A Bayesian network framework for project cost, benefit and risk analysis with an agricultural development case study

Date

2016-10-01

Author

Yet, Barbaros
Fenton, Norman
Neil, Martin
Luedeling, Eike
Shepherd, Keith

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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Successful implementation of major projects requires careful management of uncertainty and risk. Yet such uncertainty is rarely effectively calculated when analysing project costs and benefits. This paper presents a Bayesian Network (BN) modelling framework to calculate the costs, benefits, and return on investment of a project over a specified time period, allowing for changing circumstances and trade-offs. The framework uses hybrid and dynamic BNs containing both discrete and continuous variables over multiple time stages. The BN framework calculates costs and benefits based on multiple causal factors including the effects of individual risk factors, budget deficits, and time value discounting, taking account of the parameter uncertainty of all continuous variables. The framework can serve as the basis for various project management assessments and is illustrated using a case study of an agricultural development project.

Subject Keywords

General Engineering, Artificial Intelligence, Computer Science Applications

URI

https://hdl.handle.net/11511/56441

Journal

EXPERT SYSTEMS WITH APPLICATIONS

DOI

https://doi.org/10.1016/j.eswa.2016.05.005

Collections

Graduate School of Informatics, Article

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Citation Formats

B. Yet, N. Fenton, M. Neil, E. Luedeling, and K. Shepherd, “A Bayesian network framework for project cost, benefit and risk analysis with an agricultural development case study,” EXPERT SYSTEMS WITH APPLICATIONS, pp. 141–155, 2016, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/56441.