| paper | d4-conf-aaai-2013-0254 |
| 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 states no numbered theorem, lemma, corollary, or proposition with a computational result, so there is no eligible result to anchor a mirror. Its coalition procedures and experiments may motivate a new mass-based optimization problem, but that is not a computational result established by this paper.
fails bit a — no named computational result to mirror
No named computational result is covered; the coalition procedures and simulation findings remain unmirrored.
This paper has no numbered Theorem, Lemma, Corollary, or Proposition asserting a computational result. Its cooperative bid procedure and sorting-time discussion are presented in prose; the tables report simulation results. Neither supplies an eligible named anchor, so I can’t honestly state a continuous problem *per anchor* as requested.
There is a plausible population mirror in the paper’s model: types could represent consumers with the same load profile, shifting capacity, costs, and uncertainty; mass would be their share of the consumer population; and the decision variables would be how much of each type’s load to shift from peak to off-peak intervals. A continuous cooperative-bid problem could ask how to select and compensate that mass subject to capacity, group-price, individual-rationality, and budget constraints.
That is a candidate research question, not a mirror anchored in a named result of this paper. The paper’s 4,968-agent experiment does not establish a high-multiplicity regime: its agents have sampled heterogeneous preferences, and it gives no small-support type description. A convincing regime would need to show that many consumers share the complete characteristics relevant to participation and compensation.
The strongest negative point is that this paper gives no named computational result to continue. It presents four coalition-selection procedures, discusses their sorting cost, and compares them experimentally. Its theoretical claims concern incentives, individual rationality, and budget balance, not the complexity of a computational problem. So the proponent has no named computational anchor; equations (2)–(4) and the method descriptions do not supply one.
The strongest reconstruction would be a continuous version of coalition selection: represent consumers by their complete load profiles, shifting capacities, costs, availability, and forecast beliefs, then choose masses to maximize coalition surplus subject to the demand threshold, individual rationality, and budget constraints. But the paper does not settle which objective that should optimize. Its four methods make different choices, and it explicitly says that maximizing reduction need not maximize coalition gain. Asking for an optimal mass-based coalition would therefore introduce a new optimization problem, rather than mirror a computational result the paper establishes.
The proposed type space also has a serious regime problem. A complete type here must include a consumer’s relevant load and cost information across many time intervals, as well as their forecast uncertainty and availability. The paper’s simulated agents have sampled, heterogeneous characteristics; its average coalition has about 48 members. With exact types, many consumers would have distinct types, so the model may have support close to the population size. Coarsening them into a few consumer classes could make the mass model useful, but it would need to preserve the individual scoring and participation guarantees the mechanism relies on. The paper does not show that a small set of such classes captures those features.
That is a case against treating the proposed optimization as a paper-specific mirror. It is not a convincing proof that no worthwhile mirror exists. A large population of standardized residential customers could plausibly form repeated, complete types, and an optimal mass-based coalition problem might interest these authors. The honest negative case is therefore strongest on the absence of an eligible computational anchor and weaker on the universal claim: the paper leaves room for a plausible new continuous problem, even though it does not itself formulate or establish one.
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.