| paper | d4-journals-jet-2022-0131 |
| 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’s Propositions 1–5 characterize belief updating, while the GMM analyses report empirical estimates and comparisons; none states an algorithmic guarantee or computational hardness result. Bit (a) therefore fails, so there is no named computational result to anchor a continuous-population mirror.
fails bit a — no named computational result to mirror
No named computational result is covered. The propositions concern updating predictions, the experiments and GMM analyses are empirical, and the population remains finite; continuous actions and signal-precision parameters do not continuize it.
I can’t give a qualifying positive case from this paper: it contains no named computational result of the kind your anchor rule requires. Propositions 1–5 state predictions about belief updating, not whether a computational problem is tractable or hard.
The closest result is Proposition 4 (proved here): under LRTU, when the learner initially trusts the predecessor, a contradicting signal can receive more weight than a confirming one; when the learner initially distrusts the predecessor, the ordering can reverse. Proposition 5 (also proved here) says that under FBU, the confirming signal receives at least as much weight as the contradicting signal. These are behavioral comparative statics, not complexity results. The GMM estimates and model comparisons are empirical analyses, not named algorithmic results either.
The paper’s continuum is in the action and belief spaces, while its subjects remain a finite experimental population. A large-population version of the social-learning model could be a sensible extension, with types defined by signal precisions and beliefs about predecessor rationality. But asking for aggregate updating in that model would create a new analytic question; it would not mirror a computational result in this paper. So there are zero eligible anchors and no per-anchor continuous problem to hand to a prover.
The proponent raised no anchor, so none survives by default. The paper does contain named Propositions 1–5, but they characterize belief updating: for example, Proposition 4 derives asymmetric updating under LRTU, while Proposition 5 derives the FBU prediction. None states the complexity of a computational problem, gives an algorithm, or proves a computational lower bound. The GMM estimates and model comparisons are empirical analyses, not complexity results. There is therefore no eligible result here for a ChoCo mirror to take as its target.
The strongest population version would replicate the experiment across a continuum of independent two-person learning pairs, with types recording signal precisions and beliefs about predecessors. One could ask for the aggregate distribution of posterior reports or the mass of learners whose updates cross a threshold. That is a coherent population question. In the paper’s binary, two-period model, though, the individual updates are already given by explicit formulas; aggregating them asks for an analytic or statistical description of outcomes, not a computational problem whose complexity the paper studies.
The longer experimental sequence does not supply the missing anchor. The paper says later periods are outside its research question and focuses on the second decision-maker. Extending the analysis to long sequences, networks, or an intervention designer could yield computational questions about influence or signal allocation. Each would add a problem the paper does not formulate or analyze; it would be a new project built from the paper’s setting.
The action and belief ranges are continuous, and signal precision varies over an interval in some treatments. Those features do not make the population continuous or create a computational result to mirror. The negative case is strong for a mirror of this paper. It does not establish that computational social learning could never be worthwhile; it establishes that such a project would need a new computational question as its starting point.
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.