Anticipating critical transitions in psychological systems using early warning signals: Theoretical and practical considerations.

Theory and observation tell us that many complex systems exhibit tipping points—thresholds involving an abrupt and irreversible transition to a contrasting dynamical regime. Such events are commonly referred to as critical transitions. Current research seeks to develop early warning signals (EWS) of critical transitions that could help prevent undesirable events such as ecosystem collapse. However, conventional EWS do not indicate the type of transition, since they are based on the generic phenomena of critical slowing down. For instance, they may fail to distinguish the onset of oscillations (e.g. Hopf bifurcation) from a transition to a distant attractor (e.g. Fold bifurcation). Moreover, conventional EWS are less reliable in systems with density-dependent noise. Other EWS based on the power spectrum (spectral EWS) have been proposed, but they rely upon spectral reddening, which does not occur prior to critical transitions with an oscillatory component. Here, we use Ornstein–Uhlenbeck theory to derive analytic approximations for EWS prior to each type of local bifurcation, thereby creating new spectral EWS that provide greater sensitivity to transition proximity; higher robustness to density-dependent noise and bifurcation type; and clues to the type of approaching transition. We demonstrate the advantage of applying these spectral EWS in concert with conventional EWS using a population model, and show that they provide a characteristic signal prior to two different Hopf bifurcations in data from a predator–prey chemostat experiment. The ability to better infer and differentiate the nature of upcoming transitions in complex systems will help humanity manage critical transitions in the Anthropocene Era.

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Eigenvalues of the covariance matrix as early warning signals for critical transitions in ecological systems

Scientific Reports ◽

10.1038/s41598-019-38961-5 ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 5

Author(s):

Shiyang Chen ◽

Eamon B. O’Dea ◽

John M. Drake ◽

Bogdan I. Epureanu

Keyword(s):

Covariance Matrix ◽

Early Warning ◽

Ecological Systems ◽

Warning Signals ◽

Early Warning Signals ◽

Critical Transitions

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How one might miss early warning signals of critical transitions in time series data: A systematic study of two major currency pairs

PLoS ONE ◽

10.1371/journal.pone.0191439 ◽

2018 ◽

Vol 13 (3) ◽

pp. e0191439 ◽

Cited By ~ 7

Author(s):

Haoyu Wen ◽

Massimo Pica Ciamarra ◽

Siew Ann Cheong

Keyword(s):

Time Series ◽

Systematic Study ◽

Early Warning ◽

Time Series Data ◽

Series Data ◽

Warning Signals ◽

Early Warning Signals ◽

Critical Transitions

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Performance of early warning signals for disease emergence: a case study on COVID-19 data

10.1101/2021.03.30.21254631 ◽

2021 ◽

Author(s):

Daniele Proverbio ◽

Françoise Kemp ◽

Stefano Magni ◽

Jorge Gonçalves

Keyword(s):

Early Warning ◽

Disease Emergence ◽

Warning Signals ◽

Active Monitoring ◽

Prevalence Data ◽

Early Warning Signals ◽

Epidemic Dynamics ◽

Critical Transitions ◽

Parameter Values

AbstractThe sudden emergence of infectious diseases pose threats to societies worldwide and it is notably difficult to detect. In the past few years, several early warning signals (EWS) were introduced, to alert to impending critical transitions and extend the set of indicators for risk assessment. While they were originally thought to be generic, recent works demonstrated their sensitivity to some dynamical characteristics such as system noise and rates of approach to critical parameter values. Moreover, testing on empirical data is so far limited. Hence, validating their performance remains a challenge. In this study, we analyse the performance of common EWS such as increasing variance and autocorrelation in detecting the emergence of COVID-19 outbreaks in various countries, based on prevalence data. We show that EWS are successful in detecting disease emergence provided that some basic assumptions are satisfied: a slow forcing through the transitions and not fat-tailed noise. We also show cases where EWS fail, thus providing a verification analysis of their potential and limitations. Overall, this suggests that EWS can be useful for active monitoring of epidemic dynamics, but that their performance is sensitive to surveillance procedures. Our results thus represent a further step towards the application of EWS indicators for informing public health policies.

