All publications
Major revision at Manufacturing & Service Operations Management, 2026
Precise or Broad?The Reward-Learning Frontier in Algorithmic Advice Design
Philippe Blaettchen, Wichinpong Park Sinchaisri
In brief
Compares two ways of turning an optimal policy into advice in EV-charging route experiments: an exact action versus a broader rule of thumb. Exact advice performs best while it is visible; broader advice can leave people better able to act alone in similar settings once advice is removed.
Details
- Status
- Major revision at Manufacturing & Service Operations Management
- Year
- 2026
- Research areas
- Algorithmic Advice
Learning on the Job - Presented at
- CHI 2025 Workshop on Human-AI Interaction for Augmented Reasoning (poster), Yokohama, Apr 26, 2025
EC 2025 Workshop on Human-AI Collaboration
2026 talks at INSEAD, the University of Michigan, POMS, the Behavioral Decision Research in Management Conference, the INFORMS Revenue Management & Pricing Conference and YinzOR at Carnegie Mellon
Cite
Blaettchen, P., & Sinchaisri, W. P. (2026). Precise or Broad? The Reward-Learning Frontier in Algorithmic Advice Design.
@unpublished{blaettchen2026precise, title = {Precise or Broad? The Reward-Learning Frontier in Algorithmic Advice Design}, author = {Blaettchen, Philippe and Sinchaisri, Wichinpong Park}, year = {2026} }
