The Rising Threat of Algorithmic Collusion: Recommendations for Enforcing the Competition Act
Bibliographic record
Abstract
With the rise of artificial intelligence, firms across varying markets are increasingly adopting algorithmic pricing tools. While pricing algorithms