These sources advocate for Applied Information Economics, a framework for quantifying seemingly immeasurable variables like security, quality, and risk. The author argues that measurement is the reduction of uncertainty, rather than the elimination of it, to support better business decisions. To achieve this, practitioners should use calibrated estimators—experts trained to provide accurate probability ranges—and Monte Carlo simulations to model potential outcomes. The text criticizes popular scoring methods for lacking empirical validity, suggesting instead that Bayesian analysis and statistical sampling can provide meaningful data with surprisingly small amounts of information. Ultimately, the goal is to compute the value of information to ensure that measurement efforts are focused on the variables with the highest impact on a decision’s economic consequences.