| paper | d4-conf-aaai-2014-0095 |
| authors | — |
| venue | ? |
| filed under | unclassified |
| judged by | gpt-6-luna / xhigh (triple__luna__xhigh__c6r1) |
| judge confidence | high |
| authors would recognise it | no |
The paper’s numbered results characterize voting rules through axioms; none states a computational result. The conclusion’s mention of STV manipulation hardness is not a numbered result proved in this paper, so it cannot anchor the required computational mirror.
fails bit a — no named computational result to mirror
No computational result is covered; Theorems 1–3, Lemma 1, and Proposition 1 give axiomatic characterizations or properties.
This paper has no eligible named computational anchor for a continuous mirror. Its Theorems 1–3 characterize voting rules by axioms; Lemma 1 characterizes runoff scoring rules; and Proposition 1 establishes axiomatic properties of nested runoff voting. None asserts a complexity result or gives a computational formulation the paper analyzes.
The conclusion mentions that STV is NP-hard to manipulate by a single manipulator, but that claim is not a numbered result proved here. It cannot serve as the required anchor. A continuous electorate of ranking types could be defined, but asking whether voting rules on that population satisfy analogous axioms would continuize an axiomatic question, which the programme places out of scope.
So the honest positive case is empty under the stated criteria: there is no result here to mirror as a computational problem.
The proponent is right that Theorems 1–3 are axiomatic, and that the paper does not prove a numbered complexity result. But the conclusion explicitly identifies STV’s NP-hardness of manipulation as a computational fact and a reason the rule matters. Theorem 1 then singles out STV as the unique clone-independent runoff scoring rule. Together, those passages point to a computational question about a rule this paper puts at its center.
A natural continuous question is: given a rational distribution over ranking types, what is the minimum mass and cost of preference changes needed to make a target candidate a possible STV winner under the paper’s parallel-universes tie-breaking? This keeps the population as the continuous object and turns the single-manipulator question into a mass-coalition question. That is sensible for large, anonymous electorates: a type can include the ranking and the cost of changing it, and voters sharing a type are interchangeable.
The question has real computational structure. For a fixed elimination order, STV’s round-by-round score conditions are linear in the resulting type masses; optimizing over mass transfers across possible orders creates an algorithmic problem involving many candidate sequences and potentially many output types. Its answer could be easy, hard, or parameterized by the number of alternatives and supported types. Any of those answers would be useful; none needs to preserve the discrete hardness result.
The weakness is that this is an indirect anchor. The continuous problem is a computational follow-on to Theorem 1’s identification of STV and to the conclusion’s manipulation claim, rather than a mirror of a complexity theorem proved in the paper. If the grader requires a numbered computational theorem as the only eligible anchor, the proponent’s case wins on that formal criterion. But the paper’s lack of such a theorem does not make every worthwhile continuous computational question empty.
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.