| paper | d4-journals-jet-2010-0018 |
| authors | — |
| venue | ? |
| filed under | unclassified |
| judged by | gpt-6-luna / xhigh (triple__luna__xhigh__c6r1) |
| judge confidence | high |
| authors would recognise it | yes |
Theorem 4.2 establishes equilibrium uniqueness, Theorem 4.1 characterizes it with equations, and Theorem 7.1 characterizes the patient-player limit; none asserts an algorithm, complexity bound, or other computational result. The opponent’s distributional equilibrium computation is a plausible follow-on, but it is not a computational result of this paper to continuize.
fails bit a — no named computational result to mirror
No continuous mirror is anchored to a computational result; computing the distributional bargaining equilibrium or its patient-player limit would be follow-on work.
There is no qualifying computational result to anchor a continuous mirror. Theorem 4.2 proves uniqueness of the bargaining equilibrium, and Theorem 7.1 characterizes its limit as the discount factor approaches one. Neither gives a complexity classification or an algorithmic bound. Theorem 4.1 characterizes equilibrium through equations, but it is not a computational result of the kind required here.
The paper already makes the outcome space continuous, while its population of players remains finite. Replacing those players with a distribution of preference types and a type-level recognition process could define a new bargaining problem, but it would not mirror a named computational result in this paper. Under the requested anchor standard, there are no anchors and therefore no per-anchor continuous problems to propose.
The strongest case against a mirror is that this paper has no named computational result to mirror. Theorem 4.2 proves uniqueness of a stationary bargaining equilibrium; Theorem 7.1 describes its limit as the discount factor approaches one. Neither studies the complexity of a computational problem or gives an algorithmic bound. The paper’s continuous object is the set of outcomes, while its population is finite.
The best replacement candidate would be: given a distribution over preference types and a Markov process over proposer types, compute the unique stationary bargaining equilibrium—or its patient-player limit. That is a coherent high-multiplicity bargaining model, and defining types to include preferences and recognition behavior avoids losing relevant distinctions. But it asks a new algorithmic question about an equilibrium result, rather than continuizing a computational result in this paper. The time-invariant case makes the gap especially clear: the paper already expresses the limit through the preference distribution’s CDF, but proves an equilibrium-selection claim, not a computational one.
So the negative case rests on the missing anchor, not on a claim that no sensible continuous population model can be built. If “compute the equilibrium established by this theorem” is accepted as a sufficient mirror, this case is weak: it is plausible follow-on work. Under the stated requirement to anchor the mirror in a named computational result of the paper, though, it does not qualify.
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.