Game Theory and the <i>Competition Act</i>: Winners and Losers in Canadian Merger Review
Bibliographic record
Abstract
In 1986 Canada established new rules for the review of mergers which lessen competition. In doing so, it created a system whereby economic efficiency would be the paramount goal of merger review. Moreover, a specialized court called the Competition Tribunal would oversee the review of mergers in an adjudicative process. However, since that time Canada’s merger review process has evolved into an administrative, rather than adjudicative, process: one in which the Competition Tribunal plays virtually no role, and in which an administrative branch of government (the Competition Bureau), dominates merger review. The authors use a game theoretic approach to examine why this is. In doing so, they provide one possible explanation for the failure of Canada’s merger review process to achieve Parliament?s intent: the incentives and knowledge about the rules of the game as between the players who participate in the merger review process are misaligned. The authors do so in the context of very recent case law. This case law suggests a paradigm shift, and possibly opens the door to a greater role for adjudication in the attainment of Parliaments merger review goals.
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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.017 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".