| paper | d4-journals-jet-2023-0105 |
| authors | — |
| venue | ? |
| filed under | unclassified |
| judged by | gpt-6-luna / xhigh (triple__luna__xhigh__c6r1) |
| judge confidence | high |
| authors would recognise it | no |
Neither named theorem asserts that a computational task can be solved or is hard. A mass version of core membership or blocking could be formulated, but it would add a computational question absent from the results this paper proves.
fails bit a — no named computational result to mirror
The proposed extension would concern Theorems 1 and 2 and the paper's equilibrium characterizations; it does not mirror a computational result.
I find no qualifying computational anchor in this paper. Its named results concern equilibrium and core stability, not the complexity of a computational problem.
The closest is Theorem 1 (proved here): every sequential-core allocation is a status-quo PBE outcome; every status-quo PBE outcome lies in the weak sequential core; and when effective coalitions have at most two agents, the sequential core equals the set of status-quo PBE outcomes. This is a characterization, not a result that puts a problem in P or proves it hard. Theorem 2 gives a related characterization for stationary PBE outcomes in an infinite-horizon bargaining game, also without a complexity claim.
A large exchange economy with many agents sharing a finite set of preference, endowment, and information types could motivate a continuous-population extension. But the paper’s uncertainty over the types of finitely many agents is not itself a continuous population, and proposing a mass-blocking problem would not supply the required named computational anchor. There is therefore no honest one-per-anchor continuous problem to state under your criteria.
Theorem 1 is the closest candidate, but it characterizes a relationship between the sequential core and status-quo PBE outcomes. Theorem 2 gives a parallel characterization for stationary equilibria in an infinite-horizon bargaining game. Neither states the complexity of a computational problem. The propositions and refinements likewise concern equilibrium support, blocking, and relationships among core concepts. So neither theorem supplies a computational result for a continuous mirror to continuize.
The best population reinterpretation would be a large economy with repeated agent classes, where each class fixes preferences, endowments, and information structure. That could make mass coalitions and mass objections sensible objects. But asking whether an allocation belongs to a mass version of the sequential core, or how to find a blocking coalition, would introduce a new computational problem. The paper specifies no input encoding, decision task, or algorithmic result for its finite-agent core notions that this question would extend. Continuum-agent equilibrium analysis might be worthwhile, but it would sit in the analytic tradition the programme distinguishes from its computational focus.
I would not argue that high multiplicity is implausible here; the repeated-class scenario is credible. The negative case is narrower and firmer: it does not repair the missing computational anchor. No choice of population regime turns Theorems 1 or 2 into complexity results. A related computational project could still be valuable, but calling it a continuous mirror of this paper would stretch the programme’s criterion.
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.