Collective olfactory search in a turbulent environment
Mihir Durve, Lorenzo Piro, Massimo Cencini, Luca Biferale, Antonio, Celani

TL;DR
This paper investigates how groups of agents can efficiently perform olfactory search in turbulent environments by optimally combining private odor and wind information with public group data, leading to faster source localization.
Contribution
It introduces a model inspired by moth behavior showing the optimal way to blend private and public information for collective olfactory search.
Findings
Optimal information blending minimizes search time.
Discarding public information slows down the search.
Proper weighting of private info improves group performance.
Abstract
Finding the distant source of an odor dispersed by a turbulent flow is a vital task for many organisms, either for foraging or for mating purposes. At the level of individual search, animals like moths have developed effective strategies to solve this very difficult navigation problem based on the noisy detection of odor concentration and wind velocity alone. When many individuals concurrently perform the same olfactory search task, without any centralized control, sharing information about the decisions made by the members of the group can potentially increase the performance. But how much of this information is actually valuable and exploitable for the collective task ? Here we show that, in a model of a swarm of agents inspired by moth behavior, there is an optimal way to blend the private information about odor and wind detections with the publicly available information about…
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