Uncertainty in Rodos

Our objectives in this report are to argue the importance of developing a single, consistent mechanism for handling uncertainty throughout RODOS and to demonstrate that Bayesian approaches to decision support with their coherent use of probability to represent uncertainty provide such a mechanism. We emphasise that uncertainty handling includes a number of important issues from data assimilation and the inclusion of expert judgement through to communicating realistic assessments of uncertainty to the decision makers. We also emphasise that the uncertainty handling mechanism must integrate seamlessly with the evaluation modules within RODOS. RODOS is to be a decision support system built upon a client server architecture. It will have many di erent categories of user at times from the earliest moment that an accident threatens until months and years after an accident. Together these points mean that any module will need to accept input from many others and may provide output to many others. It is not possible for the designers of individual modules to choose how they will handle uncertainty without consideration for the needs of other modules. This would be as ine ective and ine cient as trying to build a system without specifying common data formats: the modules simply would not communicate. Thus we believe that the RODOS management group need to come to a considered opinion on how uncertainty will be handled and ensure that future developments take place within a common framework. We further believe that the Bayesian school of decision support provides such a framework and we spend much of the following justifying this belief; although we would emphasise that this is not intended to be a full speci cation document. For the latter much more detail would be needed. None the less, we try to provide su cient detail for an informed judgement to be made. We would emphasise the breadth of the issues we address. It is, in our view, the overriding strength of the Bayesian approach that such a wide range of issues can be addressed within a single coherent framework. Indeed, within many frameworks it is not (yet? ) possible to even ask some of the questions to which we provide potential answers. For instance, only when there is a well developed concept of correlation or interaction of uncertainties does data assimilation become possible, because one learns from data through the imputed covariation of the data and the unknown. Many rule based and fuzzy methods of inference do not have a sophisticated method of achieving this and thus only provide rough methods of data assimilation. Moreover, the Bayesian approach includes evaluation as well as inference within its structures and is thus able to support all the judgements and decisions required of the decision makers and their advisors. 1 The Bayesian School is closely related other established schools of inference in the sense that when there are many data points, Bayesian estimates agree closely with estimates provided by, for example, least squares or maximum likelihood, which again have sophisticated methods of modelling covariation. However, when data are sparse, Bayesian methods can rely on expert judgements and, moreover, as data become available the estimates move smoothly towards those obtained from entirely data-based methods. In the later sections of the report we discuss how Bayesian methods can support various phases of decision making. In some places we give far greater detail than in others. This re ects the relative e ort we have been able to expend on the di erent areas. Thus our development of a Bayesian version of the Rimpu model is well advanced and we can show speci cally how to address computational issues. On the other hand, the model in section 9 on a Bayesian method for interpolating ground contamination is much less advanced. None the less, we are convinced that its solution can be achieved in a computationally feasible method, when we turn our full attention to it. 2