Modeling choice and search in decisions from experience: A sequential sampling approach

In decisions from experience (DFE), people sample from two or more lotteries prior to making a consequential choice. Although existing models can account for how sampled experiences relate to choice, they don't explain decisions about how to search (in particular, when to stop sampling information). We propose that both choice and search behavior in this context can be understood as a sequential sampling process whereby decision makers sequentially accumulate outcome information from each option to form a preference for one alternative over the other. We formalize this process in a new model, Choice from Accumulated Samples of Experience (CHASE). The model provides a good account of choice behavior and goes beyond existing models by explaining variations in sample size under different task conditions. This approach offers a process-level framework for understanding how interactions between the choice environment and properties of the decision maker give rise to decisions from experience.