In:
PLOS ONE, Public Library of Science (PLoS), Vol. 16, No. 11 ( 2021-11-4), p. e0259177-
Abstract:
Dynamical systems can be subject to critical transitions where a system’s state abruptly shifts from one stable equilibrium to another. To a certain extent such transitions can be predicted with a set of methods known as early warning signals. These methods are often developed and tested on systems simulated with equation-based approaches that focus on the aggregate dynamics of a system. Many ecological phenomena however seem to necessitate the consideration of a system’s micro-level interactions since only there the actual reasons for sudden state transitions become apparent. Agent-based approaches that simulate systems from the bottom up by explicitly focusing on these micro-level interactions have only rarely been used in such investigations. This study compares the performance of a bifurcation estimation method for predicting state transitions when applied to data from an equation-based and an agent-based version of the Ising-model. The results show that the method can be applied to agent-based models and, despite its greater stochasticity, can provide useful predictions about state changes in complex systems.
Type of Medium:
Online Resource
ISSN:
1932-6203
DOI:
10.1371/journal.pone.0259177
DOI:
10.1371/journal.pone.0259177.g001
DOI:
10.1371/journal.pone.0259177.g002
DOI:
10.1371/journal.pone.0259177.g003
DOI:
10.1371/journal.pone.0259177.g004
DOI:
10.1371/journal.pone.0259177.g005
DOI:
10.1371/journal.pone.0259177.g006
DOI:
10.1371/journal.pone.0259177.g007
DOI:
10.1371/journal.pone.0259177.g008
DOI:
10.1371/journal.pone.0259177.t001
Language:
English
Publisher:
Public Library of Science (PLoS)
Publication Date:
2021
detail.hit.zdb_id:
2267670-3
Bookmarklink