Accounting Information Reliability and Investor Decision Making Processes

Abstract

This research introduces a novel, cross-disciplinary framework that reconceptualizes accounting information reliability through the lens of computational neuroscience and quantum-inspired information theory, moving beyond traditional auditbased verification models. We propose that investor decision-making processes are
not merely rational evaluations of verified data but complex, adaptive systems influenced by the quantum-like superposition of information states and neural predictive
coding mechanisms. Our methodology employs a hybrid approach combining agentbased modeling simulated with quantum probability amplitudes, functional neuroimaging (fMRI) correlates of investor trust formation, and a large-scale analysis
of non-audit informational signals in corporate disclosures. We formulate the problem not as one of achieving perfect reliability, but of understanding how investors
construct subjective reliability from inherently noisy, multi-source data streams, including unstructured textual sentiment, temporal patterns in disclosure timing, and
the social network dynamics of information dissemination. Results from our simulated market and neuro-behavioral experiments reveal that investors consistently
overweight coherent, narrative-driven information structures—even when accompanied by weaker traditional audit assurances—and underweight statistically robust
but narratively disjointed data. We identify a ’neural coherence threshold’ where
anterior cingulate cortex activity shifts, marking the subjective transition from information assessment to decision commitment, which is more strongly predicted by
narrative consistency metrics than by audit opinion modifications. Furthermore,
our quantum-agent model demonstrates that markets can exist in a superposition
of trust states regarding firm disclosures, with collapse to a definitive ’trust’ or
’distrust’ state being triggered by specific, often non-financial, informational catalysts. This research contributes original insights by bridging computational theory,
neuroscience, and accounting, offering a fundamentally new paradigm for understanding how reliability is perceived and processed, with significant implications
for disclosure regulation, audit communication, and the design of decision-support
systems for investors.