Essays on designing optimal spectrum license auctions
Bibliographic record
Abstract
Basically, my dissertation focuses on License Auctions. Four chapters of my dissertation are theoretical analysis of license auctions. Broadly speaking, I analyze the effects of different auction rules on revenue, efficiency and social welfare. The first chapter studies the flaw in the design of the 2000 Turkish GSM auction. In this auction, the Turkish government wants to raise as much revenue as possible and to increase competition in the cell-phone market by selling two licenses to new firms via a sequential auction, but it ends up with only one license sold. I identify this auction design failure. And I also show that if the auction were designed as a “simultaneous auction”, the government would sell two licenses and receive more revenue. In the second chapter, I show that if the cost asymmetry between the bidding firms is large enough, then having fewer firms in the market will surprisingly result in higher social welfare. This result is contrast to the common or general case in which “social welfare” will be higher if there are more firms competing in the market. In the third chapter, I characterize the optimal bidding strategies of local and global bidders for two heterogeneous licenses in a multi-unit simultaneous ascending auction with synergies. I determine the optimal bidding strategies in the presence of an exposure problem and show that global bidders may accept a loss even when they win all licenses and moreover, if a “bid-withdrawal” rule is introduced to the auction, the exposure problem disappears, and the simulation results show that revenue will be higher. In the last chapter, I study the Canadian AWS auction in which 40 percent spectrum are set aside for new firms. I characterize the effect of spectrum set-aside auctions on seller's revenue, consumer surplus and social welfare. I show that a spectrum set aside may not only encourage new entry and increase competition in the downstream market, but also under some circumstance, decreases the seller's revenue and consumer surplus. But a spectrum set aside results in inefficient allocation, and this inefficient entry further reduces social welfare.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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 source (direct Gemma or distilled Codex), 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".