2022
DOI: 10.3390/math10234527
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Knowledge Gradient: Capturing Value of Information in Iterative Decisions under Uncertainty

Abstract: Many real-life problems that involve decisions under uncertainty are often sequentially repeated and can be approached iteratively. Knowledge Gradient (KG) formulates the decision-under-uncertainty problem into repeatedly estimating the value of information observed from each possible decisions and then committing to a decision with the highest estimated value. This paper aims to provide a multi-faceted overview of modern research on KG: firstly, on how the KG algorithm is formulated in the beginning with an e… Show more

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