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Knowledge Gradient: Capturing Value of Information in Iterative Decisions under Uncertainty

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  • Donghun Lee

    (Department of Mathematics, Korea University, Seoul 02481, Republic of Korea)

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 example implementation of its most frequently used implementation; secondly, on how KG algorithms are related to other problems and iterative algorithms, in particular, Bayesian optimization; thirdly, on the significant trends found in modern theoretical research on KG; lastly, on the diverse examples of applications that use KG in their key decision-making step.

Suggested Citation

  • Donghun Lee, 2022. "Knowledge Gradient: Capturing Value of Information in Iterative Decisions under Uncertainty," Mathematics, MDPI, vol. 10(23), pages 1-20, November.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:23:p:4527-:d:989136
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