Optimizing the Ineffable: Generative Policy Learning for Human-Centered Decision-Making

Algorithmic decision-making is widely adopted in high-stakes applications affecting our daily lives but often requires human decision-makers to exercise their discretion within the process to ensure alignment. Explicitly modeling human values and preferences is challenging when tacit knowledge is difficult to formalize, as Michael Polanyi observed, "We can know more than we can tell." To address this challenge, we propose generative near-optimal policy learning (GenNOP). Our framework leverages a conditional generative model to reliably produce diverse, near-optimal, and potentially high-dimensional stochastic policies. Our approach involves a re-weighting scheme for training generative models according to the estimated probability that each training sample is near-optimal. Under our framework, decision-making algorithms focus on a primary, measurable objective, while human decision-makers apply their tacit knowledge to evaluate the generated decisions, rather than developing explicit specifications for the ineffable, human-centered objective. Through extensive synthetic and real-world experiments, we demonstrate the effectiveness of our method.