Visual social information use in collective foraging

摘要

Abstract Collective dynamics emerge from individual-level decisions, yet we still poorly understand the link between individual-level decision-making processes and collective outcomes in realistic physical systems. Using collective foraging to study the key trade-off between personal and social information use, we present a mechanistic, spatially-explicit agent-based model that combines individual-level evidence accumulation of personal and (visual) social cues with particle-based movement. Under idealized conditions without physical constraints, our mechanistic framework reproduces findings from established probabilistic models, but explains how individual-level decision processes generate collective outcomes in a bottom-up way. In clustered environments, groups performed best if agents reacted strongly to social information, while in uniform environments, individualistic search was most beneficial. Incorporating different real-world physical and perceptual constraints profoundly shaped collective performance, and could even buffer maladaptive herding by facilitating self-organized exploration. Our study uncovers the mechanisms linking individual cognition to collective outcomes in human and animal foraging and paves the way for decentralized robotic applications. Significance statement Finding and collecting rewards in heterogeneous environments is key for adaptive collective behavior in humans, animals and machines. We present an open agent-based simulation framework to study how social information use shapes collective foraging from the bottom up. Our model combines individual evidence accumulation with spatially explicit movement. Our results connect individual-level decisions to collective dynamics in realistic physical environments, highlighting the key role of real-world constraints, thereby bringing us closer to embodied collective intelligence. Our work introduces a flexible platform to study the interplay between individual cognitive and perceptual biases, agents’ physical environment and the resulting collective dynamics and thus paves the way for fully decentralized mobile robot applications.