| paper | d4-journals-geb-2005-0007 |
| authors | — |
| venue | ? |
| filed under | unclassified |
| judged by | gpt-6-luna / xhigh (triple__luna__xhigh__c6r1) |
| judge confidence | high |
| authors would recognise it | no |
The named theorems characterize or rule out mechanisms by axioms; none gives an algorithm, a computational formulation used to compute, or a complexity result. A continuum of households could be a sensible risk-sharing model, but computing allocations for it would add a computational task that this paper does not state.
fails bit a — no named computational result to mirror
No result qualifies as a computational anchor; the theorems address axiomatic characterizations and impossibilities.
I don’t find a qualifying computational anchor in this paper. The numbered results are mechanism-design characterizations and impossibility theorems: Theorem 1 characterizes efficient, individually rational, strategy-proof rules under aggregate certainty; Theorems 2 and 3 give impossibility and dictatorship results; Theorems 4 and 5 address different beliefs and multiple goods. None asserts that a computational problem is in P, NP-hard, parameterized-hard, or otherwise classifies its complexity.
A continuous population model is imaginable: agents’ complete types could include their preferences and state-contingent endowments, with mass recording each type’s population share. But the paper asks whether a *rule over an entire domain of economies* satisfies axioms. Turning that into a per-instance allocation algorithm would change the question. And in a nonatomic population, an individual’s report may have zero effect on the aggregate, making the paper’s individual strategy-proofness condition potentially vacuous.
So I can’t honestly state a continuous problem “per anchor” under your criteria. The paper contains no named computational result to anchor that case to.
The proponent’s strongest point is decisive for ChoCo: the paper has no named computational result to mirror. Theorems 1–5 characterize which rules satisfy axioms or show that certain rules cannot exist. They do not analyze an algorithm’s running time or classify a finite-input computational problem. A “rule” here is a function over an entire domain of economies, and the paper gives no finite encoding for its utility functions or for such functions.
There is a plausible high-multiplicity setting: a large insurance pool with many households sharing the same preference and state-contingent endowment. A type could include both, and a society could be a rational distribution over finitely many types. The allocations would then be per-capita state-contingent bundles. That is a sensible continuous economy; the issue is that it still does not turn the paper’s results into computational results. Computing an allocation for one distribution, deciding whether a rule satisfies the axioms over a domain, and finding a rule with those properties are different tasks. The paper studies the latter two as mechanism-design questions, without specifying finite inputs or algorithms.
The strategy-proofness concern can also be handled in a stronger mirror. A nonatomic direct mechanism could specify an allocation for an individual as a function of that individual’s report and the fixed population distribution. Then an infinitesimal agent’s report need not make the incentive constraint vacuous. But this preserves an axiomatic incentive question; it does not supply a computational problem from the paper. Preserving strategic influence through finite cohorts or positive-mass groups would likewise require choosing a new strategic model and encoding.
One could formulate a separate algorithmic project around risk sharing—for example, computing allocations from finite-support, explicitly encoded utility functions. That may be worthwhile in its own right. But it adds a computational task that this paper does not state or classify. The negative case is therefore strong on fit to ChoCo, though it cannot establish that no related continuous mechanism-design project would be valuable elsewhere.
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.