Belief Updating and Delegation in Multi-Task Human–AI Interaction: Evidence from Controlled Simulations

This paper addresses a fundamental challenge for product directors building AI-powered products: how do users develop trust and delegation strategies when the same AI system performs differently across different tasks? The controlled experimental approach reveals that people form overly general beliefs about AI capability, leading to both over-reliance in weak domains and under-utilization in strong ones. For product teams, this suggests that interface design must actively help users calibrate their expectations task by task, rather than assuming they will naturally learn appropriate delegation boundaries. The work extends Herbert Simon's insights about bounded rationality into the multi-task AI era, where the complexity of forming accurate beliefs about system capability becomes a key design constraint.