AGGREGATION OF FORECASTS FROM MULTIPLE SIMULATION MODELS

When faced with output from multiple simulation models, a decision maker must aggregate the forecasts provided by each model. This problem is made harder when the models are based on similar assumptions or use overlapping input data. This situation is similar to the problem of expert judgment aggregation where experts provide a forecast distribution based on overlapping information, but only samples from the output distribution are obtained in the simulation case. We propose a Bayesian method for aggregating forecasts from multiple simulation models. We demonstrate the approach using a climate change example, an area often informed by multiple simulation models.