Abstract
Abstract This work focuses on the modeling of multistage stochastic problems with endogenous (decision dependent) uncertainties. We assume that the probability distributions of the uncertain parameters are discrete, so that a scenario tree representation can be used. As the main contribution, the paper describes an approach to represent the gradual resolution of endogenous uncertainties after an investment in information is made; partial resolution of uncertainty through time is defined in terms of a percentage of variance reduction. The approach is based on the concepts of posterior and revelation distributions and on the practical propositions of the theory of conditional expectations. A mining production planning problem with endogenous uncertainty in ore quality is used as a case-study to show the scope of the proposed representation as well as to evaluate the effect of the gradual resolution of uncertainties on the optimal solution.
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