Exploring the potential of collective learning to reduce foraging time

Animal groups collaborate with one another throughout their lives to better comprehend their surroundings. Here, we try to model, using continuous random walks, how the entire life process and collective learning impact the searching process. We attempt to simulate an ecosystem where the post-reproductive foragers leave their colonies to find the targets while others stay and breed at the base. That is to say, a group of foragers searches for a location where they can access the targets efficiently. Particularly, we have explored a hypothetical situation in which the relocation to the new position depends on the agreement level of the species as well as an additional waiting time due to this agreement level. In this backdrop, detailed numerical results reveal that the expected foraging time attains minima for a suitable range of the agreement level. We have also shown that the expected foraging time linearly increases with the death-to-birth ratio for a given agreement level.