Strategic Shaping of Human Prosociality: A Latent-State POMDP Framework
Abstract
We propose a decision-theoretic framework in which a robot strategically can shape inferred human's prosocial state during repeated interactions. Modeling the human's prosociality as a latent state that evolves over time, the robot learns to infer and influence this state through its own actions, including helping and signaling. We formalize this as a latent-state POMDP with limited observations and learn the transition and observation dynamics using expectation maximization. The resulting belief-based policy balances task and social objectives, selecting actions that maximize long-term cooperative outcomes. We evaluate the model using data from user studies and show that the learned policy outperforms baseline strategies in both team performance and increasing observed human cooperative behavior.
Fields of Study & Subjects
Evolutionary Game Theory and Cooperation
Experimental Behavioral Economics Studies
Game Theory and Applications
Publication details
- Journal / source
- IEEE Robotics and Automation Letters
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Volume
- 11
- Issue
- 4
- Pages
- 4729–4736
- Publication date
- 25 February 2026