Systems Modeling and Numerical Simulation of Financial Reporting Oversight Mechanisms

Financial reporting oversight evolves through repeated feedback among managerial incentives, audit detection, board oversight, regulatory intervention, and market trust. I develop a transparent recursive simulation model to examine whether specified mechanism combinations can generate distinguishable governance trajectories within its rules. Monte Carlo simulations compare institutional scenarios, supported by analyses of uncertainty, alternative model designs, heterogeneous conditions, and a learning-agent extension. Within the simulations, high-transparency coordination generally produces the strongest governance outcomes, whereas weak governance remains consistently least favorable across the uncertainty analyses. In the model, governance improvement depends on interactions among disclosure, audit, regulation, and market feedback. In the learning-agent extension, learned policies yield less favorable governance and reporting outcomes than the fixed-policy benchmark. Chinese A-share evidence is broadly consistent with the main simulated patterns in direction, risk location, and broad ordering. These findings provide mechanism-sufficiency evidence within the declared rule family and explain how oversight signals become institutional outcomes and subsequent feedback.