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We contrasted and compared independently developed computational models of human performance in a common dynamic decision-making task. The task, called Dynamic Stocks and Flows, is simple and tractable enough for laboratory experiments yet exhibits many characteristics of macrocognition. A macrocognitive model was developed using a computational instantiation of Recognition-Primed Decision-Making. A microcognitive model was developed using the ACT-R cognitive architecture. Both models followed an instance-based learning paradigm and displayed striking similarities, including their constraints, limitations, and the key breakthrough that enabled satisfactory (though still short of human-like) performance, suggesting the emergence of a general design pattern. On the basis of this comparison we argue that while some substantive differences remain, microcognitive and macrocognitive approaches provide complementary rather than contradictory accounts of human behavior