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Many phenomena in judgment and decision making are often attributed to the interaction of 2 systems of reasoning. Although these so-called dual process theories can explain many types of behavior, they are rarely formalized as mathematical or computational models. Rather, dual process models are typically verbal theories, which are difficult to conclusively evaluate or test. In the cases in which formal (i.e., mathematical) dual process models have been proposed, they have not been quantitatively fit to experimental data and are often silent when it comes to the timing of the 2 systems. In the current article, we present a dynamic dual process model framework of risky decision making that provides an account of the timing and interaction of the 2 systems and can explain both choice and response-time data. We outline several predictions of the model, including how changes in the timing of the 2 systems as well as time pressure can influence behavior. The framework also allows us to explore different assumptions about how preferences are constructed by the 2 systems as well as the dynamic interaction of the 2 systems. In particular, we examine 3 different possible functional forms of the 2 systems and 2 possible ways the systems can interact (simultaneously or serially). We compare these dual process models with 2 single process models using risky decision making data from Guo, Trueblood, and Diederich (2017). Using this data, we find that 1 of the dual process models significantly outperforms the other models in accounting for both choices and response times.