Emergent Collapse and Resilience in Sequential Task Networks: A Dynamic Agent Based Simulation of Error Management Incivility and Managerial Oversight

Organizations operate under conditions of imperfect performance where human error is inevitable. Traditional theoretical frameworks often treat errors in isolation, focusing on discrete antecedents and immediate corrective responses. However, managerial responses to errors generate profound secondary effects that accumulate over time, influencing organizational culture, employee resilience and workforce sustainability. The present research introduces an agent-based simulation of a work team completing a fixed sequence of tasks under varying degrees of managerial oversight and response policies. The model uniquely isolates the mechanical loss of throughput caused by errors from the psychological and cultural consequences of managerial reactions, which are categorized into ignoring, correcting, or punishing. Furthermore, the model incorporates an autonomous self-notice mechanism, allowing workers to correct themselves in the absence of managerial intervention. By tracking the accumulation of worker resentment and the temporary enhancements in learning, the simulation acts as a dynamic laboratory to observe delayed consequences, nonlinear tipping points, and systemic organizational collapse. Statistical analyses of the simulation outputs reveal that while highly monitored punitive environments generate high short-term operational throughput, they simultaneously induce critical levels of interactional injustice and resentment, ultimately engineering a rapid cascading turnover and the highest probability of systemic collapse. Conversely, corrective policies combined with active monitoring achieve equivalent throughput while minimizing error rates and sustaining workforce viability. A comprehensive sensitivity analysis extending the design across all eight sweep parameters simultaneously establishes that error accumulation is primarily determined by structural factors, agent-level mistake propensity (η²p = 0.347) and task difficulty (η²p = 0.261), while resignation dynamics, resentment accumulation, and systemic collapse timing remain predominantly governed by managerial policy, a hierarchical finding independently validated by a Random Forest surrogate model achieving cross-validated R² values of up to 0.912. Further Interaction analysis reveals that the monitoring configuration that minimizes errors is precisely the configuration that maximizes workforce attrition when paired with punitive policy, crystallizing a fundamental paradox of organizational control. Composite objective analysis confirms that the punishing style constitutes the most harmful managerial configuration across all stakeholder weighting scenarios without exception, while seed robustness checks across thirty independent initializations per parameter configuration confirm that all primary inferences are stable and not attributable to stochastic variation. Additionally, the data indicate that while task difficulty primarily drives the sheer volume of errors, the underlying resilience profile of the workforce ultimately dictates the threshold for withdrawal behaviors. The study bridges the gap between the operational mechanics of task completion and the social dynamics of workplace mistreatment, illustrating how short-term punitive success often masks long-term structural fragility and how the consequences of managerial policy are fundamentally contingent on the structural environment in which they are applied.