  Forecasts support decision making in a variety of applications. Statistical
models can produce accurate forecasts given abundant training data, but when
data is sparse, rapidly changing, or unavailable, statistical models may not be
able to make accurate predictions. Expert judgmental forecasts---models that
combine expert-generated predictions into a single forecast---can make
predictions when training data is limited by relying on expert intuition to
take the place of concrete training data. Researchers have proposed a wide
array of algorithms to combine expert predictions into a single forecast, but
there is no consensus on an optimal aggregation model. This scoping review
surveyed recent literature on aggregating expert-elicited predictions. We
gathered common terminology, aggregation methods, and forecasting performance
metrics, and offer guidance to strengthen future work that is growing at an
accelerated pace.
