8704912
Replicating Online Yankee Auctions to Analyze Auctioneers' and Bidders' Strategies
We present a simulation approach that provides a relatively risk-free and cost-effective environment to examine the decision space for both bid takers and bid makers in web-based dynamic price setting processes. The applicability of the simulation platform is demonstrated for Yankee auctions in particular. We focus on the optimization of bid takers' revenue, as well as on examining the welfare implications of a range of consumer-bidding strategies--some observed, some hypothetical. While these progressive open discriminatory multiunit auctions with discrete bid increments are made feasible by Internet technologies, little is known about their structural characteristics, or their allocative efficiency. The multiunit and discrete nature of these mechanisms renders the traditional analytic framework of gametheory intractable (Nautz and Wolfstetter 1997). The simulation is based on theoretical revenue generating properties of these auctions. We use empirical data from real online auctions to instantiate the simulation's parameters. For example, the bidding strategies of the bidders are specified based on three broad bidding strategies observed in real online auctions. The validity of the simulation model is established and subsequently the simulation model is configured to change the values of key control factors, such as the bid increment. Our analysis indicates that the auctioneers are, most of the time, far away from the optimal choice of bid increment, resulting in substantial losses in a market with already tight margins. The simulation tool provides a test bed forjointly exploring the combinatorial space of design choices made by the auctioneer's and the bidding strategies adopted by the bidders. For instance, a multinomial logit model reveals that endogenous factors, such as the bid increment and the absolute magnitude of the auction have a statistically significant impact on consumer-bidding strategies. This endogeniety is subsequently modeled into the simulation to investigate whether the effects are significant enough to alter the optimal bid increments or auctioneer revenues. Additionally, we investigate hybrid-bidding strategies, derived as a combination of three broad strategies, such as jump bidding and strategic-at-margin (SAM) bidding. We find that hybrid strategies have the potential of significantly altering bidders' likelihood of winning, as well as their surplus.
17233820
IT-enabled transparent electronic markets: the case of the air travel industry
The electronic market hypothesis predicts that by reducing coordination costs, information technology (IT) will shift industrial organization from hierarchical to market-based forms of economic activity. While academic researchers and practitioners have witnessed these shifts with the advent of the Internet, there is little understanding about the process and the underlying forces that drive them. In this paper, we provide an in-depth analysis of the air travel industry, which has recently experienced significant IT-driven transformations. We conclude that, together with IT, pro-competitive laws and the information-intensive nature of air travel products have triggered competition for consumers with transparent market mechanisms, which is leading to the emergence of more transparent electronic markets in the air travel industry.
7914809
Predicting Bidders' Willingness to Pay in Online Multiunit Ascending Auctions: Analytical and Empirical Insights
We develop a real-time estimation approach to predict bidders' maximum willingness to pay in a multiunit ascending uniform-price and discriminatory-price (Yankee) online auction. Our two-stage approach begins with a bidder classification step, which is followed by an analytical prediction model. The classification model identifies bidders as either adopting a myopic best-response (MBR) bidding strategy or a non-MBR strategy. We then use a generalized bid-inversion function to estimate the willingness to pay for MBR bidders. We empirically validate our two-stage approach using data from two popular online auction sites. Our joint classification-and-prediction approach outperforms two other naive prediction strategies that draw random valuations between a bidder's current bid and the known market upper bound. Our prediction results indicate that, on average, our estimates are within 2% of bidders' revealed willingness to pay for Yankee and uniform-price multiunit auctions. We discuss how our results can facilitate mechanism-design changes such as dynamic-bid increments and dynamic buy-it-now prices.
57631
Insights and analyses of online auctions
166893960
Priority pricing of integrated services networks
30721210
Economic analysis of consumer purchase intentions in electronic and traditional retail channels: competitive and strategic implications
We develop a multiattribute economic model to analyze consumer behavior for traditional and electronic channels based on consumers' risk profiles and risk-neutral or risk-averse. We then conduct comparative analyses of consumer purchase decisions to derive strategies for different channels. We study the attitudinal and economic factors that lead to specific purchase decisions under different combinations of price, delivery time, and product customization. We develop theoretical propositions concerning consumer channel switching behavior, incentives to consumers, and pricing strategies for different channels. We also use simulations to gain insights for cases where analytical model becomes intractable.
