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Abrupt rise of new machine ecology beyond human response time

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 mill...

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Detalles Bibliográficos
Autores principales: Johnson, Neil, Zhao, Guannan, Hunsader, Eric, Qi, Hong, Johnson, Nicholas, Meng, Jing, Tivnan, Brian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3769652/
https://www.ncbi.nlm.nih.gov/pubmed/24022120
http://dx.doi.org/10.1038/srep02627
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author Johnson, Neil
Zhao, Guannan
Hunsader, Eric
Qi, Hong
Johnson, Nicholas
Meng, Jing
Tivnan, Brian
author_facet Johnson, Neil
Zhao, Guannan
Hunsader, Eric
Qi, Hong
Johnson, Nicholas
Meng, Jing
Tivnan, Brian
author_sort Johnson, Neil
collection PubMed
description 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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spelling pubmed-37696522013-09-11 Abrupt rise of new machine ecology beyond human response time Johnson, Neil Zhao, Guannan Hunsader, Eric Qi, Hong Johnson, Nicholas Meng, Jing Tivnan, Brian Sci Rep Article 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. Nature Publishing Group 2013-09-11 /pmc/articles/PMC3769652/ /pubmed/24022120 http://dx.doi.org/10.1038/srep02627 Text en Copyright © 2013, Macmillan Publishers Limited. All rights reserved http://creativecommons.org/licenses/by-nc-nd/3.0/ 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/
spellingShingle Article
Johnson, Neil
Zhao, Guannan
Hunsader, Eric
Qi, Hong
Johnson, Nicholas
Meng, Jing
Tivnan, Brian
Abrupt rise of new machine ecology beyond human response time
title Abrupt rise of new machine ecology beyond human response time
title_full Abrupt rise of new machine ecology beyond human response time
title_fullStr Abrupt rise of new machine ecology beyond human response time
title_full_unstemmed Abrupt rise of new machine ecology beyond human response time
title_short Abrupt rise of new machine ecology beyond human response time
title_sort abrupt rise of new machine ecology beyond human response time
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3769652/
https://www.ncbi.nlm.nih.gov/pubmed/24022120
http://dx.doi.org/10.1038/srep02627
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