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Multi-fidelity methods combine inexpensive low-fidelity simulations withcostly but high-fidelity simulations to produce an accurate model of a systemof interest at minimal cost. They have proven useful in modeling physicalsystems and have been applied to engineering problems such as wing-designoptimization. During human-in-the-loop experimentation, it has becomeincreasingly common to use online platforms, like Mechanical Turk, to runlow-fidelity experiments to gather human performance data in an efficientmanner. One concern with these experiments is that the results obtained fromthe online environment generalize poorly to the actual domain of interest. Toaddress this limitation, we extend traditional multi-fidelity approaches toallow us to combine fewer data points from high-fidelity human-in-the-loopexperiments with plentiful but less accurate data from low-fidelity experimentsto produce accurate models of how humans interact. We present both model-basedand model-free methods, and summarize the predictive performance of each methodunder different conditions.

Predicting the behavior of interacting humans by fusing data from multiple sources