The problem of addiction is examined from a computational perspective, as a malfunction of the brain’s normal decision making systems.  The main theories of drug addiction are dissected and their elements are placed in a decision making framework, with the hope of facilitating clear analysis.  There is some discussion of individual variations and behavioural addictions, and a slightly modified reinforcement learning framework is proposed to facilitate the inclusion of motivational states in modelling analysis.