Integrating Behavioral Economics Principles into AI Decision Systems: A Human-Centered Approach to Enhance Decision Making

Traditional AI decision systems often assume rational choice theory, overlooking inherent human cognitive biases and non-rational behaviors, which limits the effectiveness of human-machine interaction, especially in critical sectors like financial investment and healthcare.  Behavioral economics, exemplified by Kahneman and Tversky's prospect theory, demonstrates that human decisions are systematically influenced by heuristics, biases, and context-dependent preferences, leading to phenomena like loss aversion, mental accounting, availability heuristics, anchoring effects, and present bias.  This research proposes an integrated framework to address the theoretical gap in incorporating behavioral economics into AI design.  The framework conceptualizes AI decision systems as adaptive learning mechanisms that explicitly account for human cognitive architectures and behavioral tendencies.  It comprises three interconnected components: user behavior modeling to capture cognitive styles and preferences, personalized recommendation algorithms sensitive to biases, and dynamic learning mechanisms for co-evolutionary adaptation between humans and AI.  This approach aims to move beyond technology-driven design to human-centered design, enhancing the efficacy, acceptability, and ethical implementation of AI systems across various organizational contexts.  The implications extend to the evolving nature of human agency, expertise development, and organizational knowledge management in AI-mediated environments.