This research explores the behavioral economics of artificial intelligence, specifically how large language models function as unique economic agents with distinct decision-making patterns. The research identifies a preference-belief asymmetry, noting that advanced AI often mimics human-like irrationality in subjective tasks while exhibiting superior statistical reasoning in objective assessments. These systematic biases pose significant operational and regulatory risks for sectors like finance and healthcare, where flawed AI logic can lead to financial loss or medical errors. To address these vulnerabilities, the research advocates for evidence-based organizational responses, including structured behavioral testing and hybrid human-AI workflows. Ultimately, the research emphasizes that systematic oversight and interdisciplinary governance are essential for safely integrating these evolving models into critical decision-making environments.

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