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Diagnosing decision quality
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AI
DOI：10.1016/j.dss.2007.12.012.png)
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En 中文
Human decision making is error-prone and often subject to biases. Important information cues are misweighted and feedback delays hamper learning. Experimentally, task information has been shown to be valuable in improving decision making. However, such information is rarely available. Generalizing from lab-based approaches, we present a new field methodology for the diagnosis of decision quality (DDQ) that helps decision makers discover such information. We illustrate our approach in the context of hotel revenue management and demonstrate how it can identify context-specific systematic errors in decision making in a manner that facilitates adaptive changes and improved performance. (C) 2007 Elsevier B.V. All rights reserved.
Keyword:
decision quality
decision support
human judgment
cognitive feedback
revenue management
期刊
IF:
6.8
论文数:
3.8K
被引数:
1.5W
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