This chapter describes addiction as a failure of decision-making systems. Existing computational theories of addiction have been based on temporal difference (TD) learning as a quantitative model for decision-making. In these theories, drugs of abuse create a non-compensable TD reward prediction error signal that causes pathological overvaluation of drug-seeking choices. However, the TD model is too simple to account for all aspects of decision-making. For example, TD requires a state-space over which to learn. The process of acquiring a state-space, which involves both situation classification and learning causal relationships between states, presents another set of vulnerabilities to addiction. For example, problem gambling may be partly caused by a misclassification of the situations that lead to wins and losses. Extending TD to include state-space learning also permits quantitative descriptions of how changing representations impacts patterns of intertemporal choice behavior, potentially reducing impulsive choices just by changing cause-effect beliefs. This approach suggests that addicts can learn healthy representations to recover from addiction. All the computational models of addiction published so far are based on learning models that do not attempt to look ahead into the future to calculate optimal decisions. A deeper understanding of how decision-making breaks down in addiction will certainly require addressing the interaction of drugs with model-based look-ahead decision mechanisms, a topic that remains unexplored.