Bidding

About: Bidding is a research topic. Over the lifetime, 15371 publications have been published within this topic receiving 294233 citations. The topic is also known as: competitive bidding.

Distance-based clustering of sparsely observed stochastic processes, with applications to online auctions

Abstract: We propose a distance between two realizations of a random process where for each realization only sparse and irregularly spaced measurements with additional measurement errors are available. Such data occur commonly in longitudinal studies and online trading data. A distance measure then makes it possible to apply distance-based analysis such as classification, clustering and multidimensional scaling for irregularly sampled longitudinal data. Once a suitable distance measure for sparsely sampled longitudinal trajectories has been found, we apply distance-based clustering methods to eBay online auction data. We identify six distinct clusters of bidding patterns. Each of these bidding patterns is found to be associated with a specific chance to obtain the auctioned item at a reasonable price.

Identification and Estimation of Cost Functions Using Observed Bid Data: An Application to Electricity Markets

Abstract: This paper presents several techniques for recovering cost function estimates for electricity generation from a model of optimal bidding behavior in a competitive electricity market. Two techniques are developed based on different models of the price-setting process in a competitive electricity market. The first assumes that the firm is able to choose the price that maximizes its realized profits given the bids of its competitors and the realization of market demand. This procedure is straightforward to apply, but does not impose all of the market rules on the assumed price-setting process. The second procedure uses the assumption that the firm bids to maximize its expected profits. This procedure is considerably more complex, but can yield more insights about the nature of the firm's variable costs, because it allows the researcher to recover generation unit-level variable cost functions. These techniques are applied to bid, market outcomes and financial hedge contract data obtained from the first three months of operation of the National Electricity Market (NEM1) in Australia. The empirical analysis illustrates the usefulness of these techniques in measuring actual market power and the ability to exercise market power possessed by generation unit owners in competitive electricity markets.

To preempt or not: Tackling bid and time-based cheating in online spectrum auctions

Abstract: Online spectrum auctions offer ample flexibility for bidders to request and obtain spectrum on-the-fly. Such flexibility, however, opens up new vulnerabilities to bidder manipulation. Aside from rigging their bids, selfish bidders can falsely report their arrival time to game the system and obtain unfair advantage over others. Such time-based cheating is easy to perform yet produces severe damage to auction performance. We propose Topaz, a truthful online spectrum auction design that distributes spectrum efficiently while discouraging bidders from misreporting their bids or time report. Topaz makes three key contributions. First, Topaz applies a 3D bin packing mechanism to distribute spectrum across time, space and frequency, exploiting spatial and time reuse to improve allocation efficiency. Second, Topaz enforces truthfulness using a novel temporal-smoothed critical value based pricing. Capturing the temporal and spatial dependency among bidders who arrive subsequently, this pricing effectively diminishes gain from bid and/or time-cheating. Finally, Topaz offers a “scalable” winner preemption to address the uncertainty of future arrivals at each decision time, which significantly boosts auction revenue. We analytically prove Topaz's truthfulness, which does not require any knowledge of bidder behavior, or an optimal spectrum allocation to enforce truthfulness. Using empirical arrival and bidding models, we perform simulations to demonstrate the efficiency of Topaz. We show that proper winner preemption improves auction revenue by 45–65% at a minimum cost of spectrum utilization.