| paper | d4-conf-aaai-2013-0236 |
| 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 pilot-study measurements and qualitative findings, but contains no numbered theorem, lemma, corollary, or proposition asserting an algorithm, computational formulation, or hardness result. Since no named computational result qualifies under bit (a), there is no eligible result to anchor a continuous mirror.
fails bit a — no named computational result to mirror
No named computational results are available to mirror; the paper's findings are empirical and qualitative.
The paper contains no numbered Theorem, Lemma, Corollary, or Proposition asserting a computational result. It reports a 15-player pilot study, including 83.5% agreement on card judgments and storyteller points in 66.7% of rounds, plus qualitative findings about system errors. Those are empirical observations, not eligible anchors under your criteria.
A continuous model of a large crowd of critics might be imaginable, but it would have no named computational result in this paper to mirror. So I can’t give a valid prover-ready continuous problem per anchor without inventing one. The strongest honest positive case is that this paper supplies no qualifying anchor for one.
The appended “case for” raises no continuous anchor, and its conclusion supports the case against a mirror. There is no named computational result here to continuize: the paper reports pilot-study measurements and qualitative findings about which system faults players noticed. Those may be useful findings, but they are not complexity claims or algorithmic results.
The strongest candidate mirror would model a large crowd of critics as a distribution over types—perhaps differing in expertise, language, or critique style—and ask how to allocate review tasks or aggregate feedback to identify which subsystem to improve. A large online crowd could make that a sensible high-multiplicity setting. But the paper does not define an optimization objective for that decision: players surface critiques, and researchers interpret them and decide what to change. Adding costs, utilities, and a rule for choosing improvements would create a new computational problem, not mirror a result in the paper. Keeping the Dixit mechanics would add round-specific cards, hands, and interactions, but still would not supply the missing computational target.
So the negative case is strong for rejecting this paper as a source of a ChoCo mirror. It is not proof that no useful continuous optimization problem could ever be built around crowd critique; that would be a new research direction rather than a continuization of this paper’s 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.