| paper | d4-conf-ijcai-2022-0255 |
| authors | — |
| venue | ? |
| filed under | unclassified |
| judged by | gpt-6-luna / xhigh (triple__luna__xhigh__c6r1) |
| judge confidence | high |
| authors would recognise it | no |
The article describes computational research, but states no numbered computational result that can serve as an anchor. The FPTAS mentioned in a cited paper is not a result established here, so no continuous mirror of a result in this article survives.
fails bit a — no named computational result to mirror
The article surveys equilibrium computation, strategic self-organization, and mechanism design, but establishes no numbered computational result to mirror.
I can’t make an anchor-compliant positive case: the supplied paper has no numbered Theorem, Lemma, Corollary, or Proposition asserting a computational complexity result. It describes computational work on Resource Graph Games and cites papers on equilibrium computation, including Chan and Jiang (2018), whose title mentions an FPTAS. But this article gives no numbered result there to quote and restate.
So there are no qualifying anchors, and therefore no continuous problem per anchor to specify. A continuum model of strategic self-organization might be a plausible research direction, but without a named result in this paper to mirror, presenting it as the requested case would overstate the evidence.
The strongest negative is that this paper gives no result to anchor a mirror to. It is an overview: it says the authors studied equilibrium computation in Resource Graph Games, security investment, and self-organization, and designed approximately optimal mechanisms for facility location. But it states no numbered computational theorem, no precise complexity bound, and often not even the problem’s input and output. The cited FPTAS for Resource Graph Games is a result of a separate paper; this article names it only in the references. A continuous question based on it would be a follow-up to that source paper, not a mirror of a result established here.
The obvious rescue candidates do not fix that gap. One could ask about equilibrium computation for a continuum of player types, or mechanism design for a continuum of facility users. Both may be sensible research directions. But the overview supplies no particular equilibrium or approximation result whose continuous version can be stated and compared. The equilibrium idea also needs care: in a nonatomic model, an individual deviation may have no effect on aggregate behavior, so the strategic model itself needs specifying. That is a modeling task, not a translation licensed by a result in this paper.
So the proponent’s case has no surviving anchor. The honest limit of the negative case is that this does not establish the universal claim that no worthwhile continuous mirror exists in any scenario. The paper’s broad domains may well support useful continuous-population questions; it simply gives too little result-level detail to make one a mirror of this paper.
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.