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      Anticipating Economic Market Crises Using Measures of Collective Panic

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          Predicting panic is of critical importance in many areas of human and animal behavior, notably in the context of economics. The recent financial crisis is a case in point. Panic may be due to a specific external threat or self-generated nervousness. Here we show that the recent economic crisis and earlier large single-day panics were preceded by extended periods of high levels of market mimicry—direct evidence of uncertainty and nervousness, and of the comparatively weak influence of external news. High levels of mimicry can be a quite general indicator of the potential for self-organized crises.

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          Most cited references 16

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          Collective dynamics of 'small-world' networks.

          Networks of coupled dynamical systems have been used to model biological oscillators, Josephson junction arrays, excitable media, neural networks, spatial games, genetic control networks and many other self-organizing systems. Ordinarily, the connection topology is assumed to be either completely regular or completely random. But many biological, technological and social networks lie somewhere between these two extremes. Here we explore simple models of networks that can be tuned through this middle ground: regular networks 'rewired' to introduce increasing amounts of disorder. We find that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs. We call them 'small-world' networks, by analogy with the small-world phenomenon (popularly known as six degrees of separation. The neural network of the worm Caenorhabditis elegans, the power grid of the western United States, and the collaboration graph of film actors are shown to be small-world networks. Models of dynamical systems with small-world coupling display enhanced signal-propagation speed, computational power, and synchronizability. In particular, infectious diseases spread more easily in small-world networks than in regular lattices.
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            Early-warning signals for critical transitions.

            Complex dynamical systems, ranging from ecosystems to financial markets and the climate, can have tipping points at which a sudden shift to a contrasting dynamical regime may occur. Although predicting such critical points before they are reached is extremely difficult, work in different scientific fields is now suggesting the existence of generic early-warning signals that may indicate for a wide class of systems if a critical threshold is approaching.
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              Anticipating critical transitions.

              Tipping points in complex systems may imply risks of unwanted collapse, but also opportunities for positive change. Our capacity to navigate such risks and opportunities can be boosted by combining emerging insights from two unconnected fields of research. One line of work is revealing fundamental architectural features that may cause ecological networks, financial markets, and other complex systems to have tipping points. Another field of research is uncovering generic empirical indicators of the proximity to such critical thresholds. Although sudden shifts in complex systems will inevitably continue to surprise us, work at the crossroads of these emerging fields offers new approaches for anticipating critical transitions.

                Author and article information

                Role: Editor
                PLoS One
                PLoS ONE
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                17 July 2015
                : 10
                : 7
                [1 ]New England Complex Systems Institute, Cambridge, MA, United States of America
                [2 ]Universidade Estadual de Campinas, Campinas, SP, Brazil
                [3 ]University of Massachusetts Dartmouth, Dartmouth, MA, United States of America
                [4 ]Brandeis University, Waltham, MA, United States of America
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: DH ML MAMA DDC DB IRE YB. Performed the experiments: DH ML MAMA DDC DB IRE YB. Analyzed the data: DH ML MAMA DDC DB IRE YB. Contributed reagents/materials/analysis tools: DH ML MAMA DDC DB IRE YB. Wrote the paper: DH ML MAMA DDC DB IRE YB.


                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

                Page count
                Figures: 10, Tables: 2, Pages: 27
                The authors have no support or funding to report.
                Research Article
                Custom metadata
                The primary dataset was not originally generated by the authors, and interested researchers can obtain the data independently from the third party providers specified below. All the historical return data is publicly available from Yahoo, Google and other online sources, including Capital IQ (, which was used for this purpose. The list of companies that we used, the Russell 3000, was obtained from Russell Investments ( The robustness tests indicate that the use of this list is not essential to reproduction of the results, but can still be obtained from Russell in the same manner that the authors obtained it.



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