Strategic bidding in a Combinatorial Clock auction: The 700 MHz Canadian auction
Bibliographic record
Abstract
Using data provided by Industry Canada this paper seeks to understand what strategic bidding is in the 2014 700-MHz Canadian auction. The auction format is known as Combinatorial Clock. Its design seeks to induce truthful bidding, that is, a bidder is expected to bid in a way that at each round of the Clock Rounds stage her utility is maximised, and expected to reveal her true valuation for each bundle that make up her final bid in the Combinatorial stage. The Combinatorial Clock auction is an innovative auction design which aims to overcome some of the problems evidenced in the application of the Simultaneous Ascending auction, a format that has been widely popular for assigning radio spectrum to commercial providers of mobile communications services over the last 20 years. The paper uses publicly available data from the auction by Industry Canada. By focusing on the longest of the three auction stages, the Clock Rounds, it examines the evolution of several auction indexes as a means to devise an approach to understand bidders’ strategic behaviour. It also uses a Revealed Preference theory framework to test whether bidders bidding was consistent with utility maximisation. When it is not, the paper analyses evidence to support claims of strategic behaviour.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".