ANDURIL — A MATLAB toolbox for ANalysis and Decisions with UnceRtaInty: Learning from expert judgments

Leontaris, Georgios; Morales-Nápoles, Oswaldo

doi:10.1016/j.softx.2018.07.001

Cited by 20 publications

(15 citation statements)

References 4 publications

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“…The analysis was time consuming and it was difficult to provide a reproducible workflow for our analysis. The methods of aggregation and the scoring rules should be simple enough to re‐code in R and other freely available software (we note that recently they have been re‐coded in MATLAB; Leontaris and Morales‐Nápoles ). A revision of Excalibur could help to increase adoption of the method.…”

Section: Discussionmentioning

confidence: 99%

Weighting and aggregating expert ecological judgments

Hemming

Hanea

Walshe

et al. 2020

Ecological Applications

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Citation: Hemming, V., A. M. Hanea, T. Walshe, and M. A. Burgman. 2020. Weighting and aggregating expert ecological judgments. Ecological Applications 30(4):Abstract. Performance weighted aggregation of expert judgments, using calibration questions, has been advocated to improve pooled quantitative judgments for ecological questions. However, there is little discussion or practical advice in the ecological literature regarding the application, advantages or challenges of performance weighting. In this paper we (1) illustrate how the IDEA protocol with four-step question format can be extended to include performance weighted aggregation from the Classical Model, and (2) explore the extent to which this extension improves pooled judgments for a range of performance measures. Our case study demonstrates that performance weights can improve judgments derived from the IDEA protocol with four-step question format. However, there is no a-priori guarantee of improvement. We conclude that the merits of the method lie in demonstrating that the final aggregation of judgments provides the best representation of uncertainty (i.e., validation), whether that be via equally weighted or performance weighted aggregation. Whether the time and effort entailed in performance weights can be justified is a matter for decision-makers. Our case study outlines the rationale, challenges, and benefits of performance weighted aggregations. It will help to inform decisions about the deployment of performance weighting and avoid common pitfalls in its application.

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Section: Discussionmentioning

confidence: 99%

Weighting and aggregating expert ecological judgments

Hemming

Hanea

Walshe

et al. 2020

Ecological Applications

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“…We used ANDURIL to perform the SEJ analysis. ANDURIL is a MATLAB toolbox that implements CM, and includes an extensive list of functions that evaluate the performances of several DM's [24].…”

Section: Softwarementioning

confidence: 99%

Return Level Analysis of the Hanumante River Using Structured Expert Judgment: A Reconstruction of Historical Water Levels

Kindermann

Brouwer

Hamel

et al. 2020

Water

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Like other cities in the Kathmandu Valley, Bhaktapur faces rapid urbanisation and population growth. Rivers are negatively impacted by uncontrolled settlements in flood-prone areas, lowering permeability, decreasing channels widths, and waste blockage. All these issues, along with more extreme rain events during the monsoon due to climate change, have led to increased flooding in Bhaktapur, especially by the Hanumante River. For a better understanding of flood risk, the first step is a return level analysis. For this, historical data are essential. Unfortunately, historical records of water levels are non-existent for the Hanumante River. We measured water levels and discharge on a regular basis starting from the 2019 monsoon (i.e., June). To reconstruct the missing historical data needed for a return level analysis, this research introduces the Classical Model for Structured Expert Judgment (SEJ). By employing SEJ, we were able to reconstruct historical water level data. Expert assessments were validated using the limited data available. Based on the reconstructed data, it was possible to estimate the return periods of extreme water levels of the Hanumante River by fitting a Generalized Extreme Value (GEV) distribution. Using this distribution, we estimated that a water level of about 3.5 m has a return period of ten years. This research showed that, despite considerable uncertainty in the results, the SEJ method has potential for return level analyses.

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“…For a complete review of the classical model, the reader is referred to [34]. For a recent Matlab implementation of the method see [35].…”

Section: Expert Judgement For Uncertainty Quantificationmentioning

confidence: 99%

Structured expert judgement to understand the intrinsic vulnerability of traffic networks

Nogal

Morales-Nápoles

O’Connor

2019

Transportation Research Part A: Policy and Practice

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The concept of intrinsic vulnerability of a traffic network is defined for the first time in this paper. Intrinsic vulnerability is the susceptibility to incidents characterised by a probability of occurrence in space and time of difficult estimation, which can result in considerable reduction or loss of the system functionality.Given the nature of this type of vulnerability, its assessment might arise as a major problem. Therefore, this paper investigates the assessment of the intrinsic vulnerability of a traffic network through a set of quantifiable indicators, i.e., accessibility and reliability. Moreover, it is of interest to determine whether the selected indicators are sufficient to assess the intrinsic vulnerability or if there is any significant missing aspect to be considered. A new methodology based on structured elicitation of multivariate uncertainty from experts is presented to address these issues, allowing the estimation of the intrinsic vulnerability, and its probabilistic relationship with the indicators accessibility and reliability. Although applied to the case of the metric intrinsic vulnerability, the proposed methodology emerges as an effective tool to understand other traffic descriptors of difficult evaluation such as resilience.

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ANDURIL — A MATLAB toolbox for ANalysis and Decisions with UnceRtaInty: Learning from expert judgments

Cited by 20 publications

References 4 publications

Weighting and aggregating expert ecological judgments

Weighting and aggregating expert ecological judgments

Return Level Analysis of the Hanumante River Using Structured Expert Judgment: A Reconstruction of Historical Water Levels

Structured expert judgement to understand the intrinsic vulnerability of traffic networks

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