Resilience as an emergent property of human-infrastructure dynamics: A multi-agent simulation model for characterizing regime shifts and tipping point behaviors in infrastructure systems
The objective of this study is to establish a framework for analyzing infrastructure dynamics affecting the long-term steady state, and hence resilience in civil infrastructure systems. To this end, a multi-agent simulation model was created to capture important phenomena affecting the dynamics of c...
Gespeichert in:
Veröffentlicht in: | PloS one 2018-11, Vol.13 (11), p.e0207674-e0207674 |
---|---|
Hauptverfasser: | , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | e0207674 |
---|---|
container_issue | 11 |
container_start_page | e0207674 |
container_title | PloS one |
container_volume | 13 |
creator | Rasoulkhani, Kambiz Mostafavi, Ali |
description | The objective of this study is to establish a framework for analyzing infrastructure dynamics affecting the long-term steady state, and hence resilience in civil infrastructure systems. To this end, a multi-agent simulation model was created to capture important phenomena affecting the dynamics of coupled human-infrastructure systems and model the long-term performance regimes of infrastructure. The proposed framework captures the following three factors that shape the dynamics of coupled human-infrastructure systems: (i) engineered physical infrastructure; (ii) human actors; and (iii) chronic and acute stressors. A complex system approach was adopted to examine the long-term resilience of infrastructure based on the understanding of performance regimes, as well as tipping points at which shifts in the performance regime of infrastructure occur under the impact of external stressors and/or change in internal dynamics. The application of the proposed framework is demonstrated in a case of urban water distribution infrastructure using the data from a numerical case study network. The developed multi-agent simulation model was then used in examining the system resilience over a 100-year horizon under stressors such as population change and funding constraints. The results identified the effects of internal dynamics and external stressors on the resilience landscape of infrastructure systems. Furthermore, the results also showed the capability of the framework in capturing and simulating the underlying mechanisms affecting human-infrastructure dynamics, as well as long-term regime shifts and tipping point behaviors. Therefore, the integrated framework proposed in this paper enables building complex system-based theories for a more advanced understanding of civil infrastructure resilience. |
doi_str_mv | 10.1371/journal.pone.0207674 |
format | Article |
fullrecord | <record><control><sourceid>proquest_plos_</sourceid><recordid>TN_cdi_plos_journals_2136547111</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><doaj_id>oai_doaj_org_article_fc4a82bc311b4b5ba750f4f8aa23f4ff</doaj_id><sourcerecordid>2179230089</sourcerecordid><originalsourceid>FETCH-LOGICAL-c526t-fe47d13ffa0a3cd8a9245abc371e2406968fc2a153fd9444e8d82b11ec51f3833</originalsourceid><addsrcrecordid>eNptUltrFDEUHkSxtfoPRAO--DJrbnPzQSjFS6EgiD6HM5mT3SwzyZhkCutP89eZ7W5LW3w6yTnf950LX1G8ZnTFRMM-bP0SHIyr2TtcUU6bupFPilPWCV7WnIqn994nxYsYt5RWoq3r58WJoLLmDetOi78_MNrRotNIIBJwBCcMa3SJzMHPGNKOeEM2ywSutM4EiCksOi0BybBzMFkdP5JzMi1jsiXcEKPNP0jWOzL5AUdifCB6AwF0wmD_WLcmAdd2QhI31qR924EkO8_7yuxt1uhxA9fWh0isI4_6xl1MOMWXxTMDY8RXx3hW_Pry-efFt_Lq-9fLi_OrUle8TqVB2QxMGAMUhB5a6LisoNf5hsglrbu6NZoDq4QZOikltkPLe8ZQV8yIVoiz4u1Bdx59VMezR8WZqCvZMMYy4vKAGDxs1RzsBGGnPFh1k_BhrSAkq0dURkvI8low1su-6qGpqJGmBeAiR5O1Ph27Lf2Eg84HDTA-EH1YcXaj1v5a1Vy2XVtlgfdHgeB_LxiTmmzUOI7g0C_7uZuOC0rbLkPfPYL-fzt5QOngYwxo7oZhVO2teMtSeyuqoxUz7c39Re5It94T_wBIpuPC</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2136547111</pqid></control><display><type>article</type><title>Resilience as an emergent property of human-infrastructure dynamics: A multi-agent simulation model for characterizing regime shifts and tipping point behaviors in infrastructure systems</title><source>DOAJ Directory of Open Access Journals</source><source>Public Library of Science (PLoS) Journals Open Access</source><source>EZB-FREE-00999 freely available EZB journals</source><source>PubMed Central</source><source>Free Full-Text Journals in Chemistry</source><creator>Rasoulkhani, Kambiz ; Mostafavi, Ali</creator><contributor>Linkov, Igor</contributor><creatorcontrib>Rasoulkhani, Kambiz ; Mostafavi, Ali ; Linkov, Igor</creatorcontrib><description>The objective of this study is to establish a framework for analyzing infrastructure dynamics affecting the long-term steady state, and hence resilience in civil infrastructure systems. To this end, a multi-agent simulation model was created to capture important phenomena affecting the dynamics of coupled human-infrastructure systems and model the long-term performance regimes of infrastructure. The proposed framework captures the following three factors that shape the dynamics of coupled human-infrastructure systems: (i) engineered physical