Intermediate Interaction Strategies for Collective Behavior
Y. Kikuchi, M. Iwamoto

TL;DR
This paper introduces a new 3D self-propelled particle model combining metric and topological interactions, revealing that a balanced mix enhances collective order and robustness in biological and engineered systems.
Contribution
It presents a novel mixed-interaction SPP model that integrates metric and topological cues, bridging traditional modeling approaches and improving collective behavior robustness.
Findings
Balanced metric-topological interactions maximize global order.
Particles form internally aligned sub-flocks even with low overall order.
The model enhances robustness to density variations.
Abstract
From bird flocks and fish schools to migrating cell sheets, collective motion is a ubiquitous biological phenomenon that inspires quantitative modeling through self-propelled particle (SPP) frameworks. Conventional SPP models prescribe either distance-based (metric) or rank-based (topological) interactions; however, empirical studies indicate that real groups may blend both types of interaction. Motivated by this graded perception, we introduce a new three-dimensional SPP model in which metric and topological alignments act simultaneously and are weighted by a single tunable mixing parameter called the interaction parameter. Large-scale simulations spanning a wide ranges of interaction parameters and densities revealed rich dynamics. Even when the global order parameter is low, cluster-level analysis with HDBSCAN shows that particles self-organize into several spatially distinct but…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
