  We use a simulation study to compare three methods for adaptive
experimentation: Thompson sampling, Tempered Thompson sampling, and Exploration
sampling. We gauge the performance of each in terms of social welfare and
estimation accuracy, and as a function of the number of experimental waves. We
further construct a set of novel "hybrid" loss measures to identify which
methods are optimal for researchers pursuing a combination of experimental
aims. Our main results are: 1) the relative performance of Thompson sampling
depends on the number of experimental waves, 2) Tempered Thompson sampling
uniquely distributes losses across multiple experimental aims, and 3) in most
cases, Exploration sampling performs similarly to random assignment.
