| paper | d4-conf-sigecom-2013-0062 |
| 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 empirical estimates and simulations, but contains no numbered theorem, lemma, corollary, or proposition asserting a computational result to mirror. Its proposed mass-based estimation problem is a possible follow-on, not a continuous analogue of a named result in this paper.
fails bit a — no named computational result to mirror
No named computational result is mirrored; the paper's empirical estimates of selection effects and simulated decomposition are outside the stated computational-result criterion.
I find no eligible anchor in this paper. It contains no numbered theorem, lemma, corollary, or proposition asserting a complexity result such as polynomial-time solvability, NP-hardness, or fixed-parameter tractability. The results in Section 5.4 and Table V—positive estimates for product selection \(\rho\) and dyad selection \(\psi\), and a simulated decomposition of the adoption effect—are empirical findings, not named computational results.
There is a plausible *future* continuous-population question here: represent a large platform by rational masses of complete adopter–peer–offer types, then estimate the population selection correlations and compute the counterfactual adoption-risk ratios with product selection or dyad selection disabled. A scenario with many users but relatively few repeated behavioral and exposure types is conceivable. But the paper does not establish that regime, and its latent user and network characteristics could make the number of distinct types large. More importantly, that question would be a new computational formulation of its econometric model, not a mirror of a named complexity result in the paper. I would not assign it to Class A, B, or C on this evidence.
The strongest case is that this paper offers no named computational result to mirror. Its headline findings are estimates of \(\rho\) and \(\psi\), plus a simulated decomposition of adoption risk. They are statistical conclusions from an experiment, not complexity results for a computational problem. The paper’s MCMC procedure computes those estimates, but the article does not study the complexity of estimation or compare algorithms. There is therefore no result here to place in ChoCo’s tractability trichotomy.
The proponent’s best candidate is still worth examining: represent a large platform as masses over adopter–peer–offer types, then estimate the selection correlations and compute the counterfactual risk ratios. Repeated users and offers could make a high-multiplicity regime plausible, so I would not reject this on the grounds that such a population cannot exist. The difficulty is preserving what the estimand uses. Types detailed enough to retain network relationships, repeated exposures, and the dependence between an adopter’s sharing and a peer’s adoption may be nearly unique per observation. Coarser types could create large masses, but would need additional exchangeability assumptions to preserve those dependencies. Either way, aggregation does not supply a new computational question from the paper: exact grouping is likelihood compression, while the correlation estimates and counterfactuals remain statistical inference in a model already built around probability distributions.
A more ambitious version might optimize whom to encourage to share under exposure or campaign constraints. That could be a worthwhile project, but it adds an objective and design problem the paper does not formulate. It is an adjacent algorithmic question, not a continuous mirror of the paper’s results. The negative case is strongest at that boundary: no result in this article warrants a continuous computational-social-choice mirror, though one can imagine valuable follow-on problems motivated by its findings.
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.