| paper | d4-conf-atal-2017-0076 |
| authors | — |
| venue | ? |
| filed under | unclassified |
| judged by | gpt-6-luna / xhigh (triple__luna__xhigh__c6r1) |
| judge confidence | high |
| authors would recognise it | unclear |
The paper contains no numbered theorem, lemma, corollary, or proposition stating a computational result, so it fails the required anchor test. The proposed population-scale winner determination problem is a new project, not a continuous analogue anchored to a qualifying result in this paper.
fails bit a — no named computational result to mirror
No qualifying named computational result anchors a mirror. The paper presents the MRVM model, a winner determination MIP formulation, SATS, and an empirical fit to auction data.
The paper has no numbered Theorem, Lemma, Corollary, or Proposition asserting a computational complexity result, so it has no qualifying anchor under your criteria. Its main computational contribution is an MIP formulation for MRVM winner determination (Appendix A, equations (6)–(23)); it also reports a median runtime of 12 seconds on instances with 10 bidders and 98 licenses. Those are a formulation and an experimental finding, not a named theorem-style result. I can’t give a compliant anchored continuous mirror without treating a different kind of claim as an anchor.
The anchor set is empty, and that is the strongest case against a mirror here. The paper states no numbered theorem, lemma, corollary, or proposition about computational complexity. Its computational contribution is a MIP formulation for finite-bidder winner determination, plus runtime measurements. The other results concern a value model, software, and how well simulated auctions fit one benchmark.
The best candidate mirror would group bidders by their complete MRVM parameter vectors, assign each type a mass, scale license supplies with that mass, and ask for an algorithm for winner determination. But that would formulate a new complexity question. It would not continuize a complexity result the paper established: the MIP describes allocations of a fixed set of indivisible licenses among a small set of bidders. SATS is a tool for generating and evaluating auction instances, not a classification of algorithms over bidder populations.
There is a plausible research direction in a repeated, supply-scaled MRVM market, and the paper’s formulation could help motivate it. I can’t rule out that its authors would find that question interesting. But on the supplied text, it is a new computational project rather than a continuous mirror of a named result in this paper. The negative case is decisive under the task’s anchor rule, though less airtight as a universal claim about every related auction model.
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.