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Simulating for policy making can require modelling multiple aspects of life, realistic social behaviour and the ability to simulate up to millions of agents [1]. However realistic models are not easily scalable due to the complex deliberation that takes into account all information at every time step which is slow. Explicitly taking into account context in the deliberation can increase scalability, through a complexity by need principle. The Dynamic Context-Sensitive Deliberation (DCSD) framework uses minimal information when possible, but gradually draws in more information when necessary. To validate whether DCSD can increase scalability while retaining realism we implement DCSD into an example large scale model, the Agent-based Social Simulation of the Coronavirus Crisis (ASSOCC). We compare the original deliberation from the ASSOCC model with the implemented DCSD. We conclude that DCSD can increase scalability while retaining realism in large scale social simulation models.