We consider a problem of decision under uncertainty with outcomes distributed over time. We propose a rough set model based on a combination of time dominance and stochastic dominance. For the sake of simplicity we consider the case of traditional additive probability distribution over the set of states of the world, however, we show that the model is rich enough to handle non-additive probability distributions, and even qualitative ordinal distributions. The rough set approach gives a representation of decision maker’s time-dependent preferences under uncertainty in terms of "if..., then..." decision rules induced from rough approximations of sets of exemplary decisions.
|Number of pages||35|
|Journal||Annals of Operations Research|
|Publication status||Published - Apr 2010|