| paper | d4-conf-atal-2011-0285 |
| authors | — |
| venue | ? |
| filed under | unclassified |
| judged by | gpt-6-luna / xhigh (triple__luna__xhigh__c6r1) |
| judge confidence | high |
| authors would recognise it | no |
Algorithms 1 and 2 describe distributed procedures, but no named theorem, lemma, corollary, or proposition asserts a computational result with a complexity claim or guarantee. The reported cooperation, tax, speed, and resilience findings come from simulations, so there is no eligible paper result to anchor a continuous computational question.
fails bit a — no named computational result to mirror
The paper's coalition strategies and findings on cooperation, taxes, emergence time, and leader-failure resilience are reported through algorithms and simulations; none is a named computational result eligible for mirroring.
Under the anchor rule, I can’t make a qualifying positive case. The paper has no numbered theorem, lemma, corollary, or proposition asserting a computational complexity or algorithmic result. Algorithms 1 and 2 describe coalition dynamics; the claims about cooperation, speed, taxes, and resilience are supported by simulations, not by a named complexity result.
So there is no eligible anchor to restate, and no continuous problem per anchor to hand to a prover. A continuum model of the agents might be imaginable, but attaching a complexity classification to it would go beyond what this paper establishes.
The case against a mirror is strongest at the level of what the paper actually establishes: it has no named result about the complexity of a computational problem. Algorithms 1 and 2 describe distributed coalition dynamics, and the claims about cooperation, taxes, speed, and resilience come from simulations on finite networks. There is no theorem or algorithmic analysis here whose continuous analogue would chart a complexity landscape.
A more ambitious mirror could put mass over agent states and ask whether coalition formation converges, what taxes emerge, or how the system responds to leader failure. Those are sensible questions, but in this paper they would turn the work toward continuum dynamics and mean-field analysis. To make it a computational mirror, one would need to add a formal input-output problem—say, optimizing a coalition outcome or bounding the resources needed to reach it. That would be a new contribution, not a continuization of a result the paper proves.
The network structure makes that gap harder to bridge. The reported outcomes depend on local neighborhoods, hubs, and clustering. A continuum model that preserves those features may be possible, but then its central object is a limiting network and its dynamics; the paper provides no complexity question about that object to carry over. So the proponent raised no eligible anchor, and none survives as a paper-grounded computational mirror. This argues against spending ChoCo effort on this paper; it does not show that continuous models of networked cooperation are inherently uninteresting.
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.