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      Abrupt rise of new machine ecology beyond human response time

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          Abstract

          Society's techno-social systems are becoming ever faster and more computer-orientated. However, far from simply generating faster versions of existing behaviour, we show that this speed-up can generate a new behavioural regime as humans lose the ability to intervene in real time. Analyzing millisecond-scale data for the world's largest and most powerful techno-social system, the global financial market, we uncover an abrupt transition to a new all-machine phase characterized by large numbers of subsecond extreme events. The proliferation of these subsecond events shows an intriguing correlation with the onset of the system-wide financial collapse in 2008. Our findings are consistent with an emerging ecology of competitive machines featuring ‘crowds' of predatory algorithms, and highlight the need for a new scientific theory of subsecond financial phenomena.

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          Most cited references21

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          Power-law distributions in empirical data

          Power-law distributions occur in many situations of scientific interest and have significant consequences for our understanding of natural and man-made phenomena. Unfortunately, the detection and characterization of power laws is complicated by the large fluctuations that occur in the tail of the distribution -- the part of the distribution representing large but rare events -- and by the difficulty of identifying the range over which power-law behavior holds. Commonly used methods for analyzing power-law data, such as least-squares fitting, can produce substantially inaccurate estimates of parameters for power-law distributions, and even in cases where such methods return accurate answers they are still unsatisfactory because they give no indication of whether the data obey a power law at all. Here we present a principled statistical framework for discerning and quantifying power-law behavior in empirical data. Our approach combines maximum-likelihood fitting methods with goodness-of-fit tests based on the Kolmogorov-Smirnov statistic and likelihood ratios. We evaluate the effectiveness of the approach with tests on synthetic data and give critical comparisons to previous approaches. We also apply the proposed methods to twenty-four real-world data sets from a range of different disciplines, each of which has been conjectured to follow a power-law distribution. In some cases we find these conjectures to be consistent with the data while in others the power law is ruled out.
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            A theory of power-law distributions in financial market fluctuations.

            Insights into the dynamics of a complex system are often gained by focusing on large fluctuations. For the financial system, huge databases now exist that facilitate the analysis of large fluctuations and the characterization of their statistical behaviour. Power laws appear to describe histograms of relevant financial fluctuations, such as fluctuations in stock price, trading volume and the number of trades. Surprisingly, the exponents that characterize these power laws are similar for different types and sizes of markets, for different market trends and even for different countries--suggesting that a generic theoretical basis may underlie these phenomena. Here we propose a model, based on a plausible set of assumptions, which provides an explanation for these empirical power laws. Our model is based on the hypothesis that large movements in stock market activity arise from the trades of large participants. Starting from an empirical characterization of the size distribution of those large market participants (mutual funds), we show that the power laws observed in financial data arise when the trading behaviour is performed in an optimal way. Our model additionally explains certain striking empirical regularities that describe the relationship between large fluctuations in prices, trading volume and the number of trades.
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              Predicting the behavior of techno-social systems.

              We live in an increasingly interconnected world of techno-social systems, in which infrastructures composed of different technological layers are interoperating within the social component that drives their use and development. Examples are provided by the Internet, the World Wide Web, WiFi communication technologies, and transportation and mobility infrastructures. The multiscale nature and complexity of these networks are crucial features in understanding and managing the networks. The accessibility of new data and the advances in the theory and modeling of complex networks are providing an integrated framework that brings us closer to achieving true predictive power of the behavior of techno-social systems.
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                Author and article information

                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group
                2045-2322
                11 September 2013
                2013
                : 3
                : 2627
                Affiliations
                [1 ]Physics Department, University of Miami , Coral Gables, Florida 33124, U.S.A.
                [2 ]Nanex LLC , Evanston, Illinois, U.S.A.
                [3 ]The MITRE Corporation , McLean, VA 22102, U.S.A.
                [4 ]Complex Systems Center, University of Vermont , Burlington, VT 05405, U.S.A
                Author notes
                Article
                srep02627
                10.1038/srep02627
                3769652
                24022120
                eb3c46c0-bc79-41e6-8df5-6c09fcacccae
                Copyright © 2013, Macmillan Publishers Limited. All rights reserved

                This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-nd/3.0/

                History
                : 19 November 2012
                : 22 August 2013
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