Not all uncertainty is alike: volatility, stochasticity, and exploration

원문 초록

Adaptive decision-making in biological and artificial intelligence requires balancing the exploitation of known outcomes with the exploration of uncertain alternatives. Although prior work suggests that uncertainty generally promotes exploration, it has typically treated distinct sources of environmental uncertainty as equivalent. We consider environments with latent reward states that drift over time (volatility) and are observed through noisy outcomes (stochasticity). Both of them increase posterior uncertainty, yet we show they drive optimal exploration in opposite directions: volatility enhances it, stochasticity suppresses it. We establish this asymmetry formally by extending the Gittins index framework to Gaussian state-space bandits with latent dynamics. We further derive Cause-Aware Uncertainty-Sensitive Exploration (CAUSE), a closed-form exploration bonus obtained via control-as-inference that inherits the same monotonicities. CAUSE matches or exceeds both standard exploration strategies and numerically computed index policies in environments with heterogeneous noise structure. Learning and exploration are governed by the same noise-inference asymmetry, and the framework predicts that pathological noise inference misallocates exploration in an axis-specific way, with implications for computational accounts of psychiatric conditions.