Where can Knowledge-Based Decision Support Systems Go in Contemporary Business Management — A New Architecture for the Future

Liu, Shaofeng; Smith, Melanie Hudson; Tuck, Sarah; Pan, Jianguo; Alkuraiji, Ali; Jayawickrama, Uchitha

doi:10.7763/joebm.2015.v3.235

Cited by 6 publications

(2 citation statements)

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“…The layering diagram shown in figure 2 presents an architecture that describes the constraints in notional system functionality as layers. This diagram abstracts the underlying complexity shown in prior research (Nagaraj and Pasupathy 2017) that studied cross‐layer optimization and extended those notions to a system‐level model representing the architecture that exists in intelligent decision support systems (Liu et al 2015; Kaiwartya et al 2016; Beans 2018). Given a layered architecture such as shown in figure 2, which incorporates computational context in stochastic variables, tractable mathematical models can be developed that optimize the performance of assets and services with respect to decision objectives (Jalaeian, Zhu, Samani, and Motani 2016; Jalaian et al 2017).…”

Section: Optimization Under Uncertaintymentioning

confidence: 99%

Uncertain Context: Uncertainty Quantification in Machine Learning

2019

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Machine learning and artificial intelligence will be deeply embedded in the intelligent systems humans use to automate tasking, optimize planning, and support decision-making. However, many of these methods can be challenged by dynamic computational contexts, resulting in uncertainty in prediction errors and overall system outputs. Therefore, it will be increasingly important for uncertainties in underlying learning-related computer models to be quantified and communicated. The goal of this article is to provide an accessible overview of computational context and its relationship to uncertainty quantification for machine learning, as well as to provide general suggestions on how to implement uncertainty quantification when doing statistical learning. Specifically, we will discuss the challenge of quantifying uncertainty in predictions using popular machine learning models. We present several sources of uncertainty and their implications on statistical models and subsequent machine learning predictions.

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