Analysis of the distribution of the number of bidders in construction contract auctions

The number of bidders, N, involved in a construction procurement auction is known to have an important effect on the value of the lowest bid and the mark-up applied by bidders. In practice, for example, it is important for a bidder to have a good estimate of N when bidding for a current contract. One approach, instigated by Friedman in 1956, is to make such an estimate by statistical analysis and modelling. Since then, however, finding a suitable model for N has been an enduring problem for researchers and, despite intensive research activity in the subsequent 30 years, little progress has been made, due principally to the absence of new ideas and perspectives. The debate is resumed by checking old assumptions, providing new evidence relating to concomitant variables and proposing a new model. In doing this and in order to ensure universality, a novel approach is developed and tested by using a unique set of 12 construction tender databases from four continents. This shows the new model provides a significant advancement on previous versions. Several new research questions are also posed and other approaches identified for future study

The Caravan Rolls On

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/68409/2/10.1177_107554707900100103.pd

Nutrition planning in developing nations : a bicriterion mathematical programming approach / 287

Includes bibliographical references (p. 37-44)

Properties of sports ranking methods

Ideally, the ranking of sports teams should incorporate information (comprehensiveness) obtained from the outcome of a match, such as the strength of the opponent and schedule. In addition, the ranking method should be fair and not reward teams for poor performance or factors beyond their control, such as the sequence of the matches. We state properties such that if followed, the ranking methods will be fair and comprehensive. We evaluate five popular sports ranking methods and whether or not they adhere to these properties. Further, we identify a ranking method that under reasonably sufficient conditions will satisfy all of the properties

Crowd-ranking: a Markov-based method for ranking alternatives

Many ranking algorithms rank a set of alternatives based on their performance in a set of pairwise comparisons. In this study, a special scenario is observed in which the objective is to rate and rank a set of groups in a traditional recruiting situation, in which the groups extend offers to the set of individuals, and the individuals will select one of their available offers. The new ranking method, Crowd-Ranking, uses collective wisdom and decision-making in conjunction with Markov chains to create competitive matches between alternatives and ultimately provide a ranking of the alternatives. First, the method is evaluated by its performance in a perfect season scenario. Next, it is applied to the case of NCAA football recruiting in the power conferences (ACC, Big Ten, Big 12, Pac 12 and SEC) in the Football Bowl Subdivision. For the Big Ten conference, the method performs significantly better than popular existing services at predicting future team performance based on past recruiting rankings. For a comprehensive national ranking of the power conferences, there is no statistically significant difference between Crowd-Ranking and the other methods