  We study sequential search without priors. Our interest lies in decision
rules that are close to being optimal under each prior and after each history.
We call these rules dynamically robust. The search literature employs optimal
rules based on cutoff strategies that are not dynamically robust. We derive
dynamically robust rules and show that their performance exceeds 1/2 of the
optimum against binary environments and 1/4 of the optimum against all
environments. This performance improves substantially with the outside option
value, for instance, it exceeds 2/3 of the optimum if the outside option
exceeds 1/6 of the highest possible alternative.
