M Iftekhar

Combinatorial Auction Designs for Biodiversity Conservation

A conservation auction is a tender mechanism for allocating project contracts among landholders for undertaking environmental conservation activities. Typically, environmental indices (EIs) are used to combine multiple project benefits into an overall score for selecting successful projects. However, higher flexibility and efficiency can be achieved by allowing bidders to offer different combinations of conservation benefits where projects are evaluated directly in terms of their multiple outcomes. Combinatorial auctions that allow bidders to express their preferences for a combination of projects can be attractive. In this article, we discuss three major features of combinatorial auction designs that would be useful in conservation: submission of multiple projects by individual bidders, consideration of cost complementarities by individual bidders, and flexibility in project evaluation by the auctioneer. Using a stylized simulation experiment, we quantify and demonstrate the benefits of these features for some hypothetical conservation situations.

Choice of Item Pricing Feedback Schemes for Multiple Unit Reverse Combinatorial Auctions

Recently, interest in combinatorial auctions has extended to include trade in multiple units of heterogeneous items. Combinatorial bidding is complex and iterative auctions are used to allow bidders to sequentially express their preferences with the aid of auction market information provided in the form of price feedbacks. There are different competing designs for the provision of item price feedbacks; however, most of these have not been thoroughly studied for multiple unit combinatorial auctions. This paper focuses on addressing this gap by evaluating several feedback schemes or algorithms in the context of multiple unit auctions. We numerically evaluate these algorithms under different scenarios that vary in bidder package selection strategies and in the degree of competition. We observe that auction outcomes are best when bidders use a naïve bidding strategy and competition is strong. Performance deteriorates significantly when bidders strategically select packages to maximize their profit. Finally, the performances of some algorithms are more sensitive to strategic bidding than others.