| paper | d4-conf-ecai-2025-0499 |
| authors | — |
| venue | ? |
| filed under | unclassified |
| judged by | gpt-6-luna / xhigh (triple__luna__xhigh__c6r1) |
| judge confidence | high |
| authors would recognise it | unclear |
Theorem 2 and the other named results characterize equilibria or compare outcomes, but state no algorithm, finite input encoding, or complexity result, so bit (a) fails. A cohort equilibrium problem can be stated, but it adds coordinated bloc actions; an independent-agent nonatomic limit removes each individual’s influence and does not preserve the paper’s strategic game. That possible follow-up does not supply a computational continuous analogue of a named result in this paper.
fails bit a — no named computational result to mirror
No named computational result is covered. The cohort proposal resembles Theorem 2’s equilibrium analysis; Propositions 3–5 and Theorem 6 remain analytical characterizations and comparisons.
The strongest positive lead I can see is a two-cohort version of the digital representative game. Each cohort is a large group of interchangeable members with the same preference and communication cost; the cohorts have masses \(\mu_1=\mu_2=\tfrac12\). A cohort chooses one disclosure level \(\alpha_t\), and the team decision is
\[
\theta_T(\alpha)=\sum_{t=1}^2 \mu_t\bigl[\lambda(\alpha_t)\theta_t+(1-\lambda(\alpha_t))\mu_G\bigr].
\]
Cohort \(t\)’s per-member loss is \((\theta_t-\theta_T)^2+c(\alpha_t)\). The problem would be to compute a pair \(\alpha^*\) such that no cohort can lower its loss by changing its disclosure level alone. A scenario with many members in two homogeneous delegations gives \(N\gg\tau=2\), and the model retains the paper’s strategic disclosure, shared decision, and communication costs.
But this is an extension lead, not an eligible anchor under the stated rule. Theorem 2 is the closest named result, with its proof placed in the cited technical report [11]: it characterizes the two-player game’s equilibria as no revelation, one member partially revealing, or both partially revealing, according to conditions involving \(d_A,d_B,p,\kappa_{A,B}\). Propositions 3–5 give comparative statics on revelation, and Theorem 6 compares equilibrium and baseline outcomes. These are analytical results; none asserts a complexity classification or gives a running-time guarantee for a computational problem. The model’s \(\alpha_m\in[0,1]\) strategy spaces make the actions continuous, but the society itself still consists of two members.
The cohort version also changes who acts: members in each cohort must coordinate on one disclosure level. If individuals instead choose independently in a nonatomic population, each person’s effect on the aggregate decision vanishes, making disclosure unattractive when it has positive cost. So the cohort model is plausible as a delegation scenario, but it does not supply the paper-specific computational result the anchor requirement demands.
Accordingly, I cannot honestly name a continuous problem per qualifying anchor or predict its complexity class. The paper contains substantial named game-theoretic results, but no named computational-complexity result to mirror.
Theorem 2 is the closest named anchor, and the proponent is right that it does not qualify. It characterizes equilibria of a two-member game; Propositions 3–5 give comparative statics, and Theorem 6 compares outcomes. None defines a computational input and output problem or gives a complexity or running-time result. The strategies are continuous, but the society is still two named members. Moreover, \(\lambda\) and \(c\) are general functions with no finite input representation, so even “compute the equilibrium” has no specified complexity model. There is no paper result here for ChoCo to mirror.
The strongest proposed extension is a distribution \(\mu\) over preference types, with each type \(t\) choosing a shared disclosure level \(\alpha_t\), and the question of computing a Nash equilibrium. But if type members choose independently, the direct nonatomic limit removes each person’s influence on the team decision. With positive disclosure costs, each person then chooses \(\alpha=0\); the strategic competition at the heart of the paper disappears. Avoiding that degeneration requires making each type a coordinated bloc with a shared action. That adds collective commitment and changes who the players are. It could be a worthwhile follow-up, but it is not a population limit of the game Theorem 2 analyzes.
For a bloc model with many types, the new computational difficulty would come from solving a larger strategic game, not from the population becoming large: holding the type masses fixed, adding more people within each bloc changes nothing. One could make that a precise research question by choosing encodings for \(\lambda\) and \(c\), but those choices would define a new computational problem rather than expose complexity in a result of this paper. The two-cohort version has the same issue in its simplest form.
So the negative case is strong for the paper as written: none of its named results is an eligible computational anchor, and the most natural independent-agent limit trivializes the strategic behavior. It is weaker as a universal claim. Coordinated cohorts could be plausible, especially given the paper’s interest in richer team settings, but that possibility is a new model whose worth the paper does not establish.
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.