| paper | d4-conf-aaai-2014-0261 |
| 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 describes algorithms and computational claims, but the supplied text contains no numbered theorem, lemma, corollary, or proposition asserting a computational result. Its cited hardness results belong to earlier papers, so there is no qualifying result of this paper to anchor a population-continuous mirror.
fails bit a — no named computational result to mirror
No result qualifies as an anchor; the paper's elicitation algorithms, prose complexity claims, and empirical results therefore have no mirror coverage here.
I can’t give a valid anchor-based case under your criteria: the supplied paper contains no numbered Theorem, Lemma, Corollary, or Proposition asserting a computational result.
The paper’s own contribution includes a polynomial-time method for computing pairwise maximum regret in the OPP/OPPI models and the RtI elicitation scheme, but those claims appear in prose and numbered algorithms, not as qualifying named results. The hardness claims it discusses—NP-completeness of computing minimax regret and NP-hardness of interview minimization—are attributed to Drummond and Boutilier (2013) and Rastegari et al. (2013), respectively; they are prior results, not results of this paper.
A residency-market continuum is conceivable, but moving from individual interviews to type-level interview mass risks changing what an interview reveals. With no eligible anchor and that fidelity problem, I would not present a continuous problem as a mirror of this paper’s results.
The appended “case for” raises no pro-mirror anchor; it concludes there is none. Its strongest implied candidate is the paper’s Refine-then-Interview scheme, including the polynomial-time computation of pairwise maximum regret.
The paper is computational, but it has no numbered theorem, lemma, or corollary asserting a complexity result. Its NP-hardness claims are attributed to earlier papers. RtI is presented as a heuristic scheme and evaluated empirically; the polynomial-time regret computation appears as an algorithmic claim in the prose. Under the stated anchor rule, that leaves no eligible named result to mirror.
The strongest candidate if I relax that rule is a large residency market with a few applicant and employer preference types, where the task is to minimize the mass of interviews and queries needed to guarantee a stable assignment. The difficulty is that RtI’s information is attached to particular people and options. Under the programme’s definition of type, identical agents share the preferences and other relevant attributes that determine their answers. Then an answer from one representative can serve the cohort: charging by type-level query or interview makes the population mass irrelevant to elicitation, while charging by mass lets an arbitrarily small interviewed portion supply the same information. If answers cannot be shared because they reflect private, person-specific information, those agents are not one type. Either way, the proposed mass-interview problem no longer mirrors RtI’s interview guarantee.
A type-level stable-assignment problem may still be worth studying on its own. But the supplied paper gives no named computational result for it to mirror, and its central interview-minimization question loses its population-scale meaning under the required homogeneity assumption. I cannot rule out every model someone might devise; the weakness in this negative case is that a new, carefully specified cohort model could be interesting. I do not see one that remains a faithful and worthwhile mirror of this paper’s result.
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.