“Now or Later?” When to Deploy Qualification Screening in Open-Bid Auction for Re-Sourcing

Designing Open-bid Procurement Auction with Supplier Qualification Screenings Manufacturers often use re-sourcing initiatives to keep their suppliers’ pricing competitive; e.g., new entrant suppliers are identified and invited to compete with the incumbent supplier for supply contracts in an open-bid auction. To ensure that entrant suppliers have the capability of executing the contract, the conventional approach for many manufacturers is to conduct qualification screenings on all the entrants prior to auction bidding and only allow qualified entrant suppliers to compete in the auction. In “‘Now or Later?’ When to Deploy Qualification Screening in Open-Bid Auction for Re-Sourcing”, Zhang, Chen, and Katok explore an alternative arrangement of this process where all entrant suppliers are invited for bidding first before qualification screenings are selectively conducted afterward to determine the contract winner. This new approach helps reduce the waste of manufacturers’ screening efforts on suppliers with uncompetitive bids but in the meantime introduces incentives for less competitive supplier bidding behavior. They provide analytical and numerical evidence that this new approach could be very effective in managing manufacturers’ procurement costs.

Research on Bidding Game and Its Application Based on Copetition Scenario

Bidding decision is not only a science, an art, but also a game. The more intense the competition, the more important the game. In practice, there is the possibility of collaboration between bidders and even hidden competing behaviors such as bidding rigging. In this study, the optimized low-price bid winning method was discussed, and the characteristics and application of the bidding game under the copetition scenarios were studied. The results show the following: (1) Under the copetition scenario, the rational bidding behavior of bidders will deviate according to the different information advantages, and there is a game of making bidding strategy decisions according to the competitive scenario. (2) There is a close functional relationship between the winning bid result and the evaluation elimination factor, the number of bidders, and the number of bidders who operate bidding rigging. (3) Based on the quotation strategy matrix modeling, it enables the quantitative decision making bid amount, offer score, and deviation risk. This study enriches the theory of quota decision in copetition scenarios and is enlightening for similar business behavior game decisions.

Price Discovery and Learning during the German 5G Auction

The auctioning of frequency has to comply with a multitude of requirements in order to guarantee a transparent and efficient process. The German Federal Network Agency (Bundesnetzagentur) has opted for a design that provides participants with information on the highest bid after each round for every band along with information on the bidder. We evaluate the price formation efficiency in this setup to see how fast prices become informative about the final auction value. We find that prices are partially informative right from the beginning which allows us to conclude that participants were able to learn fast from their competitors’ bidding behavior and validates the choice of the agency to implement the auction in the present format.

A Wind Energy Supplier Bidding Strategy Using Combined EGA-Inspired HPSOIFA Optimizer and Deep Learning Predictor

As the integration of large-scale wind energy is increasing into the electricity grids, the role of wind energy suppliers should be investigated as a price-maker as their participation would influence the locational marginal price (LMP) of electricity. The existing bidding strategies for a wind energy supplier faces limitations with respect to the potential cooperation, other competitors’ bidding behavior, network loss, and uncertainty of wind production (WP) and balancing market price (BMP). Hence, to solve these problems, a novel bidding strategy (BS) for a wind power supplier as a price-maker has been proposed in this paper. The new algorithm, called the evolutionary game approach (EGA) inspired hybrid particle swarm optimization and improved firefly algorithm (HPSOIFA), has been proposed to handle the bidding issue. The bidding behavior of power suppliers, including conventional power suppliers, has been encoded as one species to obtain the equilibrium where the EGA can explore dynamically reasonable behavior changes of the opponents. Each species of behavior change has been exploited by the HPSOIFA to improve the optimization solutions. Moreover, a deep learning algorithm, namely deep belief network, has been implemented for improving the accuracy of the forecasting results considering the WP and BMP, and the uncertainty revealed in the WP and BMP has been modeled by quantile regression (QR). Finally, the Shapley value (SV) has been calculated to estimate the benefits of cooperative power suppliers. The presented case studies have verified that the proposed algorithm and the established bidding strategy exhibit higher effectiveness.

Bidding behavior in auctions versus posted prices: comparisons of mean and marginal effects

Economic experiments have been widely used to elicit individuals' evaluation for various commodities. Common elicitation methods include auction and posted price mechanisms. A field experiment is designed to compare willingness-to-pay (WTP) estimates between these two mechanisms. Despite both of these formats being theoretically incentive compatible and demand revealing, results from 115 adult consumers indicate that WTP estimates obtained from an auction are 32–39 percent smaller than those from a posted price mechanism. A comparison in statistical significance shows that auctions require a smaller sample size than posted price mechanisms in order to detect the same preference change. Nevertheless, the signs of marginal effects for different product characteristics are consistent in both mechanisms.

A Detector and Evaluation Framework of Abnormal Bidding Behavior Based on Supplier Portrait

In a large number of bidding supplier groups, it is difficult to accurately find suppliers with unreasonable bidding behavior. In order to solve the problem of precise positioning of massive abnormal bidding behavior groups of diverse and widely distributed suppliers, the authors design a detector framework of abnormal bidding behavior based on supplier portrait. This paper mainly focuses on three abnormal bidding behaviors which harmful to the tenderers—“affiliated operation,” “subcontracting behavior,” and “colluding behavior.” Based on the bidding behavior records of suppliers, this paper establishes supplier portraits in four dimensions. In order to solve the problem that the detection algorithm under the unlabeled data is difficult to verify, this research establishes a new evaluation framework based on the bid base price formula and benefit map database of the supplier. The experiment verifies that the framework can effectively detect most suppliers with abnormal bidding behavior and can significantly change the benchmark price after eliminating abnormal suppliers.