(untitled) (gjstudies, 2017)
This research introduces a novel, computational framework for analyzing andstrengthening the oversight functions of corporate audit committees, a domain traditionally dominated by qualitative, compliance-based assessments. Departing fromconventional governance studies, we formulate the audit committee’s role as a complex adaptive system and apply principles from computational organizational theory, network analysis, and information theory to model its oversight efficacy. Weposit that financial accountability is not merely a product of regulatory adherencebut emerges from specific structural and informational dynamics within the committee’s interactions with management, internal audit, and external auditors. Ourmethodology constructs a multi-agent simulation model where agents representcommittee members and key organizational actors, programmed with behavioralrules derived from governance literature and empirical data on committee failures.The model incorporates variables for expertise diversity, inquiry intensity, information asymmetry, and social network cohesion to simulate decision-making processesaround financial reporting and fraud risk assessment. A key innovation is the application of a ’supervision entropy’ metric to quantify the predictability and robustnessof oversight interactions. We test this framework using a unique dataset comprisingdetailed minutes from 45 audit committee meetings of anonymized firms, which weprocess using natural language processing techniques to extract interaction patternsand topic focus. Our results identify a non-linear relationship between committeemeeting frequency and oversight effectiveness, with a threshold point beyond whichadditional meetings yield diminishing returns. More significantly, we find thatcommittees with optimal ’informational network centrality’—acting as integratorsrather than passive recipients of reports—exhibit a 40