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This work describes a cognitively realistic approach to social simulation. It begins with a model created by Gilbert for capturing the growth of academic science. Gilbert's model, which was equation-based, is replaced here by an agent-based (neural network) model, with the (neural network based) cognitive architecture CLARION providing greater cognitive realism. Using this agent model, results comparable to previous simulations and to human data are obtained. It is found that while different cognitive settings may affect the aggregate number of scientific articles produced by the model, they do not generally lead to different distributions of number of articles per author. It is argued that using more cognitively realistic models in simulations may lead to novel insights.