A Bayesian network approach for coastal risk analysis and decision making

Emergency management and long-term planning in coastal areas depend on detailed assessments (meter scale) of flood and erosion risks. Typically, models of the risk chain are fragmented into smaller parts, because the physical processes involved are very complex and consequences can be diverse. We developed a Bayesian network (BN) approach to integrate the separate models. An important contribution is the learning algorithm for the BN. As input data, we used hindcast and synthetic extreme event scenarios, information on land use and vulnerability relationships (e.g., depth-damage curves). As part of the RISC-KIT (Resilience-Increasing Strategies for Coasts toolKIT) project, we successfully tested the approach and algorithm in a range of morphological settings. We also showed that it is possible to include hazards from different origins, such as marine and riverine sources. In this article, we describe the application to the town of Wells-next-the-Sea, Norfolk, UK, which is vulnerable to storm surges. For any storm input scenario, the BN estimated the percentage of affected receptors in different zones of the site by predicting their hazards and damages. As receptor types, we considered people, residential and commercial properties, and a saltmarsh ecosystem. Additionally, the BN displays the outcome of different disaster risk reduction (DRR) measures. Because the model integrates the entire risk chain with DRR measures and predicts in real-time, it is useful for decision support in risk management of coastal areas.European Community's 7th Framework Programme through the grant to RISC-KIT (Resilience-increasing Strategies for Coasts - Toolkit"), contract no. 603458

Classical meets modern in the IDEA protocol for structured expert judgement

Expert judgement is pervasive in all forms of risk analysis, yet the development of tools to deal with such judgements in a repeatable and transparent fashion is relatively recent. This work outlines new findings related to an approach to expert elicitation termed the IDEA protocol. IDEA combines psychologically robust interactions among experts with mathematical aggregation of individual estimates. In particular, this research explores whether communication among experts adversely effects the reliability of group estimates. Using data from estimates of the outcomes of geopolitical events, we find that loss of independence is relatively modest and it is compensated by improvements in group accuracy

Mining and visualising ordinal data with non-parametric continuous BBNs

Data mining is the process of extracting and analysing information from large databases. Graphical models are a suitable framework for probabilistic modelling. A Bayesian Belief Net (BBN) is a probabilistic graphical model, which represents joint distributions in an intuitive and efficient way. It encodes the probability density (or mass) function of a set of variables by specifying a number of conditional independence statements in the form of a directed acyclic graph. Specifying the structure of the model is one of the most important design choices in graphical modelling. Notwithstanding their potential, there are only a limited number of applications of graphical models on very complex and large databases. A method for mining ordinal multivariate data using non-parametric BBNs is presented. The main advantage of this method is that it can handle a large number of continuous variables, without making any assumptions about their marginal distributions, in a very fast manner. Once the BBN is learned from data, it can be further used for prediction. This approach allows for rapid conditionalisation, which is a very important feature of a BBN from a user's standpoint. © 2008 Elsevier B.V. All rights reserved

Eliciting improved quantitative judgements using the IDEA Protocol: A case study in natural resource management

Introduction Natural resource management uses expert judgement to estimate facts that inform important decisions. Unfortunately, expert judgement is often derived by informal and largely untested protocols, despite evidence that the quality of judgements can be improved with structured approaches. We attribute the lack of uptake of structured protocols to the dearth of illustrative examples that demonstrate how they can be applied within pressing time and resource constraints, while also improving judgements. Aims and methods In this paper, we demonstrate how the IDEA protocol for structured expert elicitation may be deployed to overcome operational challenges while improving the quality of judgements. The protocol was applied to the estimation of 14 future abiotic and biotic events on the Great Barrier Reef, Australia. Seventy-six participants with varying levels of expertise related to the Great Barrier Reef were recruited and allocated randomly to eight groups. Each participant provided their judgements using the four-step question format of the IDEA protocol (‘Investigate’, ‘Discuss’, ‘Estimate’, ‘Aggregate’) through remote elicitation. When the events were realised, the participant judgements were scored in terms of accuracy, calibration and informativeness. Results and conclusions The results demonstrate that the IDEA protocol provides a practical, cost-effective, and repeatable approach to the elicitation of quantitative estimates and uncertainty via remote elicitation. We emphasise that i) the aggregation of diverse individual judgements into pooled group judgments almost always outperformed individuals, and ii) use of a modified Delphi approach helped to remove linguistic ambiguity, and further improved individual and group judgements. Importantly, the protocol encourages review, critical appraisal and replication, each of which is required if judgements are to be used in place of data in a scientific context. The results add to the growing body of literature that demonstrates the merit of using structured elicitation protocols. We urge decision-makers and analysts to use insights and examples to improve the evidence base of expert judgement in natural resource management

InvestigateDiscussEstimateAggregate for structured expert judgement.

This study presents the results of an approach to the prediction of the outcomes of geopolitical events, which we term the IDEA protocol. The participants investigate the background and causal factors behind a question, predict the outcome, and discuss their thinking with others. They then make a second, private and anonymous judgement of the probability of the event, which is subsequently aggregated mathematically. The method performed well relative to both an equally weighted linear pool and a prediction market, and is relatively simple to implement. The results indicate the value of discussion for removing arbitrary linguistic uncertainty and for sharing and debating knowledge, thereby improving the judgements. Weighting individual judgements based on prior performance using Cooke’s method improved group judgements. Even though some of the results are not statistically significant, the study may not have had sufficient power to detect some important effects. Nevertheless, the results help us to formulate conjectures, which can then be investigated further