| paper | d4-conf-ecai-2025-0174 |
| authors | — |
| venue | ? |
| filed under | unclassified |
| judged by | gpt-6-luna / xhigh (triple__luna__xhigh__c6r1) |
| judge confidence | high |
| authors would recognise it | no |
The named results characterize participation, NOA, and rule parameters; none asserts an algorithm, computational guarantee, or complexity classification. A cohort-level continuum deviation would change the unit and property, and cannot provide a mirror for a computational result absent from the paper.
fails bit a — no named computational result to mirror
No named result is covered: Proposition 1 and Theorems 2, 3, 7, 8, and 9 characterize incentive properties or rule parameters, rather than computational guarantees or complexity results.
I can’t make a paper-anchored positive case under your anchor rule: the paper contains no named computational result asserting a problem is in P, NP-hard, FPT, or otherwise classifying its complexity.
Its numbered results are characterizations of incentive properties: Proposition 1 shows that unanimity implies NOA; Theorem 2 characterizes participation geometrically for deterministic peaks-only rules; Theorems 3 and 7 characterize participation for OWAs; and Theorems 8 and 9 do so for WAs. These are proved here, but none is a computational-complexity result. The conclusion mentions approximability as future work, not as a result. So there are no qualifying anchors and no anchor-based continuous problems to hand to a prover.
A continuous population of residents grouped by ideal location could be a plausible future model. But the paper’s outcome space is already continuous, \([0,1]\); that alone does not continuize the population. And making an individual infinitesimal voter’s participation affect an aggregate outcome requires additional modeling choices. Without a named computational result in the paper, that possibility can’t support the requested positive case.
The proponent raised no qualifying anchors: they correctly identify that the paper proves no named computational result. Proposition 1 and Theorems 2, 3, 7, 8, and 9 characterize incentive properties or rule parameters. None classifies the complexity of a computational problem, gives an algorithm or running time, or proves a hardness result. The future-work suggestion about approximability is not a result to mirror.
The best continuous version of the paper’s central question still does not supply one. The paper asks whether an individual voter benefits from participating, holding the other voters’ reports fixed. In an atomless population, one individual has zero mass, so their participation does not change an aggregate outcome; that version of the condition is vacuous. One could instead ask whether a positive-mass cohort benefits from joining, or characterize participation for an integral-based averaging rule. Those are coherent population-level questions, but they change the unit of deviation and the property being studied. As framed here, they remain axiomatic characterizations, not computational questions.
A distribution of residents’ ideal locations is a plausible setting for such new work. The continuous outcome space \([0,1]\) does not itself make the population continuous, and the paper keeps voters individually identifiable in its participation condition. The proposed setting therefore establishes that a continuous-population model could be sensible; it does not provide a computational result from this paper to mirror. Turning its future approximability direction into an algorithmic problem would be a new project, not a continuous counterpart of a result already proved here.
This is a strong negative case for a paper-anchored ChoCo mirror, though not a proof that every related continuous research question would be unworthwhile.
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.