(untitled) (gjstudies, 2020)
This research introduces a novel, cross-disciplinary framework for analyzing behavioral biases in accounting estimates and auditing judgments by integrating principlesfrom cognitive neuroscience, behavioral economics, and computational psychology. Unlike traditional studies that examine biases in isolation, we propose a dynamic, systemsbased model that captures how multiple biases interact within complex decision-makingenvironments characteristic of modern financial reporting. Our methodology employs a unique hybrid approach combining agent-based computational simulations withneuroimaging-inspired network analysis to map bias propagation through accountingjudgment pathways. We develop a computational model simulating 1,000 virtual accounting professionals making estimates under varying conditions of uncertainty, timepressure, and regulatory scrutiny. The model incorporates fourteen documented cognitive biases, including confirmation bias, anchoring, overconfidence, and availabilityheuristic, but extends beyond conventional treatment by modeling their nonlinear interactions and feedback loops. Our results reveal three original findings: first, biasinteractions create emergent systemic distortions that exceed the sum of individualbias effects; second, environmental factors like digital reporting systems and continuous auditing protocols alter traditional bias manifestations in unexpected ways; third,we identify previously undocumented ’compensatory bias patterns’ where certain biascombinations paradoxically improve estimate accuracy under specific conditions. Weintroduce the concept of ’cognitive resonance’ in accounting judgments, where alignedbiases amplify distortions, while misaligned biases sometimes create corrective interference. The research contributes a new theoretical lens for understanding judgmentquality in accounting and offers practical, technology-mediated interventions designednot to eliminate biases (an often unrealistic goal) but to strategically manage theirsystemic effects through intelligent system design and bias-aware auditing protocols.