Prediction-for-CompAction: Navigation in social environments using generalized cognitive maps

Jose A. Villacorta-Atienza, Carlos Calvo, Valeri A. Makarov

incompletemedium confidence

Category: math.DS
Journal tier: Specialist/Solid
Processed: Sep 28, 2025, 12:56 AM
arXiv Links: Abstract ↗PDF ↗

Audit review

The paper substantiates its claims empirically and heuristically (smaller CoUs obstacles, shorter agent paths; no added social load in one low‑density setting; chain-at-door raises social effort), but provides no formal proofs. The candidate solution supplies a clear, correct geometric inclusion for single-pedestrian obstacles (supporting shorter CoUs paths), and a plausible construction for the chain-at-door phenomenon. However, its Part (2) proof that S_CoUs ≤ S_AvUs in symmetric low-density flows implicitly relaxes the paper’s CoUs rule (it treats the human’s lateral component as adjustable up to v/2 rather than fixed at v/2), leaving a key gap. Hence both the paper and the model are incomplete: the paper lacks formal derivations; the model over-assumes controllability in Part (2).

Referee report (LaTeX)

\textbf{Recommendation:} minor revisions

\textbf{Journal Tier:} specialist/solid

\textbf{Justification:}

The paper integrates predictive neural maps with a simple cooperation heuristic to illuminate when socially cooperative navigation helps or hurts. Its qualitative mechanisms and simulations (reduced effective obstacles under CoUs; necessity in fully blocked corridors; increased social effort in chain-at-door) are convincing, but formal guarantees are not provided. Minor revisions clarifying assumptions, delimiting the scope of empirical claims, and adding brief lemmas (e.g., virtual-obstacle inclusion) would improve rigor without altering the main contributions.