Project Cost Benefit and Risk Analysis using Bayesian Networks

Yet, Barbaros
Fenton, Norman
Neil, Martin
Luedeling, Eike
Shepherd, Keith
Uncertainty and risks are common elements of all major projects. 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 calculates costs and benefits based on multiple causal factors including the effects of individual risk factors, budget deficits, and time value discounting. The method is illustrated using a case study of an agricultural development project.
31st Conference on Uncertainty in Artificial Intelligence (UAI 2015) - Bayesian Applications Workshop (12 - 16 Temmuz 2015)


Decision Support for Project Management by using Diagnostic Inference and Explaining-Away in Bayesian Networks
Yet, Barbaros (null; 2020-05-29)
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 Bayes...
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Risk assessment is carried out to understand uncertainties and estimate how they may affect a project. Although there are various studies on alternative methods to quantify risk in projects, limited research exists about how the results are utilized to make sense of project risk by the decision-makers. Probabilistic methods such as Monte Carlo Simulation involve an additional complication as probability is an abstract concept and hard to interpret. We argue that blurred risk pictures as well as lack of fami...
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Purpose Megaprojects are known as complex projects that involve high levels of uncertainty. This interpretive study explores and portrays perceived complexity in mega construction projects by lived experiences of project managers. Design/methodology/approach This study utilises a ground theory approach to analyse data gathered from semi-structured interviews with 18 professionals involved in 11 megaprojects. Findings Complexity in mega construction projects is defined as a project property that stems from t...
Application of a risk visualization framework using semantic risk data to empower risk communication
Ertaymaz, Muzaffer Uğurcan; Atasoy Özcan, Güzide; Department of Civil Engineering (2020)
Risk communication is one of the major factors that effects the success of a project. Conventional risk management focuses on risk checklists and matrices, and considerable amount of risk information is discarded and not communicated. Without communicating the necessary risk information, risk management strategies cannot be applied effectively. The purpose of this study is to develop a systematic and practical risk visualization framework that shall improve the risk communication strategies. The proposed fr...
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Erol, Huseyin; Dikmen, Irem; Atasoy Özcan, Güzide; Birgönül, Mustafa Talat (American Society of Civil Engineers (ASCE), 2020-12-01)
© 2020 American Society of Civil Engineers.Although complexity and risk are inherent characteristics of megaconstruction projects, existing project management approaches fail to incorporate complexity-based thinking into risk management. Complexity is usually considered as the source of risk events, along with uncertainty. However, prevailing risk management practices are oriented toward only handling the uncertainty. The lack of integration between the complexity and other risk-related concepts leads to un...
Citation Formats
B. Yet, N. Fenton, M. Neil, E. Luedeling, and K. Shepherd, “Project Cost Benefit and Risk Analysis using Bayesian Networks,” presented at the 31st Conference on Uncertainty in Artificial Intelligence (UAI 2015) - Bayesian Applications Workshop (12 - 16 Temmuz 2015), Amsterdam, Netherlands, 2015, Accessed: 00, 2021. [Online]. Available: