Described is a system and method for automated discovery of unknown patterns from multiple heterogeneous datasets in support of decision making. The system processes input data with a coarse-graining module to generate multi-scale symbolic representations of the input data having various levels of granularity. If the input data contains missing data, then values are computed for the missing data with an effective information transfer module. The multi-scale symbolic representations are sent to a local information dynamics module and a non-myopic value of information module. Previously unknown patterns in the multi-scale symbolic representations are discovered and extracted with the local information dynamics module. The previously unknown patterns and the multi -scale symbolic representations of the input data are analyzed, and the value of acquiring new input data is ranked with the non-myopic value of information module.
Multi-scale information dynamics for decision making