| paper | d4-journals-jet-2007-0155 |
| 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's numbered claims concern equilibrium, efficiency, and stability, without an algorithmic, complexity, or constructive computational assertion. With no qualifying result to anchor, a computational problem over a continuous population would be a new project rather than a mirror of this paper.
fails bit a — no named computational result to mirror
No result is mirrored; the paper studies when equilibrium binding agreements arise and whether they are efficient.
I can’t make an anchor-based case for this paper under your criteria. It has no numbered theorem, lemma, corollary, or proposition asserting a complexity or algorithmic result such as NP-hardness, polynomial-time solvability, or FPT. Its results concern equilibrium binding agreements: when efficient agreements can arise, and why inefficient ones may still arise. Those are equilibrium claims, not computational anchors, so there is no per-anchor continuous problem I can honestly hand to a prover.
There is a plausible population setting behind the subject: many firms or agents grouped into finitely many payoff-relevant types, with mass recording each type’s share. A coalition could be represented by the masses of types it contains, and deviations could transfer mass among coalition structures. But the paper does not give a computational task or compact input representation for that continuum model. Turning it into “find an efficient EBA” would be a new computational question, not a mirror anchored in a result of this paper. That gap is the strongest objection to a positive case.
The proponent names no anchor, and that is the strongest case against a continuous mirror. The paper’s propositions and theorems concern the existence, efficiency, and stability of equilibrium binding agreements in finite coalition-formation games. None gives an algorithm, analyzes complexity, or states a computational problem. There is no computational result here for continuization to mirror. A continuum-agent version would lead into equilibrium theory, while ChoCo’s target is computational analysis.
The strongest candidate is to group agents into payoff-relevant types, represent coalitions by type masses, and ask whether an efficient equilibrium binding agreement exists or can be computed. That could be a coherent new problem, but the paper does not specify it. Its framework assigns payoffs across coalition structures and studies deviations among them. A mass model would need new assumptions about payoff functions, allowed deviations, and how those objects are encoded. To make masses preserve the relevant information, types must capture agents’ payoff responses across the possible structures and deviations; in a general instance, that may leave little multiplicity to exploit. Enforcing symmetry or structured payoffs could create a useful computational problem, but it would be a new model rather than a translation of one of this paper’s results.
So no proposed anchor survives: the paper offers no computational result to continue. The limit of this negative case is that it cannot establish that a separately formulated continuum coalition-formation problem would be unworthy. It establishes that such a project would need its own computational question and motivation; this paper does not supply a continuous computational mirror.
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.