Abstract: An optimizing data-to-learning-to-action method and system identifies uncertainties embodied as probability distributions that influence a sequence of decisions. The uncertainties are mapped to a simulation of a computer-based infrastructure that supports the execution of the decisions. Actions with respect to the infrastructure that are expected to reduce the uncertainties are simulated. The probability distributions are updated accordingly for each simulated action and an associated net value of information for each simulated action is generated. The action with the greatest net value of information is implemented and the simulated infrastructure is updated accordingly. The process may then be re-run based upon the updated simulated infrastructure.