A simple method for detecting chaos in nature

Toker, Daniel; Sommer, Friedrich T.; D’Esposito, Mark

doi:10.1038/s42003-019-0715-9

Cited by 127 publications

(115 citation statements)

References 95 publications

(209 reference statements)

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“…In this regard, we note that examining the NY daily death count data studied in Ref. Chin et al (2020) , these data are found to be characterized as stochastic rather than chaotic ( Toker, Sommer, & D’Esposito, 2000 ). Taleb seems to fit an unorthodox model, and then abandons all effort to predict anything.…”

Section: Further Thoughts – Analogies Decisions Of Action and Maximmentioning

confidence: 78%

Forecasting for COVID-19 has failed

Ioannidis

Cripps

Tanner

2022

International Journal of Forecasting

317

271

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Epidemic forecasting has a dubious track-record, and its failures became more prominent with COVID-19. Poor data input, wrong modeling assumptions, high sensitivity of estimates, lack of incorporation of epidemiological features, poor past evidence on effects of available interventions, lack of transparency, errors, lack of determinacy, looking at only one or a few dimensions of the problem at hand, lack of expertise in crucial disciplines, groupthink and bandwagon effects and selective reporting are some of the causes of these failures. Nevertheless, epidemic forecasting is unlikely to be abandoned. Some (but not all) of these problems can be fixed. Careful modeling of predictive distributions rather than focusing on point estimates, considering multiple dimensions of impact, and continuously reappraising models based on their validated performance may help. If extreme values are considered, extremes should be considered for the consequences of multiple dimensions of impact so as to continuously calibrate predictive insights and decision-making. When major decisions (e.g. draconian lockdowns) are based on forecasts, the harms (in terms of health, economy, and society at large) and the asymmetry of risks need to be approached in a holistic fashion, considering the totality of the evidence.

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Section: Further Thoughts – Analogies Decisions Of Action and Maximmentioning

confidence: 78%

Forecasting for COVID-19 has failed

Ioannidis

Cripps

Tanner

2022

International Journal of Forecasting

317

271

View full text Add to dashboard Cite

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“…Chaos, which is the exponential sensitivity to small perturbations, is a ubiquitous phenomenon in nature [258,259]. Furthermore, chaos is related to how a deterministic dynamical system can be potentially unpredictable due to an extreme sensitivity to initial conditions, which is also known as the "Butterfly Effect" [259].…”

Section: Chaos Analysismentioning

confidence: 99%

A Review of the Serrated-Flow Phenomenon and Its Role in the Deformation Behavior of High-Entropy Alloys

Brechtl

Chen

Lee

et al. 2020

Metals

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High-entropy alloys (HEAs) are a novel class of alloys that have many desirable properties. The serrated flow that occurs in high-entropy alloys during mechanical deformation is an important phenomenon since it can lead to significant changes in the microstructure of the alloy. In this article, we review the recent findings on the serration behavior in a variety of high-entropy alloys. Relationships among the serrated flow behavior, composition, microstructure, and testing condition are explored. Importantly, the mechanical-testing type (compression/tension), testing temperature, applied strain rate, and serration type for certain high-entropy alloys are summarized. The literature reveals that the serrated flow can be affected by experimental conditions such as the strain rate and test temperature. Furthermore, this type of phenomenon has been successfully modeled and analyzed, using several different types of analytical methods, including the mean-field theory formalism and the complexity-analysis technique. Importantly, the results of the analyses show that the serrated flow in HEAs consists of complex dynamical behavior. It is anticipated that this review will provide some useful and clarifying information regarding the serrated-flow mechanisms in this material system. Finally, suggestions for future research directions in this field are proposed, such as the effects of irradiation, additives (such as C and Al), the presence of nanoparticles, and twinning on the serrated flow behavior in HEAs.

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“…When a single transcription factor, NF-kB, a well-described transcriptional regulator in cancer networks was modelled as a periodically forced nonlinear oscillator, chaotic dynamics emerged beyond a critical amplitude of TNF (Tumor Necrosis Factor) (Heltberg et al, 2019 [89]). Recently, these findings were confirmed with the Chaos Decision Tree algorithm, demonstrating the applicability of machine learning in detecting chaotic gene expression signals (Toker et al, 2020 [91]). Furthermore, chaotic behaviors are often equated with aperiodicity or disordered patterns of gene expression.…”

Section: Nonlinear Dynamics Fractals and Chaosmentioning

confidence: 85%

A Complex Systems Analysis of Cancer Networks

Uthamacumaran¹

2019

Preprint

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Cancers are complex, adaptive ecosystems. It remains the lead cause of disease-related, pediatric death in North America. The emerging field of complexity science has redefined cancer as a computational system with intractable, algorithmic complexity. Herein, a tumor and its heterogeneous phenotypes are discussed as dynamical systems having multiple, chaotic attractors. Machine learning, Network science and information theory are discussed as current tools for cancer network reconstruction. The fluid dynamics of cancer-cell fate transitions and chemical pattern formation are briefly reviewed. Deep Learning architectures, delay-embedding algorithms and computational fluid models are proposed for better forecasting gene expression patterns in cancer ecosystems. Cancer cell decision-making is investigated within the framework of complexity theory.

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A simple method for detecting chaos in nature

Cited by 127 publications

References 95 publications

Forecasting for COVID-19 has failed

Forecasting for COVID-19 has failed

A Review of the Serrated-Flow Phenomenon and Its Role in the Deformation Behavior of High-Entropy Alloys

A Complex Systems Analysis of Cancer Networks

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