Stable or robust? What's the difference?
Abstract
Exploring the difference between “stable” and “robust” touches on essentially every aspect of what we instinctively find interesting about robustness in natural, engineering, and social systems. It is argued here that robustness is a measure of feature persistence in systems that compels us to focus on perturbations, and often assemblages of perturbations, qualitatively different in nature from those addressed by stability theory. Moreover, to address feature persistence under these sorts of perturbations, we are naturally led to study issues including: the coupling of dynamics with organizational architecture, implicit assumptions of the environment, the role of a system’s evolutionary history in determining its current state and thereby its future state, the sense in which robustness characterizes the fitness of the set of “strategic options” open to the system; the capability of the system to switch among multiple functionalities; and the incorporation of mechanisms for learning, problem-solving, and creativity.
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Robustness means persistence in the presence of changes in system composition, system topology, or fundamental assumptions regarding the environment.
“Robustness on the other hand means persistence in the presence of changes in ' system composition, system topology, or in the fundamental assumptions regarding the environment in which the system operates ' (Jen 2003: 4).”
From Religion as a Complex and Dynamic System (2013) · cites this paper as Jen 2003: 4
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Grebe, E., & Woermann, M. (2011) · SSRN Electronic Journal · Cited by 10 · see citing works
Robustness as a non‐localizable relational phenomenon
Fernández-León, J. A. (2013) · Biological reviews/Biological reviews of the Cambridge Philosophical Society · doi:10.1111/brv.12067 · Cited by 9 · see citing works
Lenardic, A., & Seales, J. (2021) · International Journal of Astrobiology · doi:10.1017/s1473550420000415 · Cited by 9 · see citing works
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A dual scheduling model for optimizing robustness and energy consumption in manufacturing systems
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Understanding Complex Regulatory Systems: Integrating Molecular Biology and Systems Analysis
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- Authors
- Jen, E.
- Affiliations
- Santa Fe Institute (US)
- Corresponding author
- Jen, Erica
- Venue
- Complexity
- Publisher
- Wiley
- Published
- 2003-01
- Volume
- 8
- Issue
- 3
- Pages
- 12-18
- ISSN
- 1076-2787 (ISSN-L)
- 1099-0526
- OpenAlex
- W2132886043
- Cited by
- 183
- References
- 28
- Citations by year
- 2026: 3 · 2024: 3 · 2023: 5 · 2022: 5 · 2021: 10 · 2020: 8 · 2019: 7 · 2018: 9 · 2017: 11 · 2016: 12 · 2015: 3 · 2014: 8 · 2013: 10 · 2012: 10
- FWCI
- 6.42
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Complexity (publishedVersion)
- Topics
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Systems and Dynamics
- Physical Sciences › Engineering › Control and Systems Engineering › Systems Engineering Methodologies and Applications
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Chaos, Complexity, and Education
- Keywords
- robustness, stability, perturbations, organizational architecture, evolutionary history, strategic options, complex systems
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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