| paper | d4-conf-ijcai-2005-0106 |
| 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 simulation findings but has no named result asserting an algorithm, computational guarantee, or hardness classification. A continuous cohort study would need a newly specified computational task, so it would not mirror a result established here.
fails bit a — no named computational result to mirror
No qualifying named computational result is present. The simulation results on stability and regret, and the remark that perfect Bayesian equilibria are prohibitively hard to compute, are not computational results under the stated criteria.
The paper contains no numbered Theorem, Lemma, Corollary, or Proposition asserting a computational complexity result. Its findings are empirical: for example, Table 1 reports simulated convergence to stability under three matching mechanisms, and the paper says that computing the perfect Bayesian equilibrium is prohibitively hard without proving a complexity classification.
So I can’t give the required anchored case for a continuous mirror without treating an empirical result as a theorem or inventing an anchor. The paper may suggest a separate continuous-population study of learning and matching dynamics, but it offers no named computational result to mirror under the criteria given.
The proponent raised no result-specific anchor, and the paper supplies none: it proves no complexity classification or algorithmic bound for a computational problem. Its findings are simulation results about stability and regret, and its remark that perfect Bayesian equilibria are prohibitively hard to compute is not a complexity result. The reported “asymptotic” stability concerns time passing in a fixed \(5\times5\) market; it is not a large-population limit.
The strongest possible rescue would model a large labor market as cohorts of agents with shared reward laws and learning rules, then study whether the mass of matches converges to stability and what aggregate regret it incurs. That is a sensible high-multiplicity scenario, and a continuous population model could make it worth investigating. But it would require specifying the learning-state distribution, stochastic transitions, and the computational task. Asking for the complexity of deciding convergence, for example, would create a new problem rather than mirror one this paper established. Without that added construction, the natural continuous version is a study of population learning dynamics, which falls outside ChoCo’s focus on computational complexity.
I can’t make the universal claim airtight: that labor-market project might be worthwhile on its own. The negative case is strongest as a scope judgment. This paper offers no computational result for ChoCo to continuize; its continuous population extension would be a new research direction, not a mirror anchored in the paper.
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.