Fear-Driven Collective Topology: Comparing Smart-Boid Vietoris–Rips Graphs to Animal Communication Networks via Persistent Features

We test whether adaptive agents, a.k.a. ``Smart-Boids'' governed by neural networks under evolutionary pressure, can generate topologies resembling animal communication networks. Using Vietoris--Rips filtrations and persistent homology, we compare $1000+$ empirical networks to simulations via feature-based correlations. Minimal ingredients (fear of isolation, limited perception, inertia, exclusion, noise) reproduce both sparse and small-world topologies observed in diverse animal systems. Results suggest that ecological constraints, rather than complex cognition, drive the emergence of communication networks.