| paper | d4-conf-ijcai-2011-0123 |
| authors | — |
| venue | ? |
| filed under | unclassified |
| judged by | gpt-6-luna / xhigh (triple__luna__xhigh__c6r1) |
| judge confidence | high |
| authors would recognise it | unclear |
The paper’s numbered results are axiomatic: Proposition 4 proves an incompatibility, while Proposition 10 and Corollary 4 characterize merging operators. None asserts an algorithm, complexity bound, or other computational result. The proposed weighted optimization is a plausible new extension, but it cannot supply the missing computational anchor.
fails bit a — no named computational result to mirror
No computational result is covered; the paper’s results concern axioms, representation, and characterization.
This paper contains no named computational result of the required kind: no numbered theorem, lemma, corollary, or proposition establishes a problem’s complexity or gives an algorithm. Its named results are representation and axiomatic claims—for example, Proposition 10 characterizes EIC merging operators satisfying (Ind), and Corollary 4 identifies the unique such operator as \(\Delta_{d_D,\Sigma}\). Those are not computational anchors.
A continuous population interpretation is conceivable: types could be propositional belief bases, mass could be the fraction of agents holding each base, and the merged worlds could minimize the total mass of bases they contradict, subject to the integrity constraints. But turning that into a computational question would be a new extension, not a mirror of a named computational result in this paper. The paper’s central contribution is axiomatic, and continuizing that contribution while the population stays finite would also fall outside ChoCo’s stated scope.
There are no computational anchors here. The paper’s numbered results characterize operators and their axioms: Proposition 4 proves an incompatibility, Proposition 10 gives a representation, and Corollary 4 identifies a unique operator. None states a complexity bound, an algorithm, or a computational problem. So there is no named computational result to mirror.
The strongest possible extension is clear: let each type be a complete belief base with mass \(\mu_t\), then compute a merged world maximizing the total mass of bases it satisfies, subject to the integrity constraint. The paper’s multiset model makes that population regime sensible; lack of multiplicity is not a good objection. But this would study the complexity of evaluating or optimizing the characterized operator, which the paper does not do. A continuous version of the characterization itself would remain axiomatic, outside ChoCo’s scope. Adding mass transfers or other interventions would introduce still more of a new problem.
This is a narrow negative case, not proof that continuous belief merging is uninteresting. The weighted-consensus extension may be worth studying. The case against is that it is a new computational project, not a continuous mirror of a computational result in this 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.