Most agent-based models in the organizational sciences employ some variant of hill-climbing algorithms for representing human decision-making behavior. However, experimental research suggests that hill-climbing might not appropriately represent managerial behavior, while satisficing is an empirically relevant representation. Against this background, this paper takes a step forward to explore the impact of the algorithmic representation of decision-making behavior on the results of agent-based models, especially for emerging macro-patterns. Based on the framework of NK-fitness landscapes, the paper employs an agent-based model of rudimentary organizations for distributed decision-making. The results suggest that switching from hill-climbing to satisficing shifts the trade-off between “stability and enhancement of search” to the latter. Moreover, for the macro-pattern “performance declines with increasing complexity” as emerging from hill-climbing, the simulation experiments reveal mixed observations: Not only is satisficing considerably more sensitive to intra-organizational complexity; when local satisficers strive for global performance, the macro-pattern does not universally emerge. These findings suggest that further research on how our agent-based models represent human decision-making behavior appears necessary.