| paper | d4-conf-ecai-2016-0057 |
| authors | — |
| venue | ? |
| filed under | unclassified |
| judged by | gpt-6-luna / xhigh (triple__luna__xhigh__c6r1) |
| judge confidence | high |
| authors would recognise it | yes |
The paper offers a natural continuous version of its social-acceptance stage, but it states no numbered computational result to anchor that mirror. The Schulze implementation and example testing are described, while the cited complexity comparison is external; under the stated criterion, bit (a) fails.
fails bit a — no named computational result to mirror
The natural mirror covers only winner determination in the social-acceptance stage; it does not cover moral-acceptability measurement, fact validation, or the platform prototype as a whole.
MOOD has a plausible high-multiplicity setting: a large online deliberation may have many participants but relatively few distinct preference rankings over a small set of shortlisted alternatives. A natural continuous input would be a finite support of rankings \(t\), each with rational population mass \(\mu_t\). For each pair of alternatives \(a,b\), compute the mass preferring \(a\) to \(b\), then apply the Schulze method to return the winner or winners. This is a reasonable continuous formulation of the paper’s social-acceptance stage.
But it is not an anchored complexity case. The paper contains no numbered Theorem, Lemma, Corollary, or Proposition asserting that a problem is in P, NP-hard, FPT, or otherwise classifying its complexity. It describes implementing Schulze voting in a prototype; it does not prove a computational result about it. The cited strategic-behavior comparison in reference [19] is an external result, not a result of this paper.
So the best honest positive case is that the paper’s setting and voting component admit a natural continuous-population formulation. There is no qualifying anchor to mirror, and no basis here for claiming that discrete hardness dissolves—or that the continuous problem is hard. The weakness is decisive for the requested comparison: the proposed winner-determination question has no named computational result in the paper behind it.
The only concrete mirror the proponent raises is social acceptance under the Schulze method. A continuous version is quite natural: represent each preference ranking over the shortlisted alternatives by a type \(t\) with mass \(\mu_t\), compute weighted pairwise support, and apply the strongest-path rule. A large platform with many participants and relatively few rankings is a plausible high-multiplicity setting, so the objection is not that this model is incoherent.
But it is not a result of this paper. The authors describe selecting a Schulze implementation, using a PHP library, and testing it on an example. They make no claim about the computational complexity of winner determination, and prove no algorithmic result for the continuous version. The proposed mass model is a reasonable new formulation of a component in their prototype; it does not continuize a computational finding in the article.
The other promising candidates are weaker as mirrors. Fact validation and expert panels are proposed features, not completed computational methods; the panel procedure is partly unimplemented, and the vote threshold is explicitly provisional. A continuous complexity problem about those mechanisms would first require specifying the mechanism and its computational task. That would be new model-building, not a mirror of a result here.
So the negative case is strong on the question the proponent actually faced: no computational result in the paper supplies an anchor, and the Schulze formulation does not create one. It is weaker as a literal claim that no worthwhile continuous question could ever be inspired by MOOD. One could define new robustness or intervention problems around the platform. But those would be research questions built from its design, not continuous mirrors of its results.
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.