Institute for Mathematical Behavioral Sciences

Effective decision-making for crisis response depends upon the rapid integration of limited information from (possibly unreliable) human sources. Here, a Bayesian modeling framework is developed for inference from informant reports. Reports are assumed to arrive via a Poisson-like process, whose rates are dependent upon the (unknown) state of the world in addition to assorted covariates. A hierarchical modeling structure is used to represent error processes which vary based on informants’ group memberships, with the possibility of multiple, overlapping memberships for each informant. Procedures are shown for sampling from the joint posterior distribution of the parameters, and for obtaining posterior predictive quantities.