## 4 Discussions
### The E3R Modeling Paradigm: Towards Responsible Resilience in C-CPSS  
As cyber systems increasingly intertwine with physical infrastructure and social behaviors, the limitations of traditional cyber-physical systems (CPS) frameworks become increasingly apparent. Existing models primarily focus on technical resilience and control logic, often overlooking the emergent complexity introduced by human actors, dynamic organizational contexts, and ethical dilemmas. This oversight becomes critical as modern infrastructures evolve into Complex Cyber-Physical-Social Systems (C-CPSS)--multi-layered systems where cyber threats are no longer confined to code or components but manifest unpredictably across human, organizational, and environmental dimensions. In response to this pressing challenge, we introduce the concept of'responsible resilience', which we define as the adaptive capability of systems to anticipate, absorb, and recover from cyber risks in ways that are not only technically robust but also ethically transparent, socially responsive, and contextually grounded. Unlike conventional resilience models that focus solely on threat mitigation and system robustness, responsible resilience embeds trust, explainability, and adaptability as foundational properties of socio-technical system defense. At the core of this contribution is the E3R modeling paradigm, which stands for Emergent, Risk-Relevant Resilience. This paradigm seeks to operationalize responsible resilience by integrating modeling approaches capable of capturing the dynamic inter-dependencies and feedback loops between the cyber, physical, and social layers of a system. As depicted in Figure 4, E3R is implemented as a cyclic and layered framework that integrates six key components: sensing and observation, ontology-driven context modeling, agent-based simulation, ethical risk reasoning, adaptive decision support, and human-in-the-loop oversight. The modeling process begins with Observation and Sensing, where real-time data streams from across the cyber, physical, and social layers are captured. This relies on other foundational technologies including the internet of things and the implementation of sensors that can specifically observe social data inputs within the environment [64; 65; 66; 67]. These inputs are translated into formal semantic representations through Ontology-Driven Context Modeling, enabling the system to understand the meaning, relationships,and dynamics embedded in its operating environment. Ontologies serve as a \"specification of a conceptualization\", describing concepts in and relationships between elements within a system [68; 69]. For this component, lessons can be taken from previous ontologies created for existing CPS research, applied to case studies in safety design, insider threats, cyber forensics, human factors in cyber security, and broader cyber security [70; 71; 72; 73; 74; 75]. Next, Agent-Based Artificial Intelligence-driven simulation is used to explore how local decisions and external shocks might propagate through the system, revealing emergent vulnerabilities or cascading risks. This is of significant benefit to systems that may exhibit unpredictable behaviors, and utilizing concepts such as digital twins provides an opportunity to more accurately simulate these real-world phenomena [76]. Drawing from the success of agent-based research in the cyber security field, in the simulation of computer network attacks and botnets, cyber-physical production systems, internet of things defense, and CPS supply chain emergence, applying agents to C-CPSS poses a significant opportunity to analyze consequence dynamics [77; 78; 79; 80; 81]. Critically, the simulation output is not simply used to rank threats, but is instead passed through an Ethical Reasoning and Risk Inference module. This layer ensures that decisions are evaluated against transparency, accountability, and fairness metrics, reflecting human values in machine reasoning.