  Multi-fidelity methods combine inexpensive low-fidelity simulations with
costly but high-fidelity 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 different conditions.
