Predicting the behavior of interacting humans by fusing data from
  multiple sources
  Multi-fidelity methods combine inexpensive low-fidelity simulations with
costly but highfidelity simulations to produce an accurate model of a system of
interest at minimal cost. They have proven useful in modeling physical systems
and have been applied to engineering problems such as wing-design optimization.
During human-in-the-loop experimentation, it has become increasingly common to
use online platforms, like Mechanical Turk, to run low-fidelity experiments to
gather human performance data in an efficient manner. One concern with these
experiments is that the results obtained from the online environment generalize
poorly to the actual domain of interest. To address this limitation, we extend
traditional multi-fidelity approaches to allow us to combine fewer data points
from high-fidelity human-in-the-loop experiments with plentiful but less
accurate data from low-fidelity experiments to produce accurate models of how
humans interact. We present both model-based and model-free methods, and
summarize the predictive performance of each method under dierent conditions.
