Multi-agent social-network models are becoming increasingly used due to their power and flexibility in capturing emergent behaviors in complex socio-technical systems and their ability to link to real data. These models are growing in size and complexity which requires significant time and effort to calibrate, validate, improve the model, and gain insight into model behavior. In this paper, we present our knowledge-based simulation-aided approach for automating model-improvement and our tool to implement this approach (WIZER). WIZER is capable of calibrating and validating multi-agent social-network models, and facilitates model-improvement and understanding. By employing knowledge-based search, causal analysis, and simulation control and inference techniques, WIZER can reduce the number of simulation runs needed to calibrate, validate, and improve a model and improve the focus of these runs. We ran WIZER on BioWar, a city-scale multi-agent social-network model capable of simulating the effects of weaponized biological attacks on a demographically-realistic population against a background of naturally-occurring diseases. The results show the efficacy of WIZER.