Choice Heterogeneity as Structured Deviations from a Shared Value Anchor

Action preferences can differ systematically across trajectories and over time. In clinical care, patients with similar observed physiological conditions may receive different treatments even when clinicians share the goal of improving patient outcomes. Behavioral prediction alone leaves these persistent differences unexplained against a common reference. We introduce a decomposition of sequential behavior that combines a shared action-value anchor with switching regimes characterized by interpretable action-cost profiles. A continuous-time latent-state model learns interpretable physiological phenotypes and their dynamics from irregular, partially observed records. It supports forecasting, constructs the anchor under a prespecified reward and continuation rule, and provides physiological context for clinical audit. With the anchor fixed, the regime layer learns action-dependent costs within a common feature dictionary, making treatment preferences comparable across regimes. Controlled synthetic experiments support regime and action-cost contrast recovery. In CKD–MBD and MIMIC–IV, the framework improves physiological forecasting and action prediction, while inferred regimes capture persistent treatment patterns. Clinical cases link these patterns to physiological trajectories and decompose anchor-relative departures into treatment-feature contributions, supporting longitudinal clinical audit.