infrastructure; (ii) human actors; and (iii) chronic and acute stressors. A complex system approach was adopted to examine the long-term resilience of infrastructure based on the understanding of performance regimes, as well as tipping points at which shifts in the performance regime of infrastructure occur under the impact of external stressors and/or change in internal dynamics. The application of the proposed framework is demonstrated in a case of urban water distribution infrastructure using the data from a numerical case study network. The developed multi-agent simulation model was then used in examining the system resilience over a 100-year horizon under stressors such as population change and funding constraints. The results identified the effects of internal dynamics and external stressors on the resilience landscape of infrastructure systems. Furthermore, the results also showed the capability of the framework in capturing and simulating the underlying mechanisms affecting human-infrastructure dynamics, as well as long-term regime shifts and tipping point behaviors. Therefore, the integrated framework proposed in this paper enables building complex system-based theories for a more advanced understanding of civil infrastructure resilience.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0207674</identifier><identifier>PMID: 30462719</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Adaptation ; Biology and Life Sciences ; Civil engineering ; Climate change ; Complex systems ; Computer and Information Sciences ; Computer simulation ; Data processing ; Decision making ; Dynamic tests ; Dynamics ; Economic development ; Engineering and Technology ; Infrastructure ; Mathematical models ; Multiagent systems ; Physical Sciences ; Research and Analysis Methods ; Resilience ; Simulation ; Social Sciences ; Sustainability ; Technology adoption ; Water conservation ; Water distribution ; Water engineering ; Water mains ; Water shortages</subject><ispartof>PloS one, 2018-11, Vol.13 (11), p.e0207674-e0207674</ispartof><rights>This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication: https://creativecommons.org/publicdomain/zero/1.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c526t-fe47d13ffa0a3cd8a9245abc371e2406968fc2a153fd9444e8d82b11ec51f3833</citedby><cites>FETCH-LOGICAL-c526t-fe47d13ffa0a3cd8a9245abc371e2406968fc2a153fd9444e8d82b11ec51f3833</cites><orcidid>0000-0002-6694-0201</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6248985/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6248985/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,864,885,2102,2928,23866,27924,27925,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/30462719$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Linkov, Igor</contributor><creatorcontrib>Rasoulkhani, Kambiz</creatorcontrib><creatorcontrib>Mostafavi, Ali</creatorcontrib><title>Resilience as an emergent property of human-infrastructure dynamics: A multi-agent simulation model for characterizing regime shifts and tipping point behaviors in infrastructure systems</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>The objective of this study is to establish a framework for analyzing infrastructure dynamics affecting the long-term steady state, and hence resilience in civil infrastructure systems. To this end, a multi-agent simulation model was created to capture important phenomena affecting the dynamics of coupled human-infrastructure systems and model the long-term performance regimes of infrastructure. The proposed framework captures the following three factors that shape the dynamics of coupled human-infrastructure systems: (i) engineered physical infrastructure; (ii) human actors; and (iii) chronic and acute stressors. A complex system approach was adopted to examine the long-term resilience of infrastructure based on the understanding of performance regimes, as well as tipping points at which shifts in the performance regime of infrastructure occur under the impact of external stressors and/or change in internal dynamics. The application of the proposed framework is demonstrated in a case of urban water distribution infrastructure using the data from a numerical case study network. The developed multi-agent simulation model was then used in examining the system resilience over a 100-year horizon under stressors such as population change and funding constraints. The results identified the effects of internal dynamics and external stressors on the resilience landscape of infrastructure systems. Furthermore, the results also showed the capability of the framework in capturing and simulating the underlying mechanisms affecting human-infrastructure dynamics, as well as long-term regime shifts and tipping point behaviors. Therefore, the integrated framework proposed in this paper enables building complex system-based theories for a more advanced understanding of civil infrastructure resilience.