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
