# A Novel Dynamic Approach for Risk Analysis and Simulation Using Multi-Agents Model
## 8 Simulation Testbed
### Comparison with Other Modeling Approaches  
Numerical comparisons between the results obtained by current models are provided in [113]. Models studied in the comparison are system dynamics (SD) models, agent-based models (ABM), and discrete Event Simulation (DES) from a well-known case study (the spread of a disease).  
Figure 18: Agent’s behavioral modes with the related Risk Level during simulation C4.  
The case study presented in this work (a reactor/operator system), is new. Therefore, similar studies based on alternative approaches are not available yet. The case study presented in this manuscript, which is a reactor/operator system, is considered novel. Therefore, similar studies based on alternative approaches are not available yet. The most relevant study found in [12] discusses the dynamic reliability of a steam generator using a Stochastic Hybrid Automaton with MCs, but this model does not provide full details about failure and behavioral modes, which are essential for dynamic risk analysis. Another comparison performed in [114] focuses on the limitations and capacities of different approaches, including Petri-nets and MCs to model a dynamic system rather than comparing the obtained numerical results. The work shows that the Petri-nets approach is a tool used for modeling systems with discrete events, but it is not adequate to be used for the continuous complex dynamic systems. This is demonstrated in the case of product level in the reactor agent. The strength of this method, such as modeling and simulating the system evolution by events occurrence, is not appropriate to be utilized in continuous dynamic systems. Numerically MCs generate the best values when used with stochastic events, but it requires a high computational effort in complex systems.  
The same case study that was used in the paper was validated and verified when we simulated the classical ABM using Repast Simphony (2.0) open-source agent-based modeling and simulation platform, we got consistent results. A comparison between the proposed GDABM and the reference model (ABM) shows that GDABM provides higher levels of accuracy (Figure 19) (>15%). This is explained due to the detailed risk analysis performed in the proposed model, which reflects a better evaluation of the risk level with consideration of the temporal aspect (thanks to DFT).  
Concerning the execution time (Figure 20), with the presence of the detailed analysis and the consideration of all analysis components, GDABM takes around 476 ms which is a bit slower than ABM (order of a few milliseconds) with consideration of a few number of agents (<10) that can raise up to 2 s for an important number of agents. This latency can be justified by the fact of representing the risk model in our proposed simulator and displaying more details about the active failure modes and their propagation in the system and the change in the informational state (attributes, behavioral mode, etc.) for every agent in the system.  
Figure 19: Comparison of the Accuracy for both ABM and GDABM.  
Regarding the response time (Figure 21), \(GDABM\) is measured to be faster than the reference model (ABM) in detecting and representing any change in the behavioral mode in real-time as it performs a real-time evaluation and analysis of possible failure modes. Furthermore, the details provided in the GDABM model allow the immediate detection of any failure, and, thus, the dynamic behavioral mode is up to date.  
A common challenge was signed regarding the output whenever we increase the number of agents (hundreds) in the simulation and the interactions among them, a huge amount (MegaByte) of information and simulation results (change in the agent's behavioral mode, change in the agent's failure mode) should be extracted.