The design of an individual-level computational model requires modelers to deal with uncertainty by making assumptions on causal mechanisms (when they are insufficiently characterized in a problem domain) or feature values (when available data does not cover all features that need to be initialized in the model). The simplifications and judgments that modelers make to construct a model are not commonly reported or rely on evasive justifications such as ‘for the sake of simplicity’, which adds another layer of uncertainty. In this paper, we present the first framework to transparently and systematically investigate which factors should be included in a model, where assumptions will be needed, and what level of uncertainty will be produced. We demonstrate that it is computationally prohibitive (i.e. NP-Hard) to create a model that supports a set of interventions while minimizing uncertainty. Since heuristics are necessary, we formally specify and evaluate two common strategies that emphasize different aspects of a model, such as building the ‘simplest’ model in number of rules or actively avoiding uncertainty.