Over the last two decades attempts to quantify decision-making have established that, under a wide range of conditions, people trade-off effectiveness for efficiency in the strategies they adopt. However, as interesting, significant, and influential as this research has been, its scope is limited by three factors; the coarseness of how effort was measured, the confounding of the costs of steps in the decision-making algorithm with the costs of steps in a given task environment, and the static nature of the decision tasks studied. Across a series of experiments, we embed decision-making tasks into dynamic task environments and vary the cost required for various steps. Across studies, small changes in the cost of interactive behavior leads to changes in the strategy adopted for decision-making as well as to differences in how a step in the same strategy is implemented. Work is proceeding to construct a family of ACT-R models, simBorgs, that perform aspects of the DMAP tasks in the same way as humans.