Multi-agent systemsMulti-agent systems are becoming heavily relied upon as the complexityComplexity of the world increases. The effectiveness of these systems depends on consensus algorithms; however, the presence of faulted agentsFaulted agents can compromise the security and reliability of these consensus algorithms. Therefore, it is crucial to develop robust consensus methods to maintain system security and reliability. Biologically-Inspired DesignBiologically-inspired design previously led to the Synchronous Hatching Consensus Algorithm which proved to be robust even with up to 20% of faulted agentsFaulted agents reporting false positives. This work aims to provide insights for when the Synchronous Hatching Consensus Algorithm can be applied. This is achieved through three methods: comparing robustnessRobustness to faulted agentsFaulted agents reporting false negatives, performing an uncertainty analysisUncertainty analysis, and performing a sensitivity analysisSensitivity analysis. First, an agent-based ANYLOGIC model was tested with 0, 1, 5, 10, 15, and 20 faulted agentsFaulted agents reporting false negatives (out of at total population of 100). The model was applied to four separate environments. RobustnessRobustness to faulted agentsFaulted agents was measured by how consistent the hours was to reach 66% consensus across any percentage of faulted agentsFaulted agents or environments. A total of 650 iterations were run per faulted agentFaulted agents and environment combination, totalling in 15,600 runs. The model was deemed not robust to faulted agentsFaulted agents reporting false negatives. The total probability for a run failing to reach consensus was 59%. The slower changing environments most contributed to the probability a run would fail. The percentage of faulted agentsFaulted agents had the second highest impact. The findings indicate that the algorithm should be implemented in an environment which quickly reaches its decision threshold and that when a fault occurs consensus should be assumed, because the model is more robust to false positive faults.