Uncertainty-Informed Automatic Bidding via Quantile Regression
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Solution Overview
Problem
Existing bidding systems for reserved content spaces on websites and mobile applications face inefficiencies due to speculative bidding, leading to suboptimal bids and resource misallocation, as content suppliers struggle to accurately estimate the utility of their content.
Innovation Solution
The method incorporates uncertainty measurements from bidding models, such as quantile regression, into bid formulas to adjust bids based on model uncertainty, becoming more conservative during high uncertainty and more aggressive when certainty increases, thereby optimizing bidding strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If speculative bidding is used to estimate expected utility, then bidding can be performed automatically without detailed analysis, but bidding accuracy deteriorates leading to suboptimal bids
Solution Approach 1:
The system implements feedback loops where bid outcomes are fed back into the bidding model to continuously refine uncertainty measurements. This allows the automated bidding system to learn from past performance and improve accuracy over time while maintaining automation. The feedback mechanism adjusts bid amounts based on measured uncertainty from previous auctions.
Solution Approach 2:
The system dynamically changes bid parameters based on measured uncertainty levels. When uncertainty is high, bid amounts are adjusted conservatively; when uncertainty is low, bids can be more aggressive. This parameter adaptation resolves the contradiction by making bid accuracy dependent on real-time uncertainty measurements rather than static speculative estimates.
2Reliability
If conservative bidding is used during high uncertainty, then risk of suboptimal bids is reduced, but bidding efficiency deteriorates
Solution Approach 1:
The bidding system dynamically adjusts between conservative and aggressive strategies based on real-time uncertainty measurements. Rather than maintaining a fixed conservative approach, the system transitions between risk postures as uncertainty levels change, thereby maintaining both reliability during high uncertainty and efficiency during low uncertainty periods.
Solution Approach 2:
Uncertainty measurement acts as an intermediary between risk control and bidding efficiency. This intermediary metric enables the system to navigate between conservative risk management and aggressive efficiency-seeking behaviors by translating complex market conditions into actionable bid adjustments.
3Use of energy by moving object
If speculative estimates are used for content utility, then resource consumption is reduced in terms of detailed analysis, but resource allocation deteriorates leading to inefficient usage
Solution Approach 1:
The system replaces detailed mechanical analysis of content utility with a statistical uncertainty measurement approach. Instead of performing computationally intensive detailed content evaluations, the system uses uncertainty metrics from bidding models to guide resource allocation decisions, thereby reducing computational energy consumption while improving allocation efficiency.
Data Source
AI summary
This technology generally relates to a method for leveraging a measure of bidding model uncertainty to directly improve automatic bidding. The methods may include measuring the inherent uncertainty of automatic bidding models using techniques, such as quantile regression. Further, the measure of bidding model uncertainty may be incorporated into bid formulas to inform the generated bids for an auction. The method may be further formulated to modify the bids to be more conservative when the bidding model uncertainty is higher. Once the uncertainty level of the bidding model is reduced to a more stable level, the bidding method will resume generating bids with more efficiency.