Daniel A Eisenberg, Thomas P Seager, David L Alderson
{"title":"The rebound curve is a poor model of resilience.","authors":"Daniel A Eisenberg, Thomas P Seager, David L Alderson","doi":"10.1093/pnasnexus/pgaf052","DOIUrl":null,"url":null,"abstract":"<p><p>The rebound curve remains the most prevalent model for conceptualizing, measuring, and explaining resilience for engineering and community systems by tracking the functional robustness and recovery of systems over time. (It also goes by many names, including the resilience curve, the resilience triangle, and the system functionality curve, among others.) Despite longstanding recognition that resilience is more than rebound, the curve remains highly used, cited, and taught. In this article, we challenge the efficacy of this model for resilience and identify fundamental shortcomings in how it handles system function, time, dynamics, and decisions - the key elements that make up the curve. These oversimplifications reinforce misconceptions about resilience that are unhelpful for understanding complex systems and are potentially dangerous for guiding decisions. We argue that models of resilience should abandon the use of this curve and instead be reframed to open new lines of inquiry that center on improving adaptive capacity in complex systems, rather than on functional rebound. We provide a list of questions to help future researchers communicate these limitations and address any implications on recommendations derived from its use.</p>","PeriodicalId":74468,"journal":{"name":"PNAS nexus","volume":"4 3","pages":"pgaf052"},"PeriodicalIF":2.2000,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11880801/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"PNAS nexus","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1093/pnasnexus/pgaf052","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/3/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
The rebound curve remains the most prevalent model for conceptualizing, measuring, and explaining resilience for engineering and community systems by tracking the functional robustness and recovery of systems over time. (It also goes by many names, including the resilience curve, the resilience triangle, and the system functionality curve, among others.) Despite longstanding recognition that resilience is more than rebound, the curve remains highly used, cited, and taught. In this article, we challenge the efficacy of this model for resilience and identify fundamental shortcomings in how it handles system function, time, dynamics, and decisions - the key elements that make up the curve. These oversimplifications reinforce misconceptions about resilience that are unhelpful for understanding complex systems and are potentially dangerous for guiding decisions. We argue that models of resilience should abandon the use of this curve and instead be reframed to open new lines of inquiry that center on improving adaptive capacity in complex systems, rather than on functional rebound. We provide a list of questions to help future researchers communicate these limitations and address any implications on recommendations derived from its use.