## 4 Discussions
### Case Studies Applying E3R  
This section applies the E3R modeling paradigm to an Intelligent Transport System case study to demonstrate how the model assists in mission assurance scenarios where organizational priorities may shift and anticipatory adaptive scenarios must be considered. In this case study, an Intelligent Transport System (ITS) is introduced to the scenario as a form of C-CPSS. The ITS consists of a series of internet of things (IoT)-connected devices that collect road- and transport-related data and transfer this through database and cloud structures into application platforms, which are used to manage transport infrastructure [102; 103; 104]. Hazards, event logs, traffic flow information, GPS data, and weather are all examples of inputs that devices within the ITS may collect and process. People also constitute key social nodes within ITS C-CPSS, with their conduct influencing vehicle behaviors, worksite patterns, and pedestrian activity. The E3R model will be applied to the ITS case study through two scenarios. The first will be a 'business as usual' (BAU) scenario where system patterns are reflective of a typical 24 h ITS cycle. The second will be a scenario reflective of emergent phenomena, in which a major vehicle collision occurs on one of the artery highways within the ITS. Through the use of these two scenarios, the adaptive value of the E3R model will be demonstrated, particularly its strength within unpredictable complex environments such as those categorized by C-CPSS that require the application of responsible resilience principles. In the first stage of 'Observing and Sensing', real-time data from IoT devices in the ITS are collected [104]. A copy of this data is then sent into an ontology-driven context model. This model analyzes the inputs to develop a representation of the ecology of the ITS, including the dynamics between different C-CPSS nodes. For the first scenario, this would ontology would be reflective of the typical node activity experienced within the system within a 24-h time frame, such as traffic light activity, adaptive speed limit signs, and street lighting control. For the second scenario, the ontology would include data specifically relating to the vehicle collision and the flow-on relationships that were triggered by that event, such as the initiation of Ambulance and Police services. From this ontology, an agent-based Artificial Intelligence-driven simulation environment can be used to explore consequences and optimization options. Applying mission assurance principles can change the priority of cyber services within their wider context, and this is seen comparitively between the two scenarios. In the BAU context, the primary outcome of agent-based simulations is to develop strategic long-term courses of action that optimize the ITS for maximum efficiency and effectiveness. These could be through the exploration of business continuity planning procedures, testing traffic light patterns, or experimenting with new road paths. In the second collision scenario, the mission priority is short-term, considering how to reduce the impact of the accident to the wider ITS. Simulations to achieve this could focus on modifying emergency response capabilities, experimenting with adaptive detour routing, and changing speed limits to alter traffic flow. In both scenarios, applying agent-based simulations offers opportunities to understand the consequences of mitigating actions as they are applied to the ITS and how they may differ across various operating scenarios where nonlinearity increases contextual complexity. Outcomes and lessons learnt from these simulations can then be processed by ethical reasoning and risk inference modules, where they can be evaluated against transparency, accountability, and fairness metrics. This is to ensure that options that may be logically or computationally valid but are ethically unsound are not considered within the decision modules.