An Expert-Opinion Approach to the Prediction Problem in Complex Systems

The use of model forecasts for decision making should be optimized. With this in mind, the concept of modelling the future is discussed from an epistemological point of view and on the basis of a stochastic model interpretation. Traditional definitions of model statistics make reference to an ensemble of systems. Since this does not work for a complex system with a unique state, an alternative approach, based on the subjective (Delphi) opinion of a group of experts, is also considered. This approach is then generalized to the situation in which a set of competing models is available. With a Delphi method a certain likelihood can be assigned to each model. Once the statistics is defined, one may face the issue of predictability. In hindsight (in a ‘hindcasting mode’) models can be validated by checking how accurate they have been describing observations and they can be falsified when their predictions differ in an unlikely way from the observations. ‘Forecasting’ is different, because models can never be proven. Therefore, exact prediction of the future is impossible. Definitions of predictability (two examples will be given) necessarily refer to the range of modelled possibilities. It is argued that all model predictions — also those resulting from physical models — should be considered as scenarios. To make rational decisions the likelihood of all possible model forecasts has to be taken into account. In case of complex systems and difficult decisions it appears useful to consider a large variety of models. Experts need not strive for consensus, because a diversity of opinions could lead to better decisions. It is recommended that more attention is paid to Delphi aspects of forecast likelihoods.