Decision Support for Project Management by using Diagnostic Inference and Explaining-Away in Bayesian Networks

Date

2020-05-29

Author

Yet, Barbaros

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Risk and uncertainty are natural elements of projects. A project manager has to manage these elements effectively to improve the chance of project success. Yet, well-known biases and heuristics about decision-making under uncertainty limit a project manager's ability to deal with these elements, especially for complex projects, and decision support tools can be helpful for this task. This paper focuses on building a decision support model for reasoning with uncertainty in project control. We propose a Bayesian Network (BN) model that uses the notions of diagnostic reasoning and 'explaining-away' to infer the effort requirements, progress and risks of project tasks. The proposed model aids the project manager in reasoning with uncertainty and risk factors when monitoring project progress. We use a project example to demonstrate the model structure, its use, benefits and limitation compared to conventional project control tools.

URI

https://icdsst2020.files.wordpress.com/2020/05/icdsst-2020-proceedings.pdf
https://hdl.handle.net/11511/87912

Conference Name

6th International conference on decision support system technology (ICDSST 2020), (27 - 29 Mayıs 2020)

Collections

Graduate School of Informatics, Conference / Seminar

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Citation Formats

B. Yet, “Decision Support for Project Management by using Diagnostic Inference and Explaining-Away in Bayesian Networks,” Zaragoza, İspanya, 2020, p. 131, Accessed: 00, 2021. [Online]. Available: https://icdsst2020.files.wordpress.com/2020/05/icdsst-2020-proceedings.pdf.