Computational methods for studying FRP can be broadly categorized into three approaches: statistical models, deterministic methods, and stochastic methods. Statistical models predict macroscopic polymer properties based on bonding rules but overlook kinetic histories, limiting their use in dynamic systems. Deterministic methods provide detailed kinetic insights but are computationally intensive for complex reaction networks, often requiring approximations that may reduce accuracy. In contrast, stochastic methods simulate individual reaction events based on probabilities, offering a more precise representation of the inherent randomness in polymerization processes. Among these, the stochastic simulation algorithm (SSA), also known as the Gillespie algorithm, is particularly notable for capturing the discrete and probabilistic nature of molecular events. SSA simulates each reaction step individually by calculating time intervals between events based on reaction propensities and iteratively updating the system state. Unlike statistical or deterministic methods, SSA avoids the explicit derivation and solution of complex kinetic equations, relying instead on reaction probabilities to drive the simulation. This makes SSA especially suitable for describing the microkinetics of complex polymerization networks, enhancing its utility in the study of both FRP and CRP.