| paper | d4-conf-atal-2005-0140 |
| 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 reports simulation findings but contains no numbered theorem, lemma, corollary, or proposition asserting a computational result, so it fails the required anchor test. The proposed mass-transfer LP adds an intervention problem that the paper does not study and cannot supply the missing anchor.
fails bit a — no named computational result to mirror
No result is mirrored; the paper's simulation findings about voting policies, unreliable proposals, observation capacity, and task performance remain uncovered.
There is no eligible named computational anchor in this paper. It contains no numbered theorem, lemma, corollary, or proposition asserting a complexity result. Section 3.3 reports simulation outcomes—for example, that majority and weighted-observation voting are more stable than optimistic voting as corrupt or selfish agents increase—but those are empirical findings, not named computational results. So I can’t honestly supply an anchor-indexed mirror without inventing an anchor.
The best positive lead is a population version of that policy-robustness question. Imagine a large organization assigning many similar tasks, with \(N\) workers but only \(\tau\) complete types: each type records task-performance distribution, proposal behavior, observation capacity, and the model state used to make proposals. Let \(\mu_t\) be the fraction of workers of type \(t\). A policy tallies the types’ proposal mass, selects a worker type, and yields that type’s expected task performance. This makes the population continuous while keeping the voting-policy question recognizable.
A concrete, but new and unanchored, question would be: given rational masses \(\mu_t\), each type’s proposal distribution \(q_t(c)\), policy weights, candidate-type performance values \(r_c\), allowed conversions between proposal-behavior types with costs \(\gamma(t,t')\), and a threshold \(\theta\), find the minimum-cost mass transfer that makes the policy select a type \(c\) with \(r_c<\theta\). With fixed proposals and weights, the winning conditions are linear inequalities, so one can minimize transfer cost by considering each below-threshold winner in turn and solving an LP. That would give a tractable continuous robustness problem motivated by the paper’s experiments.
Its weakest point is that it formalizes a one-round robustness question, while the paper’s reported results concern repeated observations, model updates, and convergence in a five-agent simulation. The large-organization scenario is plausible, but this candidate neither mirrors a named computational result nor establishes that the authors would accept the static optimization as their problem. The paper therefore offers a promising population angle, but no anchor-compliant case for a continuous mirror.
There are no eligible computational anchors here. The paper states no theorem, lemma, or corollary about the complexity of a decision or optimization problem. Its central claims are simulation findings about how voting policies perform as team composition and agent behavior vary; the authors even defer significance testing. So there is no named computational result for a continuous mirror to restate.
The proposed mass-transfer question does not supply that missing anchor. It adds intervention costs \(\gamma\), a performance threshold, and a task of changing the population composition. The paper has no such intervention problem. With fixed proposal distributions and policy weights, the proposed question may be a clean way to study ballot robustness, but that makes it a new problem inspired by the paper. Its static setup also removes the paper’s central feedback loop: agents observe task performance, update their models, and use those models to propose workers on later tasks.
A more faithful version is easy to imagine: a large organization repeatedly assigns similar tasks, with workers grouped by performance, proposal behavior, observation capacity, and model state. High multiplicity is plausible in that setting; I would not reject the mirror on the grounds that people have individual identities. But freezing the model state returns to the new static robustness problem. Preserving the repeated updates requires modeling how a distribution of observers’ beliefs and histories evolves. That points toward a stochastic mean-field learning question. One could add a computational objective—such as finding the smallest mass of corrupt agents that causes long-run performance to cross a threshold—but the paper does not define that objective or prove a computational result that such a question would mirror.
So the negative case is strongest under the programme’s mirror standard: this paper offers no anchor-compliant continuous mirror, and the proposed LP is better described as a follow-up problem. The broader claim that *no* worthwhile continuous-population study could arise from this setting is weaker. A repeated-task population model could be worthwhile, but it would be new research motivated by the paper, not a continuization of one of its computational results.
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.