Abstract: An embodiment in accordance with the present invention provides a method for determining a demand function for an item. For example, the method includes determining a first estimate of the demand function for the item by utilizing a first auction having a first set of auction parameters. Additionally, the method includes determining a second estimate of the demand function for the item by utilizing a second auction using auction data from the first auction. The second auction has a second set of auction parameters based on the first estimate of the demand function.

Abstract: An apparatus and a method for extracting an underlying demand system for optimal product bundling and design. User input is received that indicates items for which a demand system is to be determined. Auction participants are selected. The structure of the auction is configured for determining demand complementarities of the items to be auctioned. At least one auction is then conducted using the configured structure so as to obtain bid data for each of the items that are auctioned. The bid data is then analyzed to determine an estimate of a demand system for the products.

Abstract: The present invention provides an automated estimation and optimization solution for selecting the optimal bid for an item in an auction. The characteristics of the auction are selected (e.g., auction format, reserve price). A relevant bidding model, based on the characteristics of the auction, is selected. The structure of the auction is estimated based on the relevant bidding model. A bid function is determined based on the auction structure and user inputs regarding the item being bid on and the characteristics of the rival bidders. An optimal bid is determined based on the bid function and user-defined evaluation criterion. An embodiment of the present invention provides a method and system that determines the latent elements of the auction environment taking into account the strategic and information conditions with minimal assumptions on the distributions of unobserved random elements.

Abstract: One embodiment of the present invention provides a method and system that computes the optimal level of the reserve price for an auction, with respect to a multiplicity of possible evaluation criteria that the end user of the system may specify. In one embodiment, structural elements characterizing the auction situation are estimated from bid data available on past auctions. The estimated structural elements are then used in the formulation of the user's expected payoff as a function of the reserve price. This objective function is then optimized to obtain the best level of the reserve price. In one embodiment, this method is implemented by a process executed on a computer system, under the control of software and firmware directing the operation of its processors and components. In one embodiment, a computer readable medium causes a computer system to execute the steps in a process for implementing the method.

Abstract: One embodiment of the present invention provides a method and system that determines the optimal preference policy for a market, such as an auction, with respect to a multiplicity of possible evaluation criteria that auction participants or other end users specify. In one embodiment, a method and system that configures the optimal preference policies that can be implemented in any market, particularly an auction, applicable to any auction format a market decision maker may wish to conduct. An embodiment of the present invention estimates bidders' private information and correspondingly identifies exploitable asymmetries to implement a preferential treatment policy. In one embodiment, this method is implemented on a computer system, under the control of software and firmware directing the operation of its processor and other components. In one embodiment, a computer readable medium causes a computer system to execute the method.

Abstract: The present invention provides an automated estimation and optimization solution for selecting the best auction format. An embodiment of the present invention provides a method and system that determines the latent elements of the auction environment taking into account the strategic and information conditions with minimal assumptions on the distributions of unobserved random elements. In one embodiment of the present invention, structural analysis of bid data from prior auctions is used to identify and estimate the distributions of bidders' private signals conditional on observable bidder characteristics. In one embodiment, the estimated signal distributions, identified by the structural analysis, are used to evaluate alternative auction formats and to select the best format from among a given set of candidates. In one embodiment, the present invention provides decision support tools to select an auction format based on structural econometric analysis of available data on the market environment.

Abstract: A method and apparatus for determining private information and risk attitudes of bidders at auctions. By varying auction formats, the underlying risk attitudes and joint distribution of bidders' private information is extracted. Bids submitted in utility-independent auctions are used to determine the bidders private information. Risk attitudes for bidders are determined using utility-dependent auctions and using the determined bidders' private information. When there is insufficient auction data for determining private information and risk attitudes, additional auctions are conducted to obtain the needed auction data. The results can then be used for designing auctions and for determining bids to be made by buyers.

Abstract: A computer-implemented automated decision support system for designing an auction for a given item includes a structure extractor that estimates unknown elements of market structure of the auction based on auction characteristics data extracted from historical auctions for similar items and a bidding model matching the extracted auction characteristics data. The decision support system also includes a bidding behavior predictor that predicts bidding behaviors of bidders in the auction based on the estimated unknown elements of market structure and characteristics of the auction. In addition, the system includes an optimizer that employs an evaluation criterion to generate an evaluation of the auction based on (1) the estimated unknown elements of market structure and (2) the predicted bidding behavior of bidders. A method of providing an automated auction analysis is also described.