| paper | d4-conf-ijcai-2015-0061 |
| authors | — |
| venue | ? |
| filed under | unclassified |
| judged by | gpt-6-luna / xhigh (triple__luna__xhigh__c6r1) |
| judge confidence | high |
| authors would recognise it | no |
Neither candidate result has computational character: Theorem 1 is a mechanism-design impossibility, and Theorem 2 bounds welfare in a strategic game without analyzing computation. The distribution over private types describes uncertainty about two players; turning it or repeated games into population mass would introduce a new model, not mirror a computational result in this paper.
fails bit a — no named computational result to mirror
Theorem 1 and Theorem 2 are the candidate anchors; they establish a mechanism-design impossibility and an equilibrium welfare bound. The remaining results analyze the single-offer mechanism and its efficiency, without a named computation or complexity result.
I can’t make a programme-grade positive case under the anchor rule: this paper contains no named result about a problem being in P, NP-hard, FPT, or otherwise computationally classified.
The closest anchor is Theorem 1, proved here by reduction to the cited Myerson–Satterthwaite result. It says that for one-way games there is no mechanism that is ex-post efficient, interim individually rational, Bayes–Nash incentive compatible, and budget balanced. Theorem 2, also proved here, bounds the Bayesian price of anarchy of the single-offer mechanism. Both are mechanism-design results; neither gives an algorithm or complexity classification.
A possible high-multiplicity setting would be many similar local restoration negotiations, represented by the mass of each complete negotiation type. But that would turn the paper’s single A–B game into a population of bargaining units. Theorem 1 does not establish a computational result about that new model. Treating its distribution over private valuations as the continuum would instead continuize uncertainty, while the population stays two agents. So there is no honest per-anchor continuous computational problem to hand to a prover from this paper.
The strongest negative is at the programme’s threshold: this paper names no computational result to continuize. Theorem 1 proves mechanism infeasibility; Theorem 2 and the other numbered results give welfare bounds. None studies running time, algorithmic complexity, or an algorithmic problem. A continuous version of either theorem could be an economic or mechanism-design question, but it would not mirror a computational result of this paper.
Theorem 1’s type distributions describe uncertainty about two strategically distinct agents. They do not describe the composition of a society. A plausible high-multiplicity construction would repeat many \(A\)-\(B\) bargaining units, with each type specifying a complete unit. If units remain independent and budget balance applies within each unit, the impossibility result simply repeats across units. If budget balance is pooled across units, cross-subsidies could change the result, but then the question is a new large-market mechanism-design problem whose answer does not follow from Theorem 1. A single \(B\) bargaining with a mass of \(A\) agents is another possible model; it changes the two-player game and its incentive and participation constraints.
Theorem 2 has the same obstacle. Its expectation over \(A\)’s private type is uncertainty within a game, not population mass. Replicating independent games gives an aggregate welfare bound from the unit-level bounds. Adding shared resources or pooled transfers could create an optimization problem, but those couplings are absent from the paper. Lemma 2’s optimization over \(s_A\) and \(\gamma\) might motivate a computational question if the action spaces were represented in a suitable input model; the paper neither specifies that problem nor gives a complexity result for it.
The negative case is strongest as a claim about programme fit, and weaker as a universal claim about research value. Repeated restoration negotiations could motivate a new continuous mechanism-design project. On the material here, though, that would be a new model and computational agenda, not a continuous mirror of a named computational result in 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.