Trust Under Bounded Rationality: Exploring Human-AI Interaction in Decision-Making Through Large Language Models

This study investigates the role of artificial intelligence (AI) and large language models (LLMs) within Simon’s bounded rationality framework, focusing on factors such as preferences, competence, learning, and persuasion that influence decision-makers’ trust in AI outcomes. Data were collected using mixed methods, including surveys and interviews, followed by descriptive and thematic analyses to explore the trust dynamics in human-AI interactions under bounded rationality. Participants highlighted the effectiveness of AI systems in decision-making constrained by bounded rationality and discussed how AI systems might mitigate these limitations. The findings emphasize the critical role of trust in facilitating effective human-AI interactions, indicating that AI-provided explanations not only support decision-making but also enhance users’ trust in these systems. This study identifies trust as a multifaceted and dynamic aspect of human-AI interactions, suggesting that AI developers can improve trustworthiness through transparency, demonstrated competence, and continuous learning. Enhancing these factors is expected to drive widespread adoption and improve the overall user experience with AI systems.