Limits of Risk Predictability in a Cascading Alternating Renewal Process Model

Abstract Most risk analysis models systematically underestimate the probability and impact of catastrophic events (e.g., economic crises, natural disasters, and terrorism) by not taking into account interconnectivity and interdependence of risks. To address this weakness, we propose the Cascading Al...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Xin Lin, Alaa Moussawi, Gyorgy Korniss, Jonathan Z. Bakdash, Boleslaw K. Szymanski
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/8180f95868104db69c25cf4d78118e74
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:8180f95868104db69c25cf4d78118e74
record_format	dspace
spelling	oai:doaj.org-article:8180f95868104db69c25cf4d78118e742021-12-02T11:50:57ZLimits of Risk Predictability in a Cascading Alternating Renewal Process Model10.1038/s41598-017-06873-x2045-2322https://doaj.org/article/8180f95868104db69c25cf4d78118e742017-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-06873-xhttps://doaj.org/toc/2045-2322Abstract Most risk analysis models systematically underestimate the probability and impact of catastrophic events (e.g., economic crises, natural disasters, and terrorism) by not taking into account interconnectivity and interdependence of risks. To address this weakness, we propose the Cascading Alternating Renewal Process (CARP) to forecast interconnected global risks. However, assessments of the model’s prediction precision are limited by lack of sufficient ground truth data. Here, we establish prediction precision as a function of input data size by using alternative long ground truth data generated by simulations of the CARP model with known parameters. We illustrate the approach on a model of fires in artificial cities assembled from basic city blocks with diverse housing. The results confirm that parameter recovery variance exhibits power law decay as a function of the length of available ground truth data. Using CARP, we also demonstrate estimation using a disparate dataset that also has dependencies: real-world prediction precision for the global risk model based on the World Economic Forum Global Risk Report. We conclude that the CARP model is an efficient method for predicting catastrophic cascading events with potential applications to emerging local and global interconnected risks.Xin LinAlaa MoussawiGyorgy KornissJonathan Z. BakdashBoleslaw K. SzymanskiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-11 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Xin Lin Alaa Moussawi Gyorgy Korniss Jonathan Z. Bakdash Boleslaw K. Szymanski Limits of Risk Predictability in a Cascading Alternating Renewal Process Model
description	Abstract Most risk analysis models systematically underestimate the probability and impact of catastrophic events (e.g., economic crises, natural disasters, and terrorism) by not taking into account interconnectivity and interdependence of risks. To address this weakness, we propose the Cascading Alternating Renewal Process (CARP) to forecast interconnected global risks. However, assessments of the model’s prediction precision are limited by lack of sufficient ground truth data. Here, we establish prediction precision as a function of input data size by using alternative long ground truth data generated by simulations of the CARP model with known parameters. We illustrate the approach on a model of fires in artificial cities assembled from basic city blocks with diverse housing. The results confirm that parameter recovery variance exhibits power law decay as a function of the length of available ground truth data. Using CARP, we also demonstrate estimation using a disparate dataset that also has dependencies: real-world prediction precision for the global risk model based on the World Economic Forum Global Risk Report. We conclude that the CARP model is an efficient method for predicting catastrophic cascading events with potential applications to emerging local and global interconnected risks.
format	article
author	Xin Lin Alaa Moussawi Gyorgy Korniss Jonathan Z. Bakdash Boleslaw K. Szymanski
author_facet	Xin Lin Alaa Moussawi Gyorgy Korniss Jonathan Z. Bakdash Boleslaw K. Szymanski
author_sort	Xin Lin
title	Limits of Risk Predictability in a Cascading Alternating Renewal Process Model
title_short	Limits of Risk Predictability in a Cascading Alternating Renewal Process Model
title_full	Limits of Risk Predictability in a Cascading Alternating Renewal Process Model
title_fullStr	Limits of Risk Predictability in a Cascading Alternating Renewal Process Model
title_full_unstemmed	Limits of Risk Predictability in a Cascading Alternating Renewal Process Model
title_sort	limits of risk predictability in a cascading alternating renewal process model
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/8180f95868104db69c25cf4d78118e74
work_keys_str_mv	AT xinlin limitsofriskpredictabilityinacascadingalternatingrenewalprocessmodel AT alaamoussawi limitsofriskpredictabilityinacascadingalternatingrenewalprocessmodel AT gyorgykorniss limitsofriskpredictabilityinacascadingalternatingrenewalprocessmodel AT jonathanzbakdash limitsofriskpredictabilityinacascadingalternatingrenewalprocessmodel AT boleslawkszymanski limitsofriskpredictabilityinacascadingalternatingrenewalprocessmodel
_version_	1718395173896978432

Limits of Risk Predictability in a Cascading Alternating Renewal Process Model

Ejemplares similares