</description><subject>Adaptation</subject><subject>Biology and Life Sciences</subject><subject>Civil engineering</subject><subject>Climate change</subject><subject>Complex systems</subject><subject>Computer and Information Sciences</subject><subject>Computer simulation</subject><subject>Data processing</subject><subject>Decision making</subject><subject>Dynamic tests</subject><subject>Dynamics</subject><subject>Economic development</subject><subject>Engineering and Technology</subject><subject>Infrastructure</subject><subject>Mathematical models</subject><subject>Multiagent systems</subject><subject>Physical Sciences</subject><subject>Research and Analysis Methods</subject><subject>Resilience</subject><subject>Simulation</subject><subject>Social Sciences</subject><subject>Sustainability</subject><subject>Technology adoption</subject><subject>Water conservation</subject><subject>Water distribution</subject><subject>Water engineering</subject><subject>Water mains</subject><subject>Water shortages</subject><issn>1932-6203</issn><issn>1932-6203</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>DOA</sourceid><recordid>eNptUltrFDEUHkSxtfoPRAO--DJrbnPzQSjFS6EgiD6HM5mT3SwzyZhkCutP89eZ7W5LW3w6yTnf950LX1G8ZnTFRMM-bP0SHIyr2TtcUU6bupFPilPWCV7WnIqn994nxYsYt5RWoq3r58WJoLLmDetOi78_MNrRotNIIBJwBCcMa3SJzMHPGNKOeEM2ywSutM4EiCksOi0BybBzMFkdP5JzMi1jsiXcEKPNP0jWOzL5AUdifCB6AwF0wmD_WLcmAdd2QhI31qR924EkO8_7yuxt1uhxA9fWh0isI4_6xl1MOMWXxTMDY8RXx3hW_Pry-efFt_Lq-9fLi_OrUle8TqVB2QxMGAMUhB5a6LisoNf5hsglrbu6NZoDq4QZOikltkPLe8ZQV8yIVoiz4u1Bdx59VMezR8WZqCvZMMYy4vKAGDxs1RzsBGGnPFh1k_BhrSAkq0dURkvI8low1su-6qGpqJGmBeAiR5O1Ph27Lf2Eg84HDTA-EH1YcXaj1v5a1Vy2XVtlgfdHgeB_LxiTmmzUOI7g0C_7uZuOC0rbLkPfPYL-fzt5QOngYwxo7oZhVO2teMtSeyuqoxUz7c39Re5It94T_wBIpuPC</recordid><startdate>20181121</startdate><enddate>20181121</enddate><creator>Rasoulkhani, Kambiz</creator><creator>Mostafavi, Ali</creator><general>Public Library of Science</general><general>Public Library of Science (PLoS)</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7QG</scope><scope>7QL</scope><scope>7QO</scope><scope>7RV</scope><scope>7SN</scope><scope>7SS</scope><scope>7T5</scope><scope>7TG</scope><scope>7TM</scope><scope>7U9</scope><scope>7X2</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8C1</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>D1I</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>H94</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB.</scope><scope>KB0</scope><scope>KL.</scope><scope>L6V</scope><scope>LK8</scope><scope>M0K</scope><scope>M0S</scope><scope>M1P</scope><scope>M7N</scope><scope>M7P</scope><scope>M7S</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PATMY</scope><scope>PDBOC</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0002-6694-0201</orcidid></search><sort><creationdate>20181121</creationdate><title>Resilience as an emergent property of human-infrastructure dynamics: A multi-agent simulation model for characterizing regime shifts and tipping point behaviors in infrastructure systems</title><author>Rasoulkhani, Kambiz ; Mostafavi, Ali</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c526t-fe47d13ffa0a3cd8a9245abc371e2406968fc2a153fd9444e8d82b11ec51f3833</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Adaptation</topic><topic>Biology and Life Sciences</topic><topic>Civil engineering</topic><topic>Climate change</topic><topic>Complex systems</topic><topic>Computer and Information Sciences</topic><topic>Computer simulation</topic><topic>Data processing</topic><topic>Decision making</topic><topic>Dynamic tests</topic><topic>Dynamics</topic><topic>Economic development</topic><topic>Engineering and Technology</topic><topic>Infrastructure</topic><topic>Mathematical models</topic><topic>Multiagent systems</topic><topic>Physical Sciences</topic><topic>Research and Analysis Methods</topic><topic>Resilience</topic><topic>Simulation</topic><topic>Social Sciences</topic><topic>Sustainability</topic><topic>Technology adoption</topic><topic>Water conservation</topic><topic>Water distribution</topic><topic>Water engineering</topic><topic>Water mains</topic><topic>Water shortages</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Rasoulkhani, Kambiz</creatorcontrib><creatorcontrib>Mostafavi, Ali</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Animal Behavior Abstracts</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Biotechnology Research Abstracts</collection><collection>Nursing & Allied Health Database</collection><collection>Ecology Abstracts</collection><collection>Entomology Abstracts (Full archive)</collection><collection>Immunology Abstracts</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Agricultural Science Collection</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Public Health Database</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Materials Science Collection</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Materials Science Database</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>ProQuest Engineering Collection</collection><collection>ProQuest Biological Science Collection</collection><collection>Agricultural Science Database</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biological