It is well established that there is a difference between how people actually make decisions when faced with choices involving uncertainty (i.e., risk) and how classical risk models expect decisions to be evaluated. Typically, these two paradigms have been handled separately under positive behavior-based decision theories, such as Prospect Theory, which accounts for intuition, and under normative economic theories, such as expected utility, which yields the expected value rule of probability times outcome. The Entropy Decision Risk Model (EDRM) provides a translation between the positive decision-making under uncertainty (DMUU) domain and the normative domain. This paper reviews the foundations of EDRM and then provides several sample applications to a number of real-world examples involving complex choices of gains and losses, as well as choices involving extremely small probabilities. Specifically, it demonstrates that EDRM can be used to translate subject perception of risk into terms of objective probabilities, thereby permitting analysis using standard tools, and then back again into the decision-maker’s frame of reference.