David Cattell

The risks of unbalanced bidding

Unbalanced bidding models have largely ignored the risk aspect of item pricing. Many researchers have acknowledged that there are considerable risks associated with unbalancing a bid but little has been done to describe these risks, let alone model them. A new framework is proposed by which all of these risks can be assessed. It identifies that these risks comprise the risk of rejection, the risk of reaction, and the risk of being wrong. It is further proposed that the value‐at‐risk (‘VaR’) method of measuring risk is a convenient way by which to combine all of these risks into one composite assessment. This quantified assessment serves to describe the extent of risk generated by each level of each item’s price. Previous related research has proposed an unbalanced bidding model that has likewise provided a measurement of the expected reward generated by each level of each item’s price. By doing a summation of these, keeping in mind that the prices applied to all of a project’s component items must add up to the overall bid price, the contractor is able to assess both the risks as well as the rewards of all possible item price combinations.

On being balanced in an unbalanced world

This paper examines the case of a procurement auction for a single project, in which the breakdown of the winning bid into its component items determines the value of payments subsequently made to bidder as the work progresses. Unbalanced bidding, or bid skewing, involves the uneven distribution of mark-up among the component items in such a way as to attempt to derive increased benefit to the unbalancer but without involving any change in the total bid. One form of unbalanced bidding for example, termed Front Loading (FL), is thought to be widespread in practice. This involves overpricing the work items that occur early in the project and underpricing the work items that occur later in the project in order to enhance the bidders cash flow. Naturally, auctioners attempt to protect themselves from the effects of unbalancing—typically reserving the right to reject a bid that has been detected as unbalanced. As a result, models have been developed to both unbalance bids and detect unbalanced bids but virtually nothing is known of their use, success or otherwise. This is of particular concern for the detection methods as, without testing, there is no way of knowing the extent to which unbalanced bids are remaining undetected or balanced bids are being falsely detected as unbalanced. This paper reports on a simulation study aimed at demonstrating the likely effects of unbalanced bid detection models in a deterministic environment involving FL unbalancing in a Texas DOT detection setting, in which bids are deemed to be unbalanced if an item exceeds a maximum (or fails to reach a minimum) ‘cut-off’ value determined by the Texas method. A proportion of bids are automatically and maximally unbalanced over a long series of simulated contract projects and the profits and detection rates of both the balancers and unbalancers are compared. The results show that, as expected, the balanced bids are often incorrectly detected as unbalanced, with the rate of (mis)detection increasing with the proportion of FL bidders in the auction. It is also shown that, while the profit for balanced bidders remains the same irrespective of the number of FL bidders involved, the FL bidders profit increases with the greater proportion of FL bidders present in the auction. Sensitivity tests show the results to be generally robust, with (mis)detection rates increasing further when there are fewer bidders in the auction and when more data are averaged to determine the baseline value, but being smaller or larger with increased cut-off values and increased cost and estimate variability depending on the number of FL bidders involved. The FL bidders expected benefit from unbalancing, on the other hand, increases, when there are fewer bidders in the auction. It also increases when the cut-off rate and discount rate is increased, when there is less variability in the costs and their estimates, and when less data are used in setting the baseline values.

Proposed framework for applying cumulative prospect theory to an unbalanced bidding model

Recent research on unbalanced bidding models has identified both the benefits and the risks generated from different prices applied to the component items of a construction project. It has also been proposed that modern portfolio theory (MPT) be used as the technique by which to trade-off the conflicting objectives of maximizing the expected profit and, at the same time, minimizing the risk. The MPT methodology has previously been found to provide contractors with a technique by which they can identify and sift out all of the efficient item-price combinations, such that they need not suffer the consequences of deciding upon any substandard inefficient pricing combination. The use of MPT still leaves contractors with a wide range of choices. This paper introduces and applies microeconomic techniques [namely cumulative prospect theory (CPT)] to narrow these choices down to only one optimal choice. CPT serves to equate different return-risk alternatives to find the one set of item prices that will provide th...

An overview of component unit pricing theory

Component unit pricing (CUP) theory presents a fresh approach to item pricing, described as the process of distributing the overall price among its constituent component items. This theory provides explanation and proof that different distributions of mark-up among the items of a project produce different levels of reward for contractors, while exposing them to different degrees of risk. The theory describes the three identified sources of these rewards, namely those of improved cash flow, escalation in compensation and valuations of likely variations. In addition, it also provides the first explanation of the three risks involved, namely the risk of ‘rejection’, the risk of ‘reaction’ and the risk of ‘being wrong’. In combination, it provides a means by which both the rewards as well as these risks can now be measured given any pricing scenario. This theory gives effect to fuzzy constraints on the price of each item, providing a scientific basis by which to identify more extreme prices when pursuing more profit and more restrained prices when seeking to reduce risk. Overall, it provides a basis by which to moderate these two objectives in the pursuit of the maximization of a contractor’s utility. A test on a hypothetical project indicates an improvement of more than 150% on utility, if a contractor applies this theory, compared to the position when balanced prices are used instead.