TY - JOUR
T1 - Collective learning from individual experiences and information transfer during group foraging
AU - Falcón-Cortés, Andrea
AU - Boyer, Denis
AU - Ramos-Fernández, Gabriel
N1 - Publisher Copyright:
© 2019 The Author(s) Published by the Royal Society. All rights reserved.
PY - 2019/2/1
Y1 - 2019/2/1
N2 - Living in groups brings benefits to many animals, such as protection against predators and an improved capacity for sensing and making decisions while searching for resources in uncertain environments. A body of studies has shown how collective behaviours within animal groups on the move can be useful for pooling information about the current state of the environment. The effects of interactions on collective motion have been mostly studied in models of agents with no memory. Thus, whether coordinated behaviours can emerge from individuals with memory and different foraging experiences is still poorly understood. By means of an agent-based model, we quantify how individual memory and information fluxes can contribute to improving the foraging success of a group in complex environments. In this context, we define collective learning as a coordinated change of behaviour within a group resulting from individual experiences and information transfer. We show that an initially scattered population of foragers visiting dispersed resources can gradually achieve cohesion and become selectively localized in space around the most salient resource sites. Coordination is lost when memory or information transfer among individuals is suppressed. The present modelling framework provides predictions for empirical studies of collective learning and could also find applications in swarm robotics and motivate new search algorithms based on reinforcement.
AB - Living in groups brings benefits to many animals, such as protection against predators and an improved capacity for sensing and making decisions while searching for resources in uncertain environments. A body of studies has shown how collective behaviours within animal groups on the move can be useful for pooling information about the current state of the environment. The effects of interactions on collective motion have been mostly studied in models of agents with no memory. Thus, whether coordinated behaviours can emerge from individuals with memory and different foraging experiences is still poorly understood. By means of an agent-based model, we quantify how individual memory and information fluxes can contribute to improving the foraging success of a group in complex environments. In this context, we define collective learning as a coordinated change of behaviour within a group resulting from individual experiences and information transfer. We show that an initially scattered population of foragers visiting dispersed resources can gradually achieve cohesion and become selectively localized in space around the most salient resource sites. Coordination is lost when memory or information transfer among individuals is suppressed. The present modelling framework provides predictions for empirical studies of collective learning and could also find applications in swarm robotics and motivate new search algorithms based on reinforcement.
KW - Collective motion
KW - Foraging
KW - Learning processes
KW - Memory random walks
UR - http://www.scopus.com/inward/record.url?scp=85062783781&partnerID=8YFLogxK
U2 - 10.1098/rsif.2018.0803
DO - 10.1098/rsif.2018.0803
M3 - Artículo
C2 - 30958179
SN - 1742-5689
VL - 16
JO - Journal of the Royal Society Interface
JF - Journal of the Royal Society Interface
IS - 151
M1 - 20180803
ER -