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Floating point arithmetic is a subject all too often ignored, yet, for agent-based models in particular, it has the potential to create misleading results, and even to influence emergent outcomes of the model. Using a simple demonstration model, this paper illustrates the problems that accumulated floating point errors can cause, and compares a number of techniques that might be used to address them. We show that inexact representation of parameter values, imprecision in calculation results, and differing implementations of mathematical expressions can significantly influence the behaviour of the model, and create issues for replicating results, though they do not necessarily do so. None of the techniques offer a failsafe approach that can be applied in any situation, though interval arithmetic is the most promising.