While individual-based models are attractive for addressing certain types of modeling problems, such models impose (frequently dramatically) higher performance costs for larger populations. Lengthy simulation times inhibit interactive learning, and – given limited modeler time – can impose higher opportunity costs by limiting model comprehension, refinement and user interaction. This paper proposes the novel use of dimensional analysis and scale modeling – which have long played an important role in understanding physical systems – to lessen the performance barriers associated with simulation of individual-based models. Given a dimensionally homogeneous (“full-scale”) simulation model with a large population, we propose a precise, rigorous, systematic and general-purpose technique to formulate a “reduced-scale” individual-based model that simulates a smaller population. Measurements made of particular output parameters of such reduced-scale models can then be precisely transformed (in accordance with model scaling laws) to yield comparable results for a full-scale model – without the need to run the full-scale model. While discretization effects limit the degree of scaling that can be achieved, these techniques are notable in relying only upon dimensional homogeneity of the full-scale model, and on not the specifics of model behavior or use of a particular mathematical framework.