Decision making subject to uncertain information, whether fake or factual, in the context of management of socio-technical systems, is critically discussed from both philosophical and operational perspectives. In dealing with possible fake, incorrect and/or factual information we take the perspective that any information utilized as basis for supporting decisions, has to be dealt with in exactly the same manner-in accordance with Bayesian decision analysis. The important issue is to identify and model the scenarios through which information may cause adverse consequences and to account for their potential effects on the system representation applied as basis for decision optimization. To this end we first provide a mapping of how information affects the decision making context and a categorization of causes for information leading to adverse consequences. Secondly, we introduce a decision analytical framework aiming to optimize decision alternatives for managing systems in the context of systems representations including not only one possible system model but a set of different possible system models. As a means for assessing the benefit of collecting additional information, we utilize Value of Information analysis from Bayesian decision analysis. Finally, a principal example is provided which illustrates and discusses selected aspects of how possibly fake information affects decision making and how it might be dealt with.
In the present paper, we propose a novel decision analytical framework for systems modeling in the context of risk informed integrity management of offshore facilities. Our focus concerns the development of system models representing environmental loads associated with storm events. Appreciating that system models in general serve to facilitate the optimal ranking of decision alternatives, we formulate the problem of systems modeling as an optimization problem to be solved jointly with the ranking of decision alternatives. Taking offset in recent developments in structure learning and Bayesian regression techniques, a generic approach for the modeling of environmental loads is established, which accommodates for a joint utilization of phenomenological understanding and knowledge contained in databases of observations. In this manner, we provide a framework and corresponding techniques supporting the combination of bottom-up and top-down modeling. Moreover, since phenomenological understanding as well as analysis of databases may lead to the identification of several competing system models, we include these in the formulation of the optimization problem. The proposed framework and utilized techniques are illustrated on a principal example. The example considers systems modeling and decision optimization in the context of possible evacuation of an offshore facility in the face of an emerging storm event.
This paper presents a novel decision analytical framework for systems modeling in the context of the risk-informed integrity management of offshore facilities. Our focus concerns the development of system models representing environmental loads associated with storm events. Appreciating that system models in general serve to facilitate the optimal ranking of decision alternatives, we formulate the problem of systems modeling as an optimization problem to be solved jointly with the ranking of integrity management decision alternatives. Taking offset in recent developments in structure learning and Bayesian regression techniques, a generic approach for the modeling of environmental loads is established, which accommodates for a joint utilization of phenomenological understanding and knowledge contained in databases of observations. In this manner, we provide a framework and corresponding techniques supporting the combination of bottom-up and top-down modeling. Moreover, since phenomenological understanding and analysis of databases may lead to the identification of several competing system models, we include these in the formulation of the optimization problem. The proposed framework and utilized techniques are illustrated in an example. The example considers systems modeling and decision optimization in the context of a possible evacuation of an offshore facility in the face of an emerging storm event.
In this contribution, we develop and present a Bayesian probabilistic framework for the representation of complex systems and apply this to an industrial case of offshore environmental load modeling. Based on previous contributions on probabilistic modeling using Bayesian networks, we consider the case where both the model structure and its parameters are estimated from data. Gaussian process‐based discrepancy modeling is introduced to represent uncertainties associated with data, when data are produced by models themselves. Two approaches are then introduced on how to deal with multiple model candidates, that is, Bayesian model averaging and decision context‐specific model selection. The latter comprising the main novelty of this paper. Two examples are provided: (i) a principal example illustrating the simple but fundamental idea of context‐specific model building and (ii) an industrial‐scale example considering optimal ranking of evacuation options for platform personnel in the event of an emerging storm.
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