# A Novel Dynamic Approach for Risk Analysis and Simulation Using Multi-Agents Model
###### Abstract  
Static risk analysis techniques (SRATs) use event graphs and risk analysis assessment models. Those techniques are not time-based techniques and hence are inadequate to model dynamic stochastic systems. This paper proposes a novel dynamic approach to model such stochastic systems using Dynamic Fault Trees (DFT). The proposed model is called _Generic Dynamic Agent-Based Model_ (GDABM) for risk analysis. GDABM is built on top of the well-known Agent-Based Modeling and Simulation (ABMS) technique. GDABM can model the dynamic system agents in both nominal (failure-free) and degraded (failure) modes. GDABM shows the propagation of failure between system elements and provides complete information about the system's configurations. In this paper, a complete detailed case study is provided to show the GDABM capabilities to model and study the risk analysis for such dynamic systems. In the case study, the GDABM models the risk analysis for a chemical reactor/operator and performs a complete risk analysis for the entire system. The GDABM managed to simulate the dynamic behavior of the system's components successfully using Repast Simphony 2.0. Detailed agent behavioral modes and failure modes are provided with various scenarios, including different time stamps. The proposed GDABM is compared to a reference model. The reference model is referred to as the ABM model. GDABM has given very promising results. A comparison study was performed on three performance measures. The performance measures used are (1) Accuracy, (2) response time, and (3) execution time. GDABM has outperformed the reference model by 15% in terms of accuracy and by 27% in terms of response time. GDABM incurs a slightly higher execution time (13%) when compared to the ABM reference model. It can be concluded that GDABM can deliver accepted performance in terms of accuracy and response time without incurring much processing overhead. Footnote †: journal: Applied Sciences  
consists of an event tree and a fault tree, and block diagrams. Those techniques are not adequate for studying the dynamic effects of time-dependent systems; since there are many interesting behaviors that static modeling techniques will not be able to model. An example of such interesting behavior is time-dependent behavior that changes over time. On the other hand, and after many developments over the past decades, scientists developed many dynamic modeling techniques that provide means of modeling and simulation of the time-dependent behavior of various complex systems, including a wide range of real-world industrial systems operating in a dynamic environment. Those techniques overcome the limitations of the conventional static risk analysis techniques. Examples of such methods are Petrinets [1], graphical Markov models [2], state-transition graphs, Business Process Model [3], Stochastic Hybrid Automaton (SHA), Monte Carlo simulations [4], and Agent-Based Modelling and Simulation (ABMS) [5]. [PERSON] et al. [6] have proven that ABMS is one of the most adequate tools to model dynamic systems with autonomous agents. ABMS was proven to be suitable for risk assessment since the agents frequently cooperate and interact with each other [7]. To the best of our knowledge, ABMS is not widely used in the field of risk analysis and its application is limited by performing a general risk analysis without providing any details about the failure (causes and consequences), the failure propagation between the various system's components and the mutual relation between agents behavioral and failure modes. This paper aims to (1) propose an extension to the classical ABM that overcomes the above limitations, (2) provide full details about risk analysis, (3) represent the failure propagation and risk analysis in the agent-based model, and (4) study the failure of a system' component to show its effects on the agent's behavioral mode in addition to its transition to the other system' components.