SMIC Prob-Expert

Because the probabilities experts supply are almost never mutually consistent. When someone gives a simple probability for each hypothesis and conditional probabilities linking them, those numbers usually violate the laws of probability that bind marginals and conditionals together, so no genuine joint distribution exactly matches them. SMIC's quadratic program searches for the joint distribution over all combinations that obeys the probability axioms while minimising the squared distance from the expert's raw estimates. The quadratic objective makes the correction the smallest possible adjustment that restores coherence, so the expert's intent is preserved as far as logic allows while the contradictions are removed.

An image of the future is one complete combination of the hypotheses, with each hypothesis set to either realised or not. With n binary hypotheses there are two-to-the-n such images, and each corresponds to a distinct scenario. SMIC's purpose is to assign a coherent net probability to every image, so that the analyst can see not just how likely each individual hypothesis is but how likely each full pattern of outcomes is. Ranking the images by their net probabilities identifies the core scenarios — usually a few combinations that hold most of the probability mass and are worth developing in detail.

Both are matrix-based tools in la prospective but they do different jobs. MICMAC performs structural analysis, classifying the system's variables by influence and dependence to reveal which are the key drivers, and produces no probabilities. SMIC works at a later, quantifying stage: it takes a small set of binary hypotheses, elicits simple and conditional probabilities from experts, and computes a coherent probability distribution over scenario combinations to rank them by likelihood. In short, MICMAC tells you which variables matter, while SMIC tells you which combinations of outcomes are most probable, so they are complementary rather than alternatives.