  The ability to uncover preferences from choices is fundamental for both
positive economics and welfare analysis. Overwhelming evidence shows that
choice is stochastic, which has given rise to random utility models as the
dominant paradigm in applied microeconomics. However, as is well known, it is
not possible to infer the structure of preferences in the absence of
assumptions on the structure of noise. This makes it impossible to empirically
test the structure of noise independently from the structure of preferences.
Here, we show that the difficulty can be bypassed if data sets are enlarged to
include response times. A simple condition on response time distributions (a
weaker version of first order stochastic dominance) ensures that choices reveal
preferences without assumptions on the structure of utility noise. Sharper
results are obtained if the analysis is restricted to specific classes of
models. Under symmetric noise, response times allow to uncover preferences for
choice pairs outside the data set, and if noise is Fechnerian, even choice
probabilities can be forecast out of sample. We conclude by showing that
standard random utility models from economics and standard drift-diffusion
models from psychology necessarily generate data sets fulfilling our sufficient
condition on response time distributions.
