Advice Quality

Advice quality research investigates how effectively humans and algorithms utilize predictions or recommendations (advice) to improve decision-making, focusing on balancing optimal performance when advice is accurate with robustness to inaccurate advice. Current research explores this across diverse domains, employing techniques like online algorithms with adaptive protection levels, reinforcement learning for advice conformance verification, and analysis of human-AI interaction in decision-making scenarios. Understanding and optimizing advice utilization has significant implications for various fields, including resource allocation, revenue management, and human-computer interaction, by improving the effectiveness of both human and AI-driven systems.