| paper | d4-conf-aaai-2014-0284 |
| 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 reports an experimental platform and empirical comparisons, but contains no numbered computational result to anchor a mirror. The opponent proposes a plausible new optimization task for mechanism design, but that task is not a computational result established by this paper.
fails bit a — no named computational result to mirror
No named computational result is mirrored; the paper's mechanism description and empirical comparison remain outside an anchored continuous mirror.
This paper has no qualifying named computational result: it states no numbered Theorem, Lemma, Corollary, or Proposition establishing complexity or an algorithmic classification. Its central contribution is an experimental platform and an empirical comparison of prediction markets with peer prediction.
A large online poll does suggest a plausible continuous population: types could describe participants’ information, beliefs, and reports, with mass representing the fraction of participants of each type. But the paper does not give a named computational result to anchor a continuous problem to. Its discussion of the peer truth serum’s incentive condition is not such an anchor, and turning it into one would overstate the paper’s results. So there is no properly anchored continuous question to propose here.
I would not treat “no numbered complexity theorem” as enough to rule out a mirror. The paper’s named peer truth serum has a formal incentive result: under the stated beliefs, truthful reporting is optimal when condition (3) holds. It is not a computational result, but it gives a concrete property to turn into a computational question.
For a large online poll, let each type encode the respondent’s signal, beliefs, reward sensitivity, and participation cost, with \(\mu_t\) the fraction of respondents of that type. The answer set stays finite. Given \(\mu\), one could compute whether truthful reporting is an equilibrium under the peer truth serum, then choose the reward scale and matching window to minimize expected per-person payments while preserving truthful incentives and a target forecast score. The paper itself adjusts rewards, limits tickets, and moves to a five-day running statistic after observing strategic behavior. Optimizing those design choices over a population distribution is a natural computational extension of the problem it studied. An individual can still have an incentive to report truthfully in a continuum: their deviation does not change the aggregate, but can change their expected reward.
This regime fits the paper’s open-poll setting better than its 200-user experiment alone suggests. In a large survey or crowdsourcing platform, many respondents can share the same relevant signal and incentive characteristics. The type definition can include every feature the mechanism uses; per-capita payments remain meaningful as the population grows. The paper’s existing implementation does not already answer the proposed question: it tests a particular mechanism and reports performance, but does not optimize its incentive and budget design over respondent types.
The weak point is that this mirror derives a new computational task from the paper’s incentive result; the paper does not claim a complexity classification for that task. If the requirement is specifically a numbered computational theorem as the anchor, the proponent’s objection stands. But that narrow absence does not establish that no worthwhile continuous mirror exists.
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.