Spatially explicit agent-based models integrate human and environmental systems and reveal patterns that arise from a multitude of individual decisions. Properly defining micro-scale behaviours therefore has large importance for macro outcomes, bringing model validation and calibration issues into focus as a challenge for the research community. This paper describes techniques to empirically calibrate representations of decision making processes in agent-based models. Examples are reviewed using survey data, participatory approaches with geovisualisation and experimental economics. Novel approaches are presented, including experimental economics directly integrated with a spatially explicit agent-based model to reveal trading behaviours in markets. The experimental economics calibration tool directly integrated with an agent-based model reduces the need for interpretation in subsequent use of participant data to re-calibrate artificial agents.