  It is a long-standing objective to ease the computation burden incurred by
the decision making process. Identification of this mechanism's sensitivity to
simplification has tremendous ramifications. Yet, algorithms for decision
making under uncertainty usually lean on approximations or heuristics without
quantifying their effect. Therefore, challenging scenarios could severely
impair the performance of such methods. In this paper, we extend the decision
making mechanism to the whole by removing standard approximations and
considering all previously suppressed stochastic sources of variability. On top
of this extension, our key contribution is a novel framework to simplify
decision making while assessing and controlling online the simplification's
impact. Furthermore, we present novel stochastic bounds on the return and
characterize online the effect of simplification using this framework on a
particular simplification technique - reducing the number of samples in belief
representation for planning. Finally, we verify the advantages of our approach
through extensive simulations.
