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The issue of controlling the ratio of exploration and exploitation in agent learning in dynamic environments provides a continuing challenge in the application of agent learning techniques. Methods to control this ratio in a manner that mimics human behavior are required for use in the representation of human behavior, which seek to constrain agent learning mechanisms in a manner similar to that observed in human cognition. This paper describes the use of two novel methods for adjusting the exploration and exploitation ratio of agents using a Cultural Geography (CG) Model.