Position-Based Auction Allocation Mechanism
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Solution Overview
Problem
Current auction systems for advertisement placement on search engines do not effectively manage bidder preferences for specific ranks, leading to inefficiencies in allocation and pricing, particularly in achieving envy-free or symmetric Nash equilibria.
Innovation Solution
The system allows bidders to specify minimum rank indicators for acceptable positions, assigning ranked goods based on bids and these indicators, and determines costs based on the next highest bidder's bid, ensuring an envy-free or symmetric Nash equilibrium similar to the Vickrey-Clarke-Groves auction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If current auction systems permit advertisers to specify a single bid amount, then the auction mechanism is simple, but the system cannot effectively manage bidder preferences for specific ranks leading to inefficiencies in allocation and pricing
Solution Approach 1:
The patent segments the bid into two distinct components: a bid amount and a minimum rank indicator. This segmentation allows the system to independently handle pricing (bid amount) and allocation preferences (minimum rank indicator), resolving the contradiction by adding preference management capability without creating a completely new complex mechanism.
Solution Approach 2:
The patent adds another dimension to the bid structure by introducing the minimum rank indicator as a separate parameter. This dimensional extension enables the system to manage bidder preferences for specific ranks while maintaining the simplicity of the original bid amount mechanism.
2Productivity
If the system assigns ranked goods based on bids and minimum rank indicators, then allocation efficiency improves, but the pricing mechanism becomes more complex
Solution Approach 1:
The pricing mechanism is designed to be self-service based on the next highest bidder's bid and minimum rank indicator. The system automatically determines the price without requiring complex external calculations or interventions, maintaining simplicity while achieving efficient allocation.
Solution Approach 2:
The pricing mechanism uses feedback from the next highest bidder's bid and minimum rank indicator to determine the final price. This feedback loop allows the system to adjust pricing dynamically based on actual bidding behavior and preferences, improving allocation efficiency while keeping the mechanism relatively simple.
3Reliability
If the system ensures envy-free or symmetric Nash equilibrium, then fairness and economic efficiency improve, but the algorithm complexity increases
Solution Approach 1:
The patent changes the parameters of the auction mechanism by introducing the minimum rank indicator alongside the bid amount. This parameter change enables the system to achieve envy-free or symmetric Nash equilibrium by allowing bidders to express both pricing willingness and rank preferences, thereby improving reliability without requiring fundamentally complex algorithms.
Data Source
AI summary
A method is described that includes accessing bids for ranked goods and minimum rank indicators that each specifies a lowest rank for a good that is acceptable to a bidder, assigning a ranked good to a first bidder based on a first bid and a first minimum rank indicator each associated with the first bidder, and outputting a signal indicative of a cost of the assigned rank good based on a second bid associated with a next highest bidder having a second minimum rank indicator that specifies a rank that is inclusive of a rank of the ranked good assigned to the first bidder.