A behavioural study using decision analysis of building cost prediction by chartered quantity surveyors

Previous behavioural research has shown that human beings are often poor in making good probability assessments and that the quality of these judgments is affected by human bias. In quantity surveying, considerable research has been carried out into the accuracy of building cost predictions but little is known about how the quality of these is influenced by the behavioural characteristics of the Chartered Quantity Surveyor when making them. This thesis describes a research investigation using a group of Chartered Quantity Surveyors into their predictive abilities and behavioural biases in both general event and building cost event situations. The key findings were: 1 - In relation to general events, Chartered Quantity Surveyors behaved much like other groups with a large number of the responses (surprise answers) capturing the true value in the extreme fractiles of their assessments. 2 - In relation to building events the results showed little improvement with again a large number of surprise answers, although with those excluded there was a good level of prediction. 3 - There appears to be a reasonable correlation within individuals between predictive ability in general and ability in building costs. 4 - Chartered Quantity Surveyors are generally in agreement that confidence in prediction of building cost events increases with monetary value and that this correlation is even stronger when the complexity of the item is also considered. 5 - Differences between traditional single point estimating and probability range estimating suggested that the latter would have an expected monetary value approximately 2% higher. 6 - The investigation into biases found evidence for a number, particularly overconfidence in specific situations, lack of appreciation of randomness, inability to handle two variables together, and lack of compliance with probability rules. Differing consequences of the application of anchoring and adjustment bias and examples of ignoring information because of the availability bias also occurred.