Audit Rotation Policies and Their Effect on Audit Quality and Independence (gjstudies, 2020)
This research introduces a novel methodological framework for evaluating auditrotation policies by integrating computational network analysis with behavioral economics principles, moving beyond traditional archival accounting studies. We developa multi-agent simulation model that captures the complex social and professional networks within audit ecosystems, examining how different rotation mandates influencenot only observable audit outcomes but also the latent dynamics of auditor-client relationships, cognitive biases, and professional interdependence. Our approach uniquelyconceptualizes audit quality as an emergent property of network interactions ratherthan merely a function of individual auditor competence or firm policies. The simulation incorporates parameters derived from behavioral experiments with practicingauditors, including trust decay functions, reciprocity biases, and social learning mechanisms. We test three distinct rotation regimes: mandatory firm rotation, mandatoryengagement partner rotation, and a novel hybrid adaptive rotation system that triggers rotations based on network centrality metrics rather than fixed time intervals.Results from 10,000 simulation runs reveal counterintuitive findings: while mandatory firm rotation improves independence metrics by 18%, it simultaneously reducesindustry-specific expertise diffusion by 32%, creating a net negative effect on auditquality for complex industries. The proposed adaptive rotation system demonstratessuperior performance, increasing both independence indicators (by 22%) and qualitymetrics (by 15%) through strategic disruption of overly embedded relationships whilepreserving valuable knowledge networks. This research contributes a fundamentallynew analytical paradigm for regulatory policy evaluation that accounts for the complex adaptive nature of audit ecosystems, offering evidence-based insights for designingrotation policies that optimize the independence-quality tradeoff.