This thesis demonstrates that agent-based modelling offers a viable compatriot to traditional experimental methodologies for criminology scholars, that can be applied to explore the divide between micro-level criminological theory and macro-level observations of crime; and in turn, aid in the assessment of those theories which aim to describe the crime event. The following overarching research question is addressed: Are the micro-level mechanisms of the opportunity theories generatively sufficient to explain macroscopic patterns commonly observed in the empirical study of crime? Drawing on the approach of generative social science (Epstein, 1999), this thesis presents a systematic assessment of the generative sufficiency of three distinct mechanisms of offender movement, target selection and learning derived from the routine activity approach (Cohen & Felson, 1979), rational choice perspective (Clarke, 1980; Cornish & Clarke, 1986) and crime pattern theory (Brantingham & Brantingham, 1978, 1981). An agent-based model of offending is presented, in which an artificial landscape is inhabited by both potential victims and offenders who behave according to several of the key propositions of the routine activity approach, rational choice perspective and crime pattern theory. Following a computational laboratory-based approach, for each hypothetical mechanism studied, control and experimental behaviours are developed to represent the absence or presence of a proposed mechanism within the virtual population.