| paper | d4-conf-ecai-2020-0165 |
| authors | — |
| venue | ? |
| filed under | unclassified |
| judged by | gpt-6-luna / xhigh (triple__luna__xhigh__c6r1) |
| judge confidence | high |
| authors would recognise it | no |
Theorem 9 is a named formal result, but it classifies axiom-satisfying reward classes; using polyhedral feasibility to find counterexamples is proof support, not a computational result. The other numbered theorems likewise classify preferences or characterize rewards, so no anchor meets the computational gate.
fails bit a — no named computational result to mirror
No result is mirrored: Theorems 1–6 classify preference axioms and theories, while Theorems 7–9 characterize reward policies and their axiom hierarchies.
I can’t make an honest positive case with a qualifying anchor. The paper has no named result asserting that a computational problem is in P, hard, FPT, or otherwise classifying its computational complexity. Theorems 1–6 classify preference axioms; Theorems 7–9 characterize reward policies and their relationships.
The closest candidate is Theorem 9, proved in this paper: among anonymous, cake-cutting rewards satisfying specified axiom sets, the classes have exactly the containment hierarchy shown in Figure 5. The authors say they used linear-inequality feasibility checks with the Parma Polyhedra Library to find counterexamples for that hierarchy. But the theorem’s subject is an axiomatic classification, not a computational problem from the paper; the feasibility encoding is a proof aid, not a named result about an algorithm or complexity class.
One could invent a continuum of contestants, with mass distributed over contestant types, and ask which anonymous jackpot-splitting reward policies induce given preferences over measurable rankings. That would be an axiomatic extension, not a continuous mirror of a computational result in this paper. The paper’s participants and rankings are finite, and it studies preference properties rather than computation over a population distribution.
The paper has no computational result to mirror. Theorems 1–6 classify preference axioms, and Theorems 7–9 characterize reward policies. Theorem 9 is the closest candidate, but its hierarchy is axiomatic: the Parma Polyhedra Library was used to check feasibility while finding counterexamples. The paper does not pose an instance-level computational problem or give a complexity classification for one.
A large, symmetric competition could make a high-multiplicity model sensible; the paper’s setting does not rule that out. One could also define rankings by masses of contestants at each level and ask how anonymous jackpot rewards should work. That would be a continuous extension of the paper’s axiomatic theory, which is outside ChoCo’s stated computational scope. A stronger reframing might ask an algorithm to find a reward policy satisfying chosen constraints. That could be a new research problem, but it would add a computational task the paper does not study or establish results about. Continuizing the population cannot turn its existing theorems into complexity results.
The negative case is therefore strong on the programme’s stated gate: none of the proponent’s candidate anchors survives, including the best version of Theorem 9 as a *mirror*. This does not prove that no related computational project could be worthwhile; it means such a project would need its own motivation and problem statement rather than a continuous mirror of 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.