# A Novel Dynamic Approach for Risk Analysis and Simulation Using Multi-Agents Model
## 9 Conclusions  
In this paper, a novel dynamic multi-agent model for risk analysis has been proposed and described thoroughly. The proposed model is called _Generic Dynamic Agent-Based Model_ (GDABM) for risk analysis. This model represents the dynamic behavior of agents as a result of failure occurrence. It shows the failure propagation among the system's components as well as the failure dependencies between those components. Each agent in the modeled system has a set of activities, attributes, failure modes, and behavioral modes.  
Figure 21: Comparison of the response times for both ABM and GDABM.  
Figure 20: Comparison of the Execution times for both ABM and GDABM.  
By studying the behavior of each agent in the system, GDABM was able to assess and analyze the risk of the entire system dynamically. GDABM thoroughly analyses dynamic systems in a coherent manner. It provides a graphical illustration of agents' behavioral modes with failure causes and outcomes and shows the direct relation between agents' behavioral modes transition and the activation/deactivation of a failure mode. A detailed case study of a chemical reactor/operator was provided. The case study used the GDABM to model various agents and their associated interactions. GDABM was able to simulate the behavior of the system in both nominal (failure-free) and degraded (failure) conditions. GDABM also analyzed the risk of the aforementioned systems. The goal of the proposed model is to analyze risks and to study the dynamic behavior of dynamic systems using multi-agents models. GDABM has proven to give very promising results when compared to the reference model (ABM) in terms of Accuracy (15%) and Response time (27%), for the execution time, GDABM signs an extra delay (13%) that can be accepted due to the real-time evaluation of active failure/behavioral modes.  
The future work of this paper will be to use MCs for stochastic fuzzy failure, represent the population density in a dynamic way using a probability law, and test the model in different systems with a higher number of active agents and failure modes. Examples of such systems are dangerous good transportation, evacuation, and flood systems.  
Conceptualization, S.K.; methodology and approach, H.K.; software, S.K.; validation, H.K., S.K. and W.H.F.A.; formal analysis, S.K.; investigation, W.H.F.A.; resources, W.H.F.A.; data curation, S.K.; writing--original draft preparation, H.K.; writing--review and editing, W.H.F.A.; visualization, S.K.; supervision, S.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} ABM & Agent-Based models \\ GDABM & Generic Dynamic Agent-Based models \\ ABMS & Agent-Based Modeling and Simulation \\ SD & System Dynamics \\ DE & Discrete Events \\ PN & Petri Nets \\ MCs & Monte Carlo Simulation \\ RA & Risk Analysis \\ FTA & Fault tree Analysis \\ BM & Behavioral mode \\ FM & Failure mode \\ BFM & Boolean Failure mode \\ SFM & Stochastic Failure mode \\ CFM & Complex Failure mode \\ EFAC & External Failure Agent Communication \\ IFAC & Internal Failure Agent Communication \\ \end{tabular}