On the role of collective sensing and evolution in group formation
Authored by Stefano Bennati
Date Published: 2018
DOI: 10.1007/s11721-018-0156-y
Sponsors:
European Union
Platforms:
C++
Model Documentation:
Other Narrative
Model Code URLs:
Model code not found
Abstract
Collective sensing is an emergent phenomenon which enables individuals
to estimate a hidden property of the environment through the observation
of social interactions. Previous work on collective sensing shows that
gregarious individuals obtain an evolutionary advantage by exploiting
collective sensing when competing against solitary individuals. This
work addresses the question of whether collective sensing allows for the
emergence of groups from a population of individuals without
predetermined behaviors. It is assumed that group membership does not
lessen competition on the limited resources in the environment, e.g.,
groups do not improve foraging efficiency. Experiments are run in an
agent-based evolutionary model of a foraging task, where the fitness of
the agents depends on their foraging strategy. The foraging strategy of
agents is determined by a neural network, which does not require
explicit modeling of the environment and of the interactions between
agents. Experiments demonstrate that gregarious behavior is not the
evolutionary-fittest strategy if resources are abundant, thus
invalidating previous findings in a specific region of the parameter
space. In other words, resource scarcity makes gregarious behavior so
valuable as to make up for the increased competition over the few
available resources. Furthermore, it is shown that a population of
solitary agents can evolve gregarious behavior in response to a sudden
scarcity of resources, thus individuating a possible mechanism that
leads to gregarious behavior in nature. The evolutionary process
operates on the whole parameter space of the neural networks; hence,
these behaviors are selected among an unconstrained set of behavioral
models.
Tags
Agent-based modeling
Multi-agent systems
Cooperation
Foraging
birds
neural network
Natural selection
Individuals
Public information
Collective sensing