Examining the Yahoo! Sponsored Search Auctions: A Regression Discontinuity Design Approach
Bibliographic record
Abstract
The sponsored search auction is a successful pricing mechanism which helps search engine companies sell navigation service to advertisers and generate multibillion dollar revenue. For the two popular sponsored search auctions—the Generalized First Price (GFP) auction and the Generalized Second Price (GSP) auction—current consensus in both the industry and academia is that the GSP auction is more stable than the GFP auction. Specifically, in the GSP auction, bidders are less likely to “game the system”, meaning that an individual bidder will change his bid less frequently and his bid range will be smaller. This paper examines this prevailing belief using a Regression Discontinuity Design (RDD) approach and finds that after bidders switch to the GSP auction, they actually change their bid 19% more frequently and increase their daily bid range by 46%. The paper suggests that the prevailing automated bidding strategies should not be ignored in explaining bidders’ behavior.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".