| paper | d4-journals-geb-2015-0073 |
| authors | — |
| venue | ? |
| filed under | unclassified |
| judged by | gpt-6-luna / xhigh (triple__luna__xhigh__c6r1) |
| judge confidence | high |
| authors would recognise it | no |
Each cited proposition is a welfare comparative-static theorem, and the equilibrium thresholds are derived without a stated algorithm, running-time bound, hardness result, or computation-using formulation. The paper already models a continuum of voters and politicians, but the decisive failure is that it has no named computational result to mirror.
fails bit a — no named computational result to mirror
No mirror covers a named result; Propositions 1–3 and the paper’s other equilibrium and welfare results remain analytical.
I cannot make an honest anchored case for a continuous computational mirror from this paper. Its society is already a continuum: voters and politicians are distributed over ideology, and challenger ideology is drawn from a density. The paper studies equilibrium thresholds and voter welfare analytically.
The named results do not provide the required computational anchors. Propositions 1 and 2 establish when moderation of the ideology distribution raises or lowers voter welfare; Proposition 3 shows that, under its stated patience condition, sufficiently noisy informative signals strictly reduce voter welfare. These are comparative statics, not results that a problem is in P, NP-hard, FPT, or otherwise computationally classified. The equilibrium existence and uniqueness result cited from Bernhardt et al. (2009) is also not a complexity result.
One could formulate a new computational question—say, how to compute the equilibrium thresholds and welfare from a rationally represented ideology distribution. But that would add a computational problem the paper does not analyze, and the population-continuum move would already be present in the original model. It is therefore not a defensible mirror anchored in this paper’s results under the programme’s criteria.
There is no named computational result here to continuize. Propositions 1 and 2 are comparative statics about how changes in the ideology distribution affect welfare; Proposition 3 is a welfare result about signal accuracy. The thresholds \(w\) and \(c\) characterize equilibrium behavior, but the paper presents no complexity claim or algorithmic problem. That leaves no result for a computational mirror to preserve.
The strongest possible reconstruction from Propositions 1 and 2 would be to represent candidate ideologies as types and ask for a minimum-cost mass transfer that changes voter welfare. But the paper specifies no transfer costs, budget, or feasible set. Adding those would create a new distribution-design problem, not computationally recast the propositions. Simply computing equilibrium and welfare for a finitely represented distribution is closer to the paper, but its starting model already has continuous voter and politician populations and studies distributions over ideology. That computation would be a new algorithmic question about an already continuous model, not the population continuization this programme studies.
Proposition 3 offers no stronger anchor. Its signal is public: all voters observe the same good or bad realization. Treating signal accuracy as the object to optimize would continuize information or campaign design, not the population. A heterogeneous audience or campaign-exposure model could be added, but then the result would concern a different model.
The negative case is strongest against a *mirror of this paper’s named results*. I cannot establish the broader claim that no worthwhile computational project could be built nearby. In particular, a costed candidate-recruitment problem might be interesting, but the paper does not supply that problem, and its existing continuous population means it would not be a continuization of a discrete one.
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.