Scoring rules and competitive behavior in best value construction auctions

This paper examines the extent to which engineers can influence the competitive behavior of bidders in Best Value or multi-attribute construction auctions, where both the (dollar) bid and technical non-price criteria are scored according to a scoring rule. From a sample of Spanish construction auctions with a variety of bid scoring rules, it is found that bidders are influenced by the auction rules in significant and predictable ways. The bid score weighting, bid scoring formula and abnormally low bid criterion are variables likely to influence the competitiveness of bidders in terms of both their aggressive/conservative bidding and concentration/dispersion of bids. Revealing the influence of the bid scoring rules and their magnitude on bidders’ competitive behavior opens the door for the engineer to condition bidder competitive behavior in such a way as to provide the balance needed to achieve the owner’s desired strategic outcomes.

A new method for the characterisation and verification of local spatial predictability for convective scale ensembles

The use of kilometre-scale ensembles in operational forecasting provides new challenges for forecast interpretation and evaluation to account for uncertainty on the convective scale. A new neighbourhood based method is presented for evaluating and characterising the local predictability variations from convective scale ensembles. Spatial scales over which ensemble forecasts agree (agreement scales, S^A) are calculated at each grid point ij, providing a map of the spatial agreement between forecasts. By comparing the average agreement scale obtained from ensemble member pairs (S^A(mm)_ij), with that between members and radar observations (S^A(mo)_ij), this approach allows the location-dependent spatial spread-skill relationship of the ensemble to be assessed. The properties of the agreement scales are demonstrated using an idealised experiment. To demonstrate the methods in an operational context the S^A(mm)_ij and S^A(mo)_ij are calculated for six convective cases run with the Met Office UK Ensemble Prediction System. The S^A(mm)_ij highlight predictability differences between cases, which can be linked to physical processes. Maps of S^A(mm)_ij are found to summarise the spatial predictability in a compact and physically meaningful manner that is useful for forecasting and for model interpretation. Comparison of S^A(mm)_ij and S^A(mo)_ij demonstrates the case-by-case and temporal variability of the spatial spread-skill, which can again be linked to physical processes.