| paper | d4-journals-geb-2020-0092 |
| authors | — |
| venue | ? |
| filed under | unclassified |
| judged by | gpt-6-luna / xhigh (triple__luna__xhigh__c6r1) |
| judge confidence | high |
| authors would recognise it | unclear |
No named result asserts an algorithm, complexity bound, reduction, or computational formulation used to compute. The matching and voting propositions characterize supported stable sets, while the existence proposition gives no computational claim; a mass-voting computation would be a new problem.
fails bit a — no named computational result to mirror
No result receives a computational mirror; the propositions characterize stable sets in finite matching and voting models, and the existence result is non-computational.
There is no qualifying computational anchor in this paper, so I can’t make an anchor-based positive case under the stated rules. It contains no numbered result claiming that a problem is in P, NP-hard, FPT, or otherwise classifying its computational complexity; it gives no algorithm or reduction.
The closest results are Propositions 4.8 and 4.9, which characterize singleton farsighted stable sets in one-to-one matching, and Propositions 5.3 and 5.4, which characterize them in voting games. They are proved here, but they are economic characterizations, not complexity results. Proposition 3.13’s existence result is even less suitable: it establishes existence by making every alternative absorbing.
One could imagine a continuous electorate with masses of voters sharing preference types and a mass-based voting rule. But turning that into a computational question would be a new problem, not a mirror anchored in a named computational result of this paper. The paper’s characterizations may be useful background for such a project; they do not supply the required anchor.
The negative case is unusually clean: this paper has no computational result to continuize. Propositions 4.8 and 4.9 characterize which matchings can be supported as singleton farsighted stable sets; Propositions 5.2–5.4 do the same for voting alternatives. They give economic characterizations, not algorithms, complexity bounds, or reductions. Proposition 3.13 is an existence result obtained by making every alternative absorbing.
A continuous voting society with masses of voters sharing preference types is easy to imagine. But asking how to compute its stable sets would add a computational problem the paper does not pose. The matching results could likewise inspire a new algorithmic study, but that would be a new agenda, not a mirror of a computational claim in this paper. The proponent’s closest candidates therefore do not supply qualifying anchors. The case is strong within ChoCo’s stated scope; it does not rule out a worthwhile continuous-population study under a broader agenda.
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.