Posted: Aug 12, 2024
This research presents a novel computational framework for analyzing the complex interplay between regulatory oversight mechanisms and audit firm independence through the lens of multi-agent reinforcement learning and network dynamics. Traditional approaches to studying audit regulation have relied heavily on survey data and archival financial records, which often fail to capture the emergent behavioral patterns and strategic adaptations that occur in response to regulatory changes. Our methodology introduces an innovative simulation environment where audit firms, corporate clients, and regulatory bodies interact as autonomous agents with evolving strategies and relationship networks. We model the audit ecosystem as a dynamic complex adaptive system, incorporating principles from behavioral economics, institutional theory, and computational social science. The framework captures how audit firms navigate the dual pressures of maintaining professional independence while preserving lucrative client relationships under varying regulatory scrutiny levels. Our results reveal several counterintuitive findings: moderate regulatory intensity can paradoxically strengthen client dependence on specific audit firms, stringent oversight may trigger the development of sophisticated compliance-avoidance strategies that undermine intended protections, and the effectiveness of regulatory interventions is highly dependent on the existing network structure of audit-client relationships. The study contributes to the auditing literature by providing a computational laboratory for testing regulatory policies before implementation and offers novel insights into the non-linear dynamics of professional independence in financial markets. Our findings challenge conventional wisdom about regulatory design and suggest that optimal oversight strategies must account for the adaptive nature of audit firms and their evolving relationship networks with clients.
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