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Disentangling Resilience from Robustness: Contextual Dualism, Interactionism, and Game-Theoretic Paradigms

Quanyan Zhu, Tamer Basar

TL;DR

The paper clarifies how resilience and robustness address different disturbance classes in control systems by introducing contextual dualism and interactionism. It presents a three-stage resilience model (ex ante, interim, ex post) and a two-mode toy model to illustrate resilience–robustness tradeoffs, positioning game-theoretic paradigms as a unifying framework. It argues for a quantitative science of resilience, proposing metrics, uncertainty partitioning, and dynamic games, and extends the framework to system‑of‑systems and cyber-physical contexts with AI and cyber-insurance as supporting mechanisms. By 2030, the work envisions resilient control foundations bridging feedback control, game theory, and learning theory to fortify infrastructures, cyber systems, and human-centered networks against climate, geopolitical, and cyber threats, delivering societal-scale impact.

Abstract

This article explains the distinctions between robustness and resilience in control systems. Resilience confronts a distinct set of challenges, posing new ones for designing controllers for feedback systems, networks, and machines that prioritize resilience over robustness. The concept of resilience is explored through a three-stage model, emphasizing the need for a proactive preparation and automated response to elastic events. A toy model is first used to illustrate the tradeoffs between resilience and robustness. Then, it delves into contextual dualism and interactionism, and introduces game-theoretic paradigms as a unifying framework to consolidate resilience and robustness. The article concludes by discussing the interplay between robustness and resilience, suggesting that a comprehensive theory of resilience and quantification metrics, and formalization through game-theoretic frameworks are necessary. The exploration extends to system-of-systems resilience and various mechanisms, including the integration of AI techniques and non-technical solutions, like cyber insurance, to achieve comprehensive resilience in control systems. As we approach 2030, the systems and control community is at the opportune moment to lay scientific foundations of resilience by bridging feedback control theory, game theory, and learning theory. Resilient control systems will enhance overall quality of life, enable the development of a resilient society, and create a societal-scale impact amid global challenges such as climate change, conflicts, and cyber insecurity.

Disentangling Resilience from Robustness: Contextual Dualism, Interactionism, and Game-Theoretic Paradigms

TL;DR

The paper clarifies how resilience and robustness address different disturbance classes in control systems by introducing contextual dualism and interactionism. It presents a three-stage resilience model (ex ante, interim, ex post) and a two-mode toy model to illustrate resilience–robustness tradeoffs, positioning game-theoretic paradigms as a unifying framework. It argues for a quantitative science of resilience, proposing metrics, uncertainty partitioning, and dynamic games, and extends the framework to system‑of‑systems and cyber-physical contexts with AI and cyber-insurance as supporting mechanisms. By 2030, the work envisions resilient control foundations bridging feedback control, game theory, and learning theory to fortify infrastructures, cyber systems, and human-centered networks against climate, geopolitical, and cyber threats, delivering societal-scale impact.

Abstract

This article explains the distinctions between robustness and resilience in control systems. Resilience confronts a distinct set of challenges, posing new ones for designing controllers for feedback systems, networks, and machines that prioritize resilience over robustness. The concept of resilience is explored through a three-stage model, emphasizing the need for a proactive preparation and automated response to elastic events. A toy model is first used to illustrate the tradeoffs between resilience and robustness. Then, it delves into contextual dualism and interactionism, and introduces game-theoretic paradigms as a unifying framework to consolidate resilience and robustness. The article concludes by discussing the interplay between robustness and resilience, suggesting that a comprehensive theory of resilience and quantification metrics, and formalization through game-theoretic frameworks are necessary. The exploration extends to system-of-systems resilience and various mechanisms, including the integration of AI techniques and non-technical solutions, like cyber insurance, to achieve comprehensive resilience in control systems. As we approach 2030, the systems and control community is at the opportune moment to lay scientific foundations of resilience by bridging feedback control theory, game theory, and learning theory. Resilient control systems will enhance overall quality of life, enable the development of a resilient society, and create a societal-scale impact amid global challenges such as climate change, conflicts, and cyber insecurity.
Paper Structure (11 sections, 1 equation, 3 figures)

This paper contains 11 sections, 1 equation, 3 figures.

Table of Contents

  1. Multi-stage characterization of resilience
  2. A toy model for resilience
  3. Robustness-Resilience Contextual Dualism
  4. Robustness-Resilience Interactionism
  5. Game-Theoretic Paradigms
  6. Research Directions
  7. Toward a science of resilience
  8. Mechanisms to achieve resilience
  9. Relevance to Control Systems Community
  10. Box I: Dualism and Interactionism
  11. Box II: Elastic and Inelastic Events

Figures (3)

  • Figure 1: Three-stage illustration of resilience: Resilience focuses on the response to disruptive events and graceful recovery of the system following such disruptions.
  • Figure 2: A two-mode toy model to illustrate resilience.
  • Figure 3: Events are classified into two categories based on their elasticity: the set of elastic events, denoted by $E$, and the set of inelastic events, denoted by $I$. The set of events in the middle, denoted by $M$, can be managed through either robustness or resilience strategies. The choice between these strategies is determined by system specifications or a cost-and-benefit analysis.