5230577
A cooperative framework for segmentation using 2D active contours and 3D hybrid models as applied to branching cylindrical structures
Hybrid models are powerful tools for recovery in that they simultaneously provide a gross parametric as well as a detailed description of an object. However, it is difficult to directly employ hybrid models in the segmentation process since they are not guaranteed to locate the optimal boundaries in cross-sectional slices. Propagating 2D active contours from slice to slice, on the other hand, to delineate an object's boundaries is often effective, but may run into problems when the object's topology changes, such as at bifurcations or even in areas of high curvature. Here, we present a cooperative framework to exploit the positive aspects of both 3D hybrid model and 2D active contour approaches for segmentation and recovery. In this framework the user-defined parametric component of a 3D hybrid model provides constraints for a set of 2D segmentations performed by active contours. The same hybrid model is then fit both parametrically and locally to this segmentation. For the hybrid model fit we employ several new variations on the physically-motivated paradigm which seek to speed recovery while guaranteeing stability. A by-product of these variations is an increased generality of the method via the elimination, of some of its ad hoc parameters. We apply our cooperative framework to the recovery of branching cylindrical structures from 3D image volumes. The hybrid model we employ has a novel parametric component which is a fusion of individual cylinders. These cylinders have spines that are arbitrary space curves and cross-sections which may be any star shaped planar curve.
626014
The use of hybrid models to recover cardiac wall motion in tagged MR images
We present a new algorithm for the automatic recovery of tag grid-line intersections in Tagged MR images of the left ventricle of the heart. Our method uses an active spring mesh to capture local properties of the motion and a global motion model to capture the global coherence of the motion. We recover the global component of the motion using robust estimation. Different motion models have been developed for short and long axis views of the heart. The algorithm has been tested on healthy and pathological data.
14870566
An information fusion framework for robust shape tracking
Existing methods for incorporating subspace model constraints in shape tracking use only partial information from the measurements and model distribution. We propose a unified framework for robust shape tracking, optimally fusing heteroscedastic uncertainties or noise from measurement, system dynamics, and a subspace model. The resulting nonorthogonal subspace projection and fusion are natural extensions of the traditional model constraint using orthogonal projection. We present two motion measurement algorithms and introduce alternative solutions for measurement uncertainty estimation. We build shape models offline from training data and exploit information from the ground truth initialization online through a strong model adaptation. Our framework is applied for tracking in echocardiograms where the motion estimation errors are heteroscedastic in nature, each heart has a distinct shape, and the relative motions of epicardial and endocardial borders reveal crucial diagnostic features. The proposed method significantly outperforms the existing shape-space-constrained tracking algorithm. Due to the complete treatment of heteroscedastic uncertainties, the strong model adaptation, and the coupled tracking of double-contours, robust performance is observed even on the most challenging cases.
29871849
Optimal Deployment of Triggers for Detecting Events
In active d atabases, rules are represented in the form of ECA (event-condition-action). Database events can be detected by defining triggers on the underlying application databases. Many-a-times, temporal conditions that limit the validity period of the event are as sociated with the ECA rule. The performance of the database can get adversely affected if such temporal constraints are checked (either at the application level or at database level) for every transaction (event) irrespective of whether that transaction (event) has occurred within the said time interval. This drawback can be avoided by optimizing the temporal constraints associated with the sub-events of a composite event based on the semantics of the composite event operators. This paper describes such an algorithm that optimizes the temporal constraints associated with (composite) events and improves the efficiency of the databases by creating and destroying triggers dynamically such that the semantics of the event is unchanged. The efficiency of the technique is validated by our experimental results.
2044368
AGATHA: An Integrated Expert System to Test and Diagnose Complex Personal Computer Boards
PA-RISC is Hewlett-Packard's (HP) reduced instruction set computer (RISC) architecture that is used in its high-performance computer systems (Mahon et al. 1986). Implementations of this architecture have produced some of the most complex processor boards that HP makes (Robinson et al. 1987; Gassman et al. 1987): They can contain as many as 8 very large scale integrated (VLSI) chips—most of them custom, from central processing units to bus controllers to floating-point pro-cessors—several high-speed random-access memory arrays, one or more high-speed buses with over 100 lines, and many other components. In large part because of this complexity, the testing of PA-RISC processor boards became a bottleneck, resulting in an undesirable backlog of undiagnosed boards, growing at a rate of 10 percent each month.