| paper | d4-conf-atal-2014-0200 |
| 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 has no numbered Theorem, Lemma, Corollary, or Proposition asserting an algorithmic or complexity result to anchor a mirror. Its control algorithm and numerical Gillespie analysis do not meet that requirement; the credible continuous state-mass model does not supply the missing computational result.
fails bit a — no named computational result to mirror
No named computational result is available to mirror. The paper's ODE over state proportions, finite-size master-equation analysis, and simulations remain outside the proposed computational mirror.
The strongest positive case is the regime: a large, homogeneous swarm of robots running the same controller, with \(N\) agents and only four relevant types—dancing or surveying for \(A\) or \(B\). The mass of each type is its fraction of the swarm, and the shared parameters \(g\), \(q\), and the site qualities govern the transitions. That is a credible high-multiplicity setting, and the authors would recognize those masses as describing their system.
But I cannot give a qualifying anchor or a prover-ready continuous problem per anchor. The paper contains no numbered Theorem, Lemma, Corollary, or Proposition asserting a computational-complexity result. Its thermodynamic-limit consensus claim in Section 3 is an analytic result stated in prose; its finite-size performance results rely on numerical solutions and simulations.
There is also a complication for a positive mirror: the paper already models the population continuously through the ODE on \(w_A,w_B,s_A,s_B\). Restating that model would not be a new mirror. A computational design problem built on it might be a plausible follow-up, but it would not be anchored in a named computational result of this paper. So the regime supports continuization, but the requested positive complexity case has no qualifying anchor.
The strongest negative case is that this paper supplies no computational result to mirror. It gives a control algorithm for agents, then studies its dynamics: an ODE in the thermodynamic limit, a finite-size master equation solved numerically, and agent-based simulations. Its consensus and performance claims are analytic or empirical. It does not pose a computational social choice problem with an input, an algorithmic task, and a complexity result.
The proponent raises no qualifying anchor either. Their four-type description is a sensible high-multiplicity regime: identical robots can be grouped by their controller state. But the paper already uses those state proportions in its ODE. Treating that model as the continuous mirror would restate the paper’s analytic treatment, not open the computational question ChoCo targets.
The best alternative would be to ask how a designer should choose \(g\) and \(q\) to meet accuracy, time, or resource constraints. In the paper’s well-mixed binary model, that question appears too slight to anchor a complexity programme: when \(\rho_A>\rho_B\), the ODE converges to the correct consensus from any initial state with some support for \(A\), while \(g\) and \(q\) chiefly control timing and the final distribution between the nest and sites. The interesting accuracy curves instead come from finite-swarm fluctuations. In the thermodynamic limit those fluctuations vanish; retaining them means studying the finite-\(N\) master equation the paper already solves numerically, or adding a finite-size stochastic approximation.
A richer model of spatial interactions or noisy sensing might yield difficult computational design questions. But that would introduce a new problem, rather than mirror a computational result in this paper. The swarm is a convincing high-multiplicity system; the difficulty is that its paper offers dynamics and performance analysis, not a computational result for a continuous population to carry into ChoCo’s complexity landscape.
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.