| paper | d4-conf-atal-2015-0122 |
| 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-based equilibrium and welfare comparisons, but it has no numbered theorem, lemma, corollary, or proposition asserting a computational result. The proposed type-distribution market would also require a new equilibrium and market model, so it cannot supply the missing anchor.
fails bit a — no named computational result to mirror
No result is mirrored; the paper's empirical equilibrium and welfare comparisons remain outside coverage.
The strongest positive foothold is the market scenario: a high-volume electronic market could have many background investors who fall into a modest number of complete trader types, with each type’s mass representing its share of the investor population. That makes a population-continuum model plausible in principle. The paper itself, however, studies markets with 25 or 66 background traders, and its “continuous” double auction describes the trading institution, not a continuous population.
The paper reports computational findings, including empirical equilibrium and welfare comparisons in Tables 2–8 and Figures 3–9. But it contains no numbered Theorem, Lemma, Corollary, or Proposition asserting an algorithmic or complexity result. Those findings therefore cannot serve as the required anchors. I can’t responsibly supply one continuous problem per anchor, or turn the paper’s simulation results into a formal result it does not claim. There is a plausible scenario-level case for a mirror, but no qualifying named computational result on which to ground the requested positive case.
The proponent names no result as an anchor. The paper has no numbered theorem, lemma, or corollary asserting a computational or complexity result. Its findings are empirical comparisons of welfare and surplus in simulated equilibria. The proposed high-volume market is a plausible setting, but it is a scenario, not a continuous question tied to a named result. So no anchor survives because none was supplied.
The strongest replacement would model background traders as a distribution over complete types—valuation schedules, arrival rates, and strategies—and ask for equilibrium and welfare with a finite number of market makers. That is a coherent large-market question. But it changes the paper’s computational object: the reported equilibria are finite-player Nash equilibria, where one trader’s deviation can affect the outcome. In an atomless population, a single deviation leaves the distribution unchanged. Keeping unit-sized orders makes aggregate flow grow with the population; scaling order sizes or market-maker capacity instead creates a fluid or mean-field market model. Either route needs new market mechanics and an equilibrium notion. The result would be a computational study of that new model, not a continuization of a computational problem the paper states.
That makes the negative case strong against a direct ChoCo mirror: the paper offers no algorithmic problem to mirror, and its main contribution is empirical analysis of a finite stochastic market. It is not a proof that no scenario could yield worthwhile work. A finite-type population game for high-volume markets could be a useful adjacent project, but the supplied material does not establish it as a mirror of this paper. The universal negative is therefore weaker than the narrower conclusion.
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.