Ambiguity-aware cognitive mapping for epidemic resource allocation

Abstract

Epidemic resource-allocation decisions combine rapidly changing demand, incomplete reporting, and conflicting evidence. We formulate an Ambiguous Cognitive Map (ACM) in which each concept has four coupled evidence channels—true, false, partially true, and partially false. The evidence-conditioned operator maps to a capped convex state space, admits a verifiable contraction condition, and generates priorities for a constrained allocation layer. Evaluation combines 30 paired synthetic supply-chain runs, observational NHS England and U.S. Department of Health and Human Services hospital panels, retrospective GetUsPPE assignment records, and PPE-Match request, offer, and distance streams. Hyperparameters for the two allocation replays are selected by seeded tree-structured Parzen-estimator optimization on temporally separated calibration and validation periods, with the final periods held out. ACM attains NDCG@3 values of 0.948 and 0.905 on the NHS holdout and external HHS panels, respectively, while persistence-based comparators rank subsequent stress more accurately. On held-out GetUsPPE assignments, calibrated ACM reaches an average precision of 0.081 and allocation overlap of 0.008, compared with 0.177 and 0.110 for a scalar baseline. On held-out PPE-Match streams, calibration raises the prespecified operational composite from 0.132 to 0.200 and reduces unit-miles from 1999.6 to 24.8; ACM exceeds demand-only allocation (0.161) but remains below equal priority (0.204). Coupled and independent four-channel variants are operationally indistinguishable in both replays. ACM consequently provides a mathematically bounded and auditable ambiguity-aware decision interface, with performance that depends on the operational endpoint and data environment.