| paper | d4-conf-atal-2016-0154 |
| 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 reports simulation findings and gives an unnumbered cycling example, but has no numbered result asserting a computational result. Without a qualifying anchor, there is no result of this paper to mirror under the stated standard.
fails bit a — no named computational result to mirror
The experimental findings and the cycling example are not numbered computational results, so none supplies an anchor for a continuous mirror.
I can’t make a positive case that meets the requested anchor standard. This paper has no numbered theorem, lemma, corollary, or proposition asserting a computational complexity result.
Its results are experimental findings about strategic voting under different network structures, reported in tables and figures. Section 5 also gives a finite-network example in which voter updates cycle instead of converging, but it is an unnumbered counterexample, not a complexity result.
A continuous model of strategic voting on a large network might be worth studying, but it would be an analyst-proposed extension, not a mirror anchored in a named result of this paper. I won’t invent an anchor or present that extension as one.
The proponent raises no anchors, so there is no continuous question here that needs to be defeated one by one. Their central point is decisive for this paper: it reports simulation findings and a finite-network cycling example, but states no named computational problem with a complexity result. Turning those findings into a continuous dynamics or graphon study could be interesting, but it would be a new research direction, not a mirror of a result this paper established.
The strongest candidate is the finding that homophily dampens strategic voting. But voters’ incentives here come from exact probabilities of winning ties and near-ties: a single ballot can change the outcome. In a population continuum, an individual has zero mass, so that pivotal effect disappears. Retaining it requires a finite-population fluctuation model; then the question is driven by the finite-size layer the continuous mirror was meant to replace. The same problem affects the cycling example: preserving the groups’ cycles as mass movements would make each group act as a bloc, changing the paper’s individual best-response model.
The network structure creates a further obstacle to a useful high-multiplicity model. Grouping voters by ideology alone loses who observes whom. Adding each voter’s exact neighborhood preserves that information, but in the random networks studied, neighborhoods are generally too distinct for the type support to compress. A block-structured or fluctuation-corrected model could avoid these problems, but its findings would depend on assumptions added to the paper’s model, and there would still be no complexity result to anchor the continuization programme. I cannot rule out a worthwhile adjacent project; the honest negative case is that this paper supplies no worthwhile computational mirror of its own 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.