A comparison of simplified value function approaches for treating uncertainty in multi-criteria decision analysis

Uncertainty is present in many decisions where an action's consequences are unknown because they depend on future events. Multi-attribute utility theory (MAUT) offers an axiomatic basis for choice, but practitioners may prefer to use simpler decision models for transparency, ease of use, or other practical reasons. We identify some ‘simplified’ models currently in use and use a simulation experiment to evaluate their ability to approximate results obtained using MAUT. Our basic message is that avoiding assessment errors in the application of a simplified model is more important than the choice of a particular type of model, but that the best performance over a range of decision problems is from a model using a small number of quantiles.

► We use simulation to compare a number of models using simplified representations of uncertainty with multi-attribute utility theory.
► For good results, avoiding assessment errors is more important than the choice of a particular type of decision model.
► Over a range of decision problems, the best performance is given by a model using a small number of quantiles.
► Models using explicit risk attributes like variances or probabilities of failure perform relatively poorly.
► Models using scenarios can give good results but this depends heavily on the construction of scenarios.