The magnitude of preparedness in crisis management is not to be understated. The nature of a crisis is unpredictable and rare, but when similar crises occur, a “before and after” comparison of management techniques is striking. Two hurricanes of similar intensity struck Galveston and Texas at the turn of the 20th century, but the latter claimed 99.1% fewer lives [1,2]. Sometimes, comprehensive preparedness reaps additional rewards. Motivated by concerns regarding a critical influx of people into the city of Los Angeles for the 1984 summer Olympic games, officials installed a modern, automated traffic control system that exists to this day [3,4]. Such an event is termed “crisis-similar”, as it has similar unpredictability, mitigation techniques, and focus on preparedness as more well-known crises such as natural disasters. The common constraints on all crises are information and time, with the crisis manager attempting to achieve information superiority over a crisis immediately after its occurrence [5,6]. With these observations in mind, we present a perennial simulation framework for management of crises and crisis-similar events. In addition, our framework attempts to exploit modern technological developments in the field of computer science. The framework is also flexible enough to provide hindsight and foresight studies for use in crisis-related preparedness exercises. We call our framework perennial because it is enduring and recurring – enduring because the computer simulation works symbiotically with the real system it is simulating, and recurring because the framework continually performs “What-if?” simulations to source for the optimal (or sub-optimal solution) for feedback control into the real system. The framework also allows humans in the loop (HITL) to run in the symbiotic environment. To our knowledge, this has not been applied before in symbiotic simulation. In addition to the contribution of the framework itself, we also provide a proper categorization and classification of different types of crises. Following this, we describe several real-world case studies to test the efficacy of the proposed framework. These contribute to the knowledge and breadth of the inter-disciplinary field of crisis management simulation. The remainder of this paper is organized as follows. Section 2 covers related work in the field of crisis management simulation and how our framework compares with similar work in symbiotic simulation and dynamic data-driven application systems (DDDAS). Section 3 describes the framework. Section 4 covers its usage in three case studies, together with the results analysis. Section 5 concludes the paper.