Using Bayesian Networks to assess the risk appetite of construction contractors

The pricing of items of construction work using Component Unit Pricing (CUP) Theory requires that contractors have to assess and quantify their risk profiles. Those contractors with a willingness to take on greater risks can then be rewarded with a prospect of greater profits. CUP Theory provides a basis by which this can be accomplished by way of the manner and extent to which contractors spread their overall bid prices amongst all of the constituent component item prices. Conversely, this theory also facilitates that contractors who want to moderate their exposure to risk are able to do so, independently of any adjustment they might choose to make to their overall mark-ups. Contractors are, however, typically unaware of their risk profiles and will not have had these assessed. There are no universally accepted or popular methods established for the assessment of the risk profiles of firms operating within the construction industry. Bayesian networking (BN) is gaining popularity in the financial management arena as a sophisticated statistical approach for the assessment and management of risks. It is envisaged that it might serve well for evaluating and explaining contractors' risk profiles as well as facilitate a process by which these can be reviewed and modified in line with inevitable changes over time. Against this contextual backdrop this paper provides an overview as to how BNs can be used to improve the risk profiles of contractors.