A Systematic Development of Uncertainty Modeling in the Assessment of Health Risk to Environmental Contaminants

Mishra, Harshit; Karmakar, Subhankar; Kumar, Rakesh

doi:10.1007/978-981-10-7332-8_9

Cited by 4 publications

(1 citation statement)

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“…The enduring influence of the W&H framework illustrates that many scholars addressing challenges associated with computational modeling or model-based decision support have gravitated toward the tripartite conceptualization of uncertainty, which divides uncertainty along three dimensions: location, nature, and level. Elements of this framework are also found in several derivative frameworks that aim to build off or replace the W&H framework (e.g., Dewulf et al, 2005;Mishra et al, 2018;Refsgaard et al, 2007;Skinner et al, 2014).…”

Section: The Wandh Frameworkmentioning

confidence: 99%

Confidence deficits and reducibility: Toward a coherent conceptualization of uncertainty level

Janzwood

2022

Risk Analysis

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Outside of the field of risk analysis, an important theoretical conversation on the slippery concept of uncertainty has unfolded over the last 40 years within the adjacent field of environmental risk. This literature has become increasingly standardized behind the tripartite distinction between uncertainty location, the nature of uncertainty, and uncertainty level, popularized by the “W&H framework.” This article introduces risk theorists and practitioners to the conceptual literature on uncertainty with the goal of catalyzing further development and clarification of the uncertainty concept within the field of risk analysis. It presents two critiques of the W&H framework's dimension of uncertainty level—the dimension that attempts to define the characteristics separating greater uncertainties from lesser uncertainties. First, I argue the framework's conceptualization of uncertainty level lacks a clear and consistent epistemological position and fails to acknowledge or reconcile the tensions between Bayesian and frequentist perspectives present within the framework. This article reinterprets the dimension of uncertainty level from a Bayesian perspective, which understands uncertainty as a mental phenomenon arising from “confidence deficits” as opposed to the ill‐defined notion of “knowledge deficits” present in the framework. And second, I elaborate the undertheorized concept of uncertainty “reducibility.” These critiques inform a clarified conceptualization of uncertainty level that can be integrated with risk analysis concepts and usefully applied by modelers and decisionmakers engaged in model‐based decision support.

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Section: The Wandh Frameworkmentioning

confidence: 99%