| paper | d4-journals-geb-2022-0124 |
| authors | — |
| venue | ? |
| filed under | unclassified |
| judged by | gpt-6-luna / xhigh (triple__luna__xhigh__c6r1) |
| judge confidence | high |
| authors would recognise it | no |
Proposition 3 and the other named results establish equilibrium comparative statics, but none asserts an algorithm, running-time bound, or hardness result. A continuous-population auction model could be a worthwhile new project, but it would not mirror a computational result in this paper.
fails bit a — no named computational result to mirror
Propositions 1–4 and Corollary 1 concern equilibrium dropout values; the calibration and counterfactual simulations are not stated as named computational results.
I can’t make a positive case that meets the anchor requirement: the paper has no named computational result establishing that a problem is in P, NP-hard, FPT, or similar. Propositions 1–4 and Corollary 1 are equilibrium comparative statics. For example, Proposition 3 says that when the global bidder has no credit, local credits weakly lower its equilibrium dropout value, strictly so with at least two active local bidders. That is a useful economic result, but it does not analyze an algorithm or a computational problem.
The tempting population mirror would be a large regional spectrum auction: types could encode credit eligibility, license interests, and valuation or complementarity parameters; mass would represent the share of firms of each type; and decisions would be dropout thresholds under the auction’s activity rule. But the paper models a finite set of bidders with random valuations. Its valuation distribution is uncertainty over values, not a continuous population, and the calibration is not a named complexity result. So that mirror would be a new research question, not an honest anchor-based case for continuizing a computational result in this paper.
The strongest candidate is Proposition 3: local bid credits weakly lower a global bidder’s equilibrium dropout value, strictly when at least two local bidders remain active. But that is an economic comparative static, not a computational result. The paper states no complexity bound or algorithmic question about computing the equilibrium, calibration, or counterfactuals. So there is no computational result here for ChoCo to continuize.
One could make a more ambitious mirror: represent bidders by a distribution over credit status, license interests, and values, then ask how to compute aggregate dropout thresholds or revenue after a credit change. That is a coherent research question, but it changes what Proposition 3 studies. Its model has finitely many bidders, and its exposure effect depends on competition among particular bidders for a finite set of indivisible licenses. Making bidders atomless while keeping the licenses finite leaves aggregate winners with zero population mass; scaling the license supply or changing the licenses into divisible capacity would produce a different auction model. Either way, the computational problem would need to be supplied from outside the paper.
The case against is strongest on that boundary: a worthwhile continuous-population auction problem might be built, but the paper does not provide a computational result to anchor it. The boundary is not airtight—one could motivate a new high-multiplicity auction model—but that would be a new ChoCo project, not a continuous mirror of this paper’s 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.