| paper | d4-conf-ecai-2008-0092 |
| 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 has no numbered theorem, lemma, corollary, or proposition asserting a computational result. Its protocol and strategies are evaluated experimentally, but those findings provide no qualifying result to anchor a continuous mirror.
fails bit a — no named computational result to mirror
No named computational result is covered; the paper's protocol, CV and RG strategies, and experimental comparisons remain without a qualifying mirror anchor.
This paper contains no numbered Theorem, Lemma, Corollary, or Proposition asserting a computational result such as polynomial-time solvability or NP-hardness. Its numbered items are Definitions 1 and 2; its main evidence is experimental, including comparisons of the CV, RG, and basic strategies. OCAT is cited prior work, not a computational result established here.
So there are no qualifying anchors. I can’t honestly give an anchor-specific continuous problem or predict its place in the tractability trichotomy. A large group-buying market with many buyers sharing a small set of preference types is a plausible setting for a continuous coalition-formation model, but that would be a new proposal, not a mirror anchored in this paper’s named computational results.
The proponent identifies no named computational result to mirror, and I see none in the paper. Definitions 1 and 2 define coalitions and acceptance vectors; the paper’s claims about CV and RG are experimental comparisons, not complexity results or algorithms with stated guarantees. The experiments use 30 agents, at most 200 proposals per agent, and particular synthetic preferences. There is no theorem, lemma, or other named computational result whose problem can be carried over to a distribution of types. Under the programme’s stated standard, that is the decisive objection.
The group-buying example is a plausible high-multiplicity setting, but the proposed mirror would need a new formal problem: for instance, finding or evaluating a protocol that forms coalitions among masses of buyers. The strategies in this paper depend on pairwise proposal histories and acceptance and refusal vectors for named agents. Aggregating buyers by their initial preference type loses that information; adding evolving histories to the types changes the model into a distribution over protocol states. Either choice requires designing and studying a new mechanism, rather than continuizing a result established here. Simply converting the reported number of agents who join a coalition into a fraction of the population would rescale an experimental measure, not answer the programme’s computational questions.
I cannot honestly rule out a worthwhile continuous coalition-formation project in such markets. But that would be a related new project, not a mirror of this paper’s named computational results. The case against a mirror of this paper is strong precisely because there is no qualifying result to anchor one.
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.