| paper | d4-conf-atal-2019-0424 |
| 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 contains no numbered theorem, lemma, corollary, or proposition asserting an algorithmic or complexity result. Its experiments, behavioral models, parameter tuning, and placement simulations are computational analyses, but a continuous placement or consensus model would be a new problem rather than a mirror of a named result.
fails bit a — no named computational result to mirror
No named computational result is available to mirror; the paper reports experimental findings, learned behavior models, and simulation analyses, including parameter tuning and node placement.
I can’t make an anchor based case for a continuous mirror: this paper contains no numbered Theorem, Lemma, Corollary, or Proposition asserting a computational problem is in a complexity class or is hard.
The closest computational material is the simulation work in Sections 5.2–5.3, which tunes behavior model parameters and studies greedy placement of trusted and adversarial nodes. Those are reported as simulation analyses, not as named complexity results. The experimental findings about consensus rates and communication likewise provide no qualifying anchor.
One could propose a new continuous model for large organizations: represent network positions and their interactions with a graphon, assign mass fractions of trusted and adversarial roles, and ask how those assignments affect consensus probability under the paper’s learned behavioral dynamics. But that would be a proposed extension, not a continuous form of a named result in this paper. The paper’s small, fixed networks also leave the high multiplicity regime for such a model unestablished.
There is no anchor to defeat: the proponent names none, and the paper supplies none. Its results are experimental findings, learned behavior models, and simulation analyses. Section 5.3 studies node placement, and Section 5.2 uses coordinate greedy search to tune parameters, but neither states a computational problem with a complexity result. A continuous complexity question built from either would be a new problem, not a mirror of a result in this paper.
The best candidate for a stronger mirror is to study a large organization, represent its agents by masses of regular, trusted, and adversarial types, and optimize placement to maximize consensus. But the paper’s behavior depends on who is connected to whom, local states, and the global consensus event. Masses of agent types alone lose that structure. Adding a network kernel or a distribution of local neighborhoods could retain more of it, but would require a new model and objective. A dense graph limit also changes the paper’s mostly sparse network regime; keeping sparse local interactions and their randomness requires still more structure. Replacing exact consensus with near-total population agreement may be sensible, but it defines another new problem.
The paper itself studies deliberately small games: 20 consensus players and at most 5 adversaries. A large-organization setting is imaginable, but the paper does not establish that repeated agents in such a setting preserve its interaction model or its research question. The negative case is therefore strong for this paper as a ChoCo mirror: it offers no named computational result to continuize, and rescuing one requires substantial new modeling. It cannot prove that no worthwhile project could grow out of the setting; a formal computational study of placement on large networks might be valuable in its own right. That would be an extension, not this paper’s continuous mirror.
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.