Autonomous Budget Reallocation Under Revenue Uncertainty: Balancing Financial Performance, Risk Limits, and Managerial Oversight
Keywords:
Autonomous Budgeting, Budget Reallocation, Revenue Uncertainty, Managerial Oversight, Financial Risk, Management Control, SimulationAbstract
Static budgets can become misaligned when realized revenue departs from forecast, but unconstrained automated reallocation can transfer financial authority without adequate accountability. This paper develops a Governed Autonomous Budget Reallocation Model and evaluates fixed budgeting, deterministic rule-based reallocation, and human-supervised autonomous reallocation. A reproducible synthetic experiment models six departments over 12 monthly periods, five revenue environments, and 500 matched Monte Carlo paths per environment. Actual revenue is generated from bounded normal shocks; departmental floors, caps, adjustment limits, reserve safeguards, and materiality-based review are enforced. Outcomes include organizational value, financing gaps, utilization, cash reserve, allocation volatility, limit violations, managerial interventions, and a stated simulation-specific risk-adjusted performance measure. Across all paths, fixed budgeting produced the highest unconstrained value but a mean cumulative financing gap of 9.506 and a negative final reserve. Human-supervised reallocation reduced the gap to 0.994 and achieved mean risk-adjusted performance of 6386.500, compared with 4864.115 for fixed budgeting. It led four of five scenarios; deterministic rules performed better under the severe negative shock and marginally higher in the pooled average. Managers modified or rejected 14,544 of 30,000 proposals. The findings demonstrate a conditional performance-governance trade-off rather than universal superiority. Synthetic assumptions, simplified value functions, and simulated oversight require validation with real organizational data and governance processes.
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