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- Title
Visual social information use in collective foraging.
- Authors
Mezey, David; Deffner, Dominik; Kurvers, Ralf H. J. M.; Romanczuk, Pawel
- 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. Author summary: Finding and collecting rewards in ever-changing environments is key for adaptive collective behavior in humans, animals and machines. We present an agent-based simulation framework to study how individuals of groups use social information during foraging together and how this social information use shapes the collective outcome through the behavior of single individuals. Our model combines models of individual decision-making of foraging agents (evidence accumulation processes) with the movement models of these individuals in space. Our results connect decisions of individuals to group dynamics and collective outcomes in realistic physical environments, highlighting the key role of the laws of real-world constraints, 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 also paves the way for fully decentralized mobile robot applications.
- Subjects
GROUP dynamics; SOCIAL cues; COGNITIVE bias; SWARM intelligence; DECISION making; MOBILE robots; REINFORCEMENT learning
- Publication
PLoS Computational Biology, 2024, Vol 20, Issue 5, p1
- ISSN
1553-734X
- Publication type
Article
- DOI
10.1371/journal.pcbi.1012087