For historians, agent-based modeling (ABM) methodologies allow us to formalise our thinking about how the past worked and explore those assumptions in a way previously limited to thought-experiments. In ABM, numerous autonomous, heterogeneous agents are allowed to interact in a digital environment according to rules of behaviour directly drawn from the historical event the researcher wishes to study. The exact number of agents is dependent on the given event, but they should be appropriately scaled to that situation (where “appropriate” is determined on statistical or other clearly defined criteria). This technique allows the researcher to determine the range of possible outcomes – in terms of emergent structures and behaviours − that can be obtained given the specified rules for agent interaction. The purpose of this exercise is to determine whether the model can produce an outcome similar to what has been observed historically, through an adjustment of the model’s parameters. If the model does produce a match, then the historian has a basis to claim that he or she has discovered something new about the conditions that underpinned that historical moment. In a sense, ABM allows the historian to systematically and formally explore a series of counterfactuals based on the historian’s own understanding of the past. The historian becomes the geographer of behaviour space.