| paper | d4-journals-geb-2016-0088 |
| 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 has no numbered result asserting an algorithm, complexity bound, hardness result, or computational formulation used to compute. Proposition 1 characterizes bargaining outcomes under specified behavior, while the empirical results report observed behavior; neither supplies a computational result to mirror.
fails bit a — no named computational result to mirror
No computational result is available to mirror; Proposition 1 concerns bargaining dynamics, and the remaining findings are empirical or theoretical.
I can’t make a valid anchored positive case under your criteria. The paper contains no numbered result asserting a computational problem is in P, NP-hard, FPT, or any comparable complexity class.
The closest result is Proposition 1, proved in the paper: if the two bargainers offer their preferred focal points in period 1 and then follow the credibility principle, bargaining ends at the Nash bargaining solution with disagreement outcome \(d=(d_h,d_l)\). That is a result about bargaining dynamics, not computational complexity or an encoding that the paper computes with, so it does not qualify as an anchor.
One could imagine extending the setting to many repeated negotiations among a bounded set of bargainer types, with mass representing the fraction of negotiations of each type pair. But asking for aggregate settlements in that population would be a new aggregation problem, not a continuous version of a named computational result in this paper. The paper’s continuous split of the salary budget is continuity of the outcome; its society remains a pair of bargainers. I therefore have no per-anchor continuous problem to put to a prover.
The strongest case against a mirror is that the paper gives us no computational problem to continuize. Its named result, Proposition 1, says that under specified offers and the credibility principle, bargaining ends at the Nash bargaining solution. That is a claim about bargaining dynamics, not an algorithmic problem or a complexity result. The empirical findings about settlements, fairness judgments, offers, and concessions are statistical results from 86 pairs; they do not define computational tasks either.
The best broader proposal would model a large population of bargaining pairs, with types recording roles, preferences, beliefs, focal points, and treatment. One could study the distribution of settlements or concessions across that population. That may be a sensible project, but it would extend the paper rather than mirror one of its results. If the model preserves pairings and the characteristics of both bargainers, it aggregates dyads; if it asks how to match heterogeneous people, predict equilibrium outcomes, or optimize assignments, it adds a new problem the paper does not pose. A continuum of negotiations alone supplies no missing complexity question.
So the proponent raised no anchor, and there is no paper-level continuous mirror for a prover to answer. I would not claim that no related population model could ever be worthwhile; the honest negative case is narrower and stronger: this paper offers no named computational result whose continuous mirror fits the programme.
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.