Behavioral Biases Affecting Accounting Estimates and Auditing Judgments

Abstract

This research introduces a novel, cross-disciplinary framework for analyzing behavioral biases in accounting estimates and auditing judgments by integrating principles
from 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-making
environments characteristic of modern financial reporting. Our methodology employs a unique hybrid approach combining agent-based computational simulations with
neuroimaging-inspired network analysis to map bias propagation through accounting
judgment pathways. We develop a computational model simulating 1,000 virtual accounting professionals making estimates under varying conditions of uncertainty, time
pressure, and regulatory scrutiny. The model incorporates fourteen documented cognitive biases, including confirmation bias, anchoring, overconfidence, and availability
heuristic, but extends beyond conventional treatment by modeling their nonlinear interactions and feedback loops. Our results reveal three original findings: first, bias
interactions create emergent systemic distortions that exceed the sum of individual
bias 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 bias
combinations paradoxically improve estimate accuracy under specific conditions. We
introduce the concept of ’cognitive resonance’ in accounting judgments, where aligned
biases amplify distortions, while misaligned biases sometimes create corrective interference. The research contributes a new theoretical lens for understanding judgment
quality in accounting and offers practical, technology-mediated interventions designed
not to eliminate biases (an often unrealistic goal) but to strategically manage their
systemic effects through intelligent system design and bias-aware auditing protocols.