An air of paradox attends the problem of modeling uncertain events in an ABM or other simulation. If no known probability distribution is associated with a collection of uncertain possible events, how are we to compute their realizations in a simulation? This study is about decision making under uncertainty, a quite common circumstance and one of core interest to agent-based modeling. In the context of decision making under uncertainty we addressed two main questions: We proposed and implement an uncertainty number generator, which affords an answer to the second question. We find that uncertainty decision rules can identify more valuable alternatives among the consideration set and upon modeling with the uncertain number generator these identifications are robust. Agents lacking probabilities for the eventualities (agents engaging in decision making under uncertainty) can make sensible choices from among the available alternatives and do so robustly to uncertain events. We have demonstrated all of this with a class of examples which we believe is appropriate. Further research will be required to gain insight on how widely our findings generalize.