| paper | d4-journals-jet-2019-0028 |
| authors | — |
| venue | ? |
| filed under | unclassified |
| judged by | gpt-6-luna / xhigh (triple__luna__xhigh__c6r1) |
| judge confidence | high |
| authors would recognise it | unclear |
The numbered results characterize when social choice rules can be implemented in equilibrium or establish an impossibility of implementation; none asserts an algorithm, computational hardness, or a computable formulation. A continuous population of residents is imaginable, but that does not supply the missing computational result, and atomless residents may lose the individual pivotal influence central to the paper’s model.
fails bit a — no named computational result to mirror
No result has a computational mirror: Theorem 1 and Proposition 4 give economic implementation conditions, while Proposition 2 establishes economic implementation impossibility. Proposition 3 supplies an explicit transfer rule, but no computational objective or complexity claim.
The strongest honest positive case is an application-level one, but it does not meet the paper’s required anchor test. A large municipality choosing a public facility is a plausible high-multiplicity setting: a type would specify residents’ preferences and their complete investment-cost technology, and \(\mu_t\) would be the fraction of residents of type \(t\). The common project, transfers, and investment incentives would remain the decision problem, with welfare aggregated across types. The paper itself uses a city’s choice of public project as an example, so its authors could recognize this as a natural extension.
There is a serious modeling weakness: in an atomless population, one resident’s report or investment cannot change the aggregate choice. Full implementation may therefore become trivial or behave very differently from the finite-agent game. Treating a coordinated group as a strategic unit could preserve pivotality, but that changes the strategic model and needs justification.
More decisively, the paper has no named computational-complexity result of the kind required for an anchor. Theorem 1, proved here, says that an efficient social choice rule is implementable in subgame-perfect equilibria for every \(\delta\in(0,1)\) if and only if its unique associated allocation and transfer rules exist and are commitment-proof. Proposition 2, also proved here, says that without ex post investments, no efficient social choice rule is implementable in subgame-perfect equilibria. Proposition 4 gives a sufficient condition for implementation in perfect Bayesian equilibrium under incomplete information. These are results about economic implementability, not running time, hardness, or algorithmic complexity.
So there are zero eligible anchors, and I cannot state a continuous computational problem per anchor without inventing one. The public-project setting supports a plausible continuous *economic implementation* extension; it does not give this paper a qualifying continuous-complexity mirror.
The strongest case against a ChoCo mirror is that this paper has no computational result to anchor one. Theorem 1 characterizes economic implementability through commitment-proofness; Proposition 2 gives an impossibility result; Proposition 4 gives a sufficient condition under incomplete information. None states a running time, hardness result, or algorithm. They also give no finite encoding of the cost functions and valuation domains from which to define input size. Calling one of these a computational anchor would stretch the programme’s criterion.
The city choosing a public facility is a plausible high-multiplicity setting, as the proponent says. The best attempt to turn it into a computational question would be to ask whether commitment-proof transfers can be found for a finite-support distribution of resident types. But that is a new transfer-design problem, not a continuous mirror of a computational result in this paper. With unrestricted transfers, Proposition 3 already gives an explicit budget-balanced, commitment-proof rule for any efficient allocation rule. Adding transfer limits or other design constraints might create an algorithmic problem, but those constraints would supply the substance of the new question.
There is also a deeper modeling change. In an atomless population, one resident’s investment or report has zero effect on the aggregate valuation used to choose the project. The link between individual investment and pivotal influence that motivates the paper’s implementation problem therefore disappears. Making an entire type cohort move together could restore that influence, but it treats the cohort as a coordinated strategic player. The paper’s equilibrium concept gives incentives to individual agents; it does not supply that coordination assumption.
A finite, high-multiplicity version could preserve individual strategic behavior, and researchers might find its mechanism design questions worthwhile. The negative case is weaker there: a sufficiently specific finite encoding and computational objective could yield a valuable new project. But absent that added structure, the paper offers a plausible population story and economic characterizations, not a computational question about a continuous society that fits ChoCo’s remit.
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.