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Early warning signals from the periphery

Journal of Computational Social Science ◽

10.1007/s42001-021-00142-8 ◽

2021 ◽

Author(s):

Manfred Füllsack ◽

Daniel Reisinger ◽

Marie Kapeller ◽

Georg Jäger

Keyword(s):

Time Series ◽

Social Interactions ◽

Early Warning ◽

Focus Attention ◽

Warning Signals ◽

Feedback Effects ◽

Early Warning Signals ◽

Agent Based ◽

Critical Transitions ◽

Micro Level

AbstractStudies on the possibility of predicting critical transitions with statistical methods known as early warning signals (EWS) are often conducted on data generated with equation-based models (EBMs). These models base on difference or differential equations, which aggregate a system’s components in a mathematical term and therefore do not allow for a detailed analysis of interactions on micro-level. As an alternative, we suggest a simple, but highly flexible agent-based model (ABM), which, when applying EWS-analysis, gives reason to (a) consider social interaction, in particular negative feedback effects, as an essential trigger of critical transitions, and (b) to differentiate social interactions, for example in network representations, into a core and a periphery of agents and focus attention on the periphery. Results are tested against time series from a networked version of the Ising-model, which is often used as example for generating hysteretic critical transitions.

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Data-driven identification of reliable sensor species to predict regime shifts in ecological networks

Royal Society Open Science ◽

10.1098/rsos.200896 ◽

2020 ◽

Vol 7 (8) ◽

pp. 200896 ◽

Cited By ~ 1

Author(s):

Amin Ghadami ◽

Shiyang Chen ◽

Bogdan I. Epureanu

Keyword(s):

Indicator Species ◽

Early Warning ◽

Regime Shifts ◽

Data Driven ◽

Warning Signals ◽

Early Warning Signals ◽

Critical Transitions ◽

Spatially Distributed ◽

Data Driven Approach ◽

Interacting Components

Signals of critical slowing down are useful for predicting impending transitions in ecosystems. However, in a system with complex interacting components not all components provide the same quality of information to detect system-wide transitions. Identifying the best indicator species in complex ecosystems is a challenging task when a model of the system is not available. In this paper, we propose a data-driven approach to rank the elements of a spatially distributed ecosystem based on their reliability in providing early-warning signals of critical transitions. The proposed method is rooted in experimental modal analysis techniques traditionally used to identify structural dynamical systems. We show that one could use natural system fluctuations and the system responses to small perturbations to reveal the slowest direction of the system dynamics and identify indicator regions that are best suited for detecting abrupt transitions in a network of interacting components. The approach is applied to several ecosystems to demonstrate how it successfully ranks regions based on their reliability to provide early-warning signals of regime shifts. The significance of identifying the indicator species and the challenges associated with ranking nodes in networks of interacting components are also discussed.

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Dynamics of sleep: Exploring critical transitions and early warning signals

Computer Methods and Programs in Biomedicine ◽

10.1016/j.cmpb.2020.105448 ◽

2020 ◽

Vol 193 ◽

pp. 105448

Author(s):

Susanne M.M. de Mooij ◽

Tessa F. Blanken ◽

Raoul P.P.P. Grasman ◽

Jennifer R. Ramautar ◽

Eus J.W. Van Someren ◽

...

Keyword(s):

Early Warning ◽

Warning Signals ◽

Early Warning Signals ◽

Critical Transitions

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Several Indicators of Critical Transitions for Complex Diseases Based on Stochastic Analysis

Computational and Mathematical Methods in Medicine ◽

10.1155/2017/7560758 ◽

2017 ◽

Vol 2017 ◽

pp. 1-10 ◽

Cited By ~ 1

Author(s):

Gang Wang ◽

Yuanyuan Li ◽

Xiufen Zou

Keyword(s):

Early Warning ◽

Self Assembly ◽

Disease Onset ◽

Complex Diseases ◽

Critical Threshold ◽

Warning Signals ◽

Early Warning Signals ◽

Critical Transitions ◽

Development And Differentiation ◽

Quantitative Changes

Many complex diseases (chronic disease onset, development and differentiation, self-assembly, etc.) are reminiscent of phase transitions in a dynamical system: quantitative changes accumulate largely unnoticed until a critical threshold is reached, which causes abrupt qualitative changes of the system. Understanding such nonlinear behaviors is critical to dissect the multiple genetic/environmental factors that together shape the genetic and physiological landscape underlying basic biological functions and to identify the key driving molecules. Based on stochastic differential equation (SDE) model, we theoretically derive three statistical indicators, that is, coefficient of variation (CV), transformed Pearson’s correlation coefficient (TPC), and transformed probability distribution (TPD), to identify critical transitions and detect the early-warning signals of the phase transition in complex diseases. To verify the effectiveness of these early-warning indexes, we use high-throughput data for three complex diseases, including influenza caused by either H3N2 or H1N1 and acute lung injury, to extract the dynamical network biomarkers (DNBs) responsible for catastrophic transition into the disease state from predisease state. The numerical results indicate that the derived indicators provide a data-based quantitative analysis for early-warning signals for critical transitions in complex diseases or other dynamical systems.

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