Modelling collective motion based on the principle of agency: General framework and the case of marching locusts
Authored by Katja Ried, Thomas Mueller, Hans J Briegel
Date Published: 2019
DOI: 10.1371/journal.pone.0212044
Sponsors:
Austrian Science Fund (FWF)
Platforms:
Python
Model Documentation:
Other Narrative
Mathematical description
Model Code URLs:
https://projectivesimulation.org/files/ps-code/environments/env_locust.py
Abstract
Collective phenomena are studied in a range of contexts-from controlling
locust plagues to efficiently evacuating stadiums-but the central
question remains: how can a large number of independent individuals form
a seemingly perfectly coordinated whole? Previous attempts to answer
this question have reduced the individuals to featureless particles,
assumed particular interactions between them and studied the resulting
collective dynamics. While this approach has provided useful insights,
it cannot guarantee that the assumed individual-level behaviour is
accurate, and, moreover, does not address its origin-that is, the
question of why individuals would respond in one way or another. We
propose a new approach to studying collective behaviour, based on the
concept of learning agents: individuals endowed with explicitly modelled
sensory capabilities, an internal mechanism for deciding how to respond
to the sensory input and rules for modifying these responses based on
past experience. This detailed modelling of individuals favours a more
natural choice of parameters than in typical swarm models, which
minimises the risk of spurious dependences or overfitting. Most notably,
learning agents need not be programmed with particular responses, but
can instead develop these autonomously, allowing for models with fewer
implicit assumptions. We illustrate these points with the example of
marching locusts, showing how learning agents can account for the
phenomenon of density-dependent alignment. Our results suggest that
learning agent-based models are a powerful tool for studying a broader
class of problems involving collective behaviour and animal agency in
general.
Tags
Simulation
Mechanism
Swarms