| paper | d4-conf-aaai-1999-0030 |
| authors | — |
| venue | ? |
| filed under | unclassified |
| judged by | gpt-6-luna / xhigh (triple__luna__xhigh__c6r1) |
| judge confidence | high |
| authors would recognise it | no |
Proposition 7 states when a plan is individually stable, but makes no claim about computing stability or its complexity. The introduction’s NP-completeness claim is attributed to prior work and concerns a different problem, leaving no computational result in this paper to mirror.
fails bit a — no named computational result to mirror
No result in the paper supplies a computational anchor; Propositions 1–9 give structural claims about dependence and stability.
I can’t make a case anchored in a named computational result from this paper: it contains no numbered theorem, lemma, corollary, or proposition asserting a complexity or algorithmic result. Its numbered propositions characterize when multiagent plans are stable.
The closest candidate is Proposition 7, proved in the paper: a joint plan that contains a dependence relation is individually stable if and only if it is based on reciprocal dependence. That is a structural characterization, not a result about the complexity of deciding stability, so it does not qualify as an anchor under the requested standard.
A continuous-population stability problem might be worth formulating separately, but presenting it as a mirror of a computational result here would overstate the paper’s contribution.
The strongest case against a mirror is that there is no computational result here to mirror. The proponent’s case concedes that, and the numbered propositions confirm it: they characterize dependence and stability, but state no algorithm, complexity bound, or hardness result for a computational problem. The paper does not specify the finite input representation needed to ask such a complexity question. The cited NP-completeness result about cooperation is from another paper and concerns a different problem; importing it would make the proposed mirror a study of that prior result, not a mirror of this paper.
The best rescue would be to ask how hard it is to find or recognize stable plans in a large society, using Propositions 6–9 as the starting point. But that requires choosing finite plan spaces, utility encodings, and which deviations or coalitions count. Those choices determine the computational problem; the propositions do not. Continuizing the population makes the gap sharper: in a standard atomless model, one person has zero mass, so a unilateral deviation cannot change aggregate outcomes. Replacing an individual deviation with a positive-mass cohort’s deviation could make power meaningful, but it changes individual stability into a coalition or population-level question. Keeping identity-specific power instead requires types rich enough to preserve the relevant identities and relations, which can defeat the small-support high-multiplicity premise.
So I would reject a continuous *mirror of this paper’s results*: none of its results is computational. That does not establish the stronger universal claim that no worthwhile continuous computational study could ever be built from its model. A separate study of continuum coalition stability might be valuable, but calling it a mirror would overstate what this paper contributes.
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.