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

• We focus on project cost, benefit and risk analysis.
• We propose a modelling framework that uses a hybrid and dynamic Bayesian network(BN).
• BN offers unique features of analysıng risk scenarios and budget policies.
• It uses uncertainty and variability of risk and economic factors in its predictions.
• The framework is illustrated by a case study of agricultural development projects.

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.