Collective search with finite perception: transient dynamics and search efficiency

TL;DR

This paper introduces a framework combining random walks and optimal control to study how finite perception horizons improve search efficiency and collective sensing in biological organisms during transient dynamics.

Contribution

It develops a novel model integrating finite perception with optimal control, revealing benefits over local strategies in transient search efficiency and collective behavior.

Findings

Finite perception leads to faster convergence in search tasks.

Tuning response sensitivity to environmental scales enhances search efficiency.

Interaction among agents increases consensus and improves collective sensing.

Abstract

Motile organisms often use finite spatial perception of their surroundings to navigate and search their habitats. Yet standard models of search are usually based on purely local sensory information. To model how a finite perceptual horizon affects ecological search, we propose a framework for optimal navigation that combines concepts from random walks and optimal control theory. We show that, while local strategies are optimal on asymptotically long and short search times, finite perception yields faster convergence and increased search efficiency over transient time scales relevant in biological systems. The benefit of the finite horizon can be maintained by the searchers tuning their response sensitivity to the length scale of the stimulant in the environment, and is enhanced when the agents interact as a result of increased consensus within subpopulations. Our framework sheds light on the role of spatial perception and transients in search movement and collective sensing of the environment.