Decision support for multi-attribute auctions

Karakaya, Gülşah
In this study, we address multi-attribute, multi-item auction problems. In multi-attribute auctions there are additional attributes to the price and the comparison of bids is not straightforward. In multi-item auctions which are also known as combinatorial auctions, it is not so trivial to determine the winning bidders. We develop an auction decision support system (ADSS) that supports sellers to bid on multiple items. We demonstrate our approach in a multi-attribute, multi-item reverse auction setting. The approach is also directly applicable to forward auctions. During the auction process, ADSS estimates the underlying preference function of the buyer and supports sellers providing them information based on these estimations. We first assume that the sellers do not share their cost functions with ADSS and develop interactive algorithms for underlying linear preference functions as well as for underlying quasiconvex preference functions. The aim of the developed approaches is to have the more competitive bidders eventually end up winning the auction, with predetermined reasonable mark-up values. We demonstrate that our algorithms work well on a variety of test problems. We also develop an interactive algorithm for the case that sellers explicitly make their cost functions available to ADSS. In this approach, ADSS tries to find the best possible combinations considering both the estimated preference function of the buyer and the cost functions of the sellers. The experiments show that our algorithm finds the optimal winners (achieved with exact parameters of the underlying preference function).


Decision Support for Multi attribute Multi item Reverse Auctions
Karakaya, Gülşah (null; 2012-08-19)
In this study, we address multi-item auction problems in a multiattribute, multi-round reverse auction setting. In each round, we provide the buyer with a set of efficient bid combinations, who then chooses the provisional winners whose bids collectively provide all the required items. We estimate preference information from the buyer’s choices and provide this to the bidders. The bidders update/improve their bids in order to become/stay competitive. The process continues several rounds. The developed inter...
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This paper proposes a new methodology for subspace-based state-space identification for linear time-periodic (LTP) systems. Since LTP systems can be lifted to equivalent linear time-invariant (LTI) systems, we first lift input-output data from an unknown LTP system as if they were collected from an equivalent LTI system. Then, we use frequency-domain subspace identification methods to find the LTI system estimate. Subsequently. we propose a novel method to obtain a time-periodic realization for the estimate...
Citation Formats
G. Karakaya, “Decision support for multi-attribute auctions,” Ph.D. - Doctoral Program, Middle East Technical University, 2013.