From understanding our distant past to building systems of future, useful simulations demand 'efficient models'. Standing in the way is the twofold challenge of restraining complexity and scale of models. We describe these traits in view of component-based model development. We substantiate the roles complexity and scale play in view of modeling formalisms. We propose semi-formal modeling methods, in contrast to formal, are suitable for qualifying/quantifying model complexity and scale. For structural abstractions, we use class and component models. For behavioral abstractions, we use activity and state machines models. Furthermore, we consider these traits from the vantage point of having families of component-based models. We exemplify the concept and approach by developing families of DEVS models in the COSMOS framework supporting DEVS-based activity and state machines models that persist in relational databases across multiple model development sessions. We conclude by discussing future research directions for real-time and heterogeneous model composability.