorcet-winner frequency, rule agreement, or distance to domains—but that is an empirical/axiomatic population description, not a new computational bribery/control/robustness question. It is outside the programme’s verdict scope.
The closest computational lead would be a high-multiplicity Kemeny-ranking problem, but it mirrors the cited Betzler et al. result, not a named result of Boehmer–Schaar. Treating it as an anchor would overclaim. Thus the strongest honest positive case is that this paper supplies potentially valuable real-world type distributions and regimes for testing future continuized problems; it does not itself furnish a named computational result to continuize.
The negative case is decisive on eligibility: this paper has no named computational result to mirror. Its own contributions are a data collection, a normalization/sampling protocol, maps and empirical measurements. It proves no complexity classification and states no algorithmic theorem, lemma, corollary, or proposition.
That defeats the proponent’s entire case, because it raises no anchors at all. The cited fixed-parameter algorithms for Kemeny are Betzler et al.’s results, not Boehmer–Schaar’s; continuizing them would be a project about that earlier paper, not this one.
A distribution over ranking types could certainly re-express the paper’s descriptive quantities—Kemeny dispersion per unit mass, Condorcet margins, rule agreement, or distance from a domain. But those are population statistics and empirical benchmarks, not the programme’s computational questions about optimizing or deciding under a continuous society. Recasting the sample as a measure therefore produces neither an eligible computational problem nor a new complexity landscape.
The paper may be useful infrastructure for ChoCo: its data could test future high-multiplicity models. That is materially different from providing a worthwhile continuous mirror of this paper.
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.