Science Database</collection><collection>Engineering Database</collection><collection>Nursing & Allied Health Premium</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Environmental Science Database</collection><collection>Materials Science Collection</collection><collection>Access via ProQuest (Open Access)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Engineering Collection</collection><collection>Environmental Science Collection</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Rasoulkhani, Kambiz</au><au>Mostafavi, Ali</au><au>Linkov, Igor</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Resilience as an emergent property of human-infrastructure dynamics: A multi-agent simulation model for characterizing regime shifts and tipping point behaviors in infrastructure systems</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2018-11-21</date><risdate>2018</risdate><volume>13</volume><issue>11</issue><spage>e0207674</spage><epage>e0207674</epage><pages>e0207674-e0207674</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>The objective of this study is to establish a framework for analyzing infrastructure dynamics affecting the long-term steady state, and hence resilience in civil infrastructure systems. To this end, a multi-agent simulation model was created to capture important phenomena affecting the dynamics of coupled human-infrastructure systems and model the long-term performance regimes of infrastructure. The proposed framework captures the following three factors that shape the dynamics of coupled human-infrastructure systems: (i) engineered physical infrastructure; (ii) human actors; and (iii) chronic and acute stressors. A complex system approach was adopted to examine the long-term resilience of infrastructure based on the understanding of performance regimes, as well as tipping points at which shifts in the performance regime of infrastructure occur under the impact of external stressors and/or change in internal dynamics. The application of the proposed framework is demonstrated in a case of urban water distribution infrastructure using the data from a numerical case study network. The developed multi-agent simulation model was then used in examining the system resilience over a 100-year horizon under stressors such as population change and funding constraints. The results identified the effects of internal dynamics and external stressors on the resilience landscape of infrastructure systems. Furthermore, the results also showed the capability of the framework in capturing and simulating the underlying mechanisms affecting human-infrastructure dynamics, as well as long-term regime shifts and tipping point behaviors. Therefore, the integrated framework proposed in this paper enables building complex system-based theories for a more advanced understanding of civil infrastructure resilience.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>30462719</pmid><doi>10.1371/journal.pone.0207674</doi><orcidid>https://orcid.org/0000-0002-6694-0201</orcidid><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | ISSN: 1932-6203 |
ispartof | PloS one, 2018-11, Vol.13 (11), p.e0207674-e0207674 |
issn | 1932-6203 1932-6203 |
language | eng |
recordid | cdi_plos_journals_2136547111 |
source | DOAJ Directory of Open Access Journals; Public Library of Science (PLoS) Journals Open Access; EZB-FREE-00999 freely available EZB journals; PubMed Central; Free Full-Text Journals in Chemistry |
subjects | Adaptation Biology and Life Sciences Civil engineering Climate change Complex systems Computer and Information Sciences Computer simulation Data processing Decision making Dynamic tests Dynamics Economic development Engineering and Technology Infrastructure Mathematical models Multiagent systems Physical Sciences Research and Analysis Methods Resilience Simulation Social Sciences Sustainability Technology adoption Water conservation Water distribution Water engineering Water mains Water shortages |
title | Resilience as an emergent property of human-infrastructure dynamics: A multi-agent simulation model for characterizing regime shifts and tipping point behaviors in infrastructure systems |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-23T11%3A50%3A57IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_plos_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Resilience%20as%20an%20emergent%20property%20of%20human-infrastructure%20dynamics:%20A%20multi-agent%20simulation%20model%20for%20characterizing%20regime%20shifts%20and%20tipping%20point%20behaviors%20in%20infrastructure%20systems&rft.jtitle=PloS%20one&rft.au=Rasoulkhani,%20Kambiz&rft.date=2018-11-21&rft.volume=13&rft.issue=11&rft.spage=e0207674&rft.epage=e0207674&rft.pages=e0207674-e0207674&rft.issn=1932-6203&rft.eissn=1932-6203&rft_id=info:doi/10.1371/journal.pone.0207674&rft_dat=%3Cproquest_plos_%3E2179230089%3C/proquest_plos_%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2136547111&rft_id=info:pmid/30462719&rft_doaj_id=oai_doaj_org_article_fc4a82bc311b4b5ba750f4f8aa23f4ff&rfr_iscdi=true |