Modelling collective motion based on the principle of agency: General framework and the case of marching locusts release_uhqstqcvlzcppk5xay6hhv2nwu

by Katja Ried, Thomas Müller, Hans J. Briegel

Published in PLoS ONE by Public Library of Science (PLoS).

2019   Volume 14, Issue 2, e0212044

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.
In text/plain format

Archived Files and Locations

application/pdf  2.0 MB
file_jmvmdbipobehpjbdxrvf34v7xu
web.archive.org (webarchive)
kops.uni-konstanz.de (web)
application/pdf  2.1 MB
file_b6372ymdwngepjffkfnsnnv43m
web.archive.org (webarchive)
journals.plos.org (publisher)
Read Archived PDF
Preserved and Accessible
Type  article-journal
Stage   published
Date   2019-02-20
Language   en ?
Container Metadata
Open Access Publication
In DOAJ
Not in Keepers Registry
ISSN-L:  1932-6203
Work Entity
access all versions, variants, and formats of this works (eg, pre-prints)
Catalog Record
Revision: 047da7f1-35bd-4635-9933-4be0d845a85c
API URL: JSON