Next Article in Journal
A Rapid Improvement Process through “Quick-Win” Lean Tools: A Case Study
Previous Article in Journal
Ethical Regulators and Super-Ethical Systems
Open AccessArticle

Comparing Equation-Based and Agent-Based Data Generation Methods for Early Warning Signal Analysis

Institute of Systems Sciences, Innovation and Sustainability Research, University of Graz, 8010 Graz, Austria
*
Author to whom correspondence should be addressed.
Systems 2020, 8(4), 54; https://doi.org/10.3390/systems8040054
Received: 14 October 2020 / Revised: 20 November 2020 / Accepted: 8 December 2020 / Published: 10 December 2020
(This article belongs to the Special Issue Systems Science)
Dynamical systems are known to exhibit sudden state transitions, with abrupt shifts from one stable state to another. Such transitions are widely observed, with examples ranging from abrupt extinctions of species in ecosystems to unexpected financial crises in the economy or sudden changes in medical conditions. Statistical methods known as early warning signals (EWSs) are used to predict these transitions. In most studies to date, EWSs have been tested on data generated using equation-based methods that represent a system’s aggregate state and thus show limitations in considering the interactions of a system at the component level. Agent-based models offer an alternative without these limitations. This study compares the performance of EWSs when applied to data from an equation-based and from an agent-based version of the Ising model. The results provide a reason to consider agent-based modelling a promising complementary method for investigating the predictability of state changes with EWSs. View Full-Text
Keywords: critical transitions; regime shifts; tipping points; early warning signals; equation-based modelling; agent-based modelling critical transitions; regime shifts; tipping points; early warning signals; equation-based modelling; agent-based modelling
Show Figures

Figure 1

MDPI and ACS Style

Reisinger, D.; Füllsack, M. Comparing Equation-Based and Agent-Based Data Generation Methods for Early Warning Signal Analysis. Systems 2020, 8, 54.

AMA Style

Reisinger D, Füllsack M. Comparing Equation-Based and Agent-Based Data Generation Methods for Early Warning Signal Analysis. Systems. 2020; 8(4):54.

Chicago/Turabian Style

Reisinger, Daniel; Füllsack, Manfred. 2020. "Comparing Equation-Based and Agent-Based Data Generation Methods for Early Warning Signal Analysis" Systems 8, no. 4: 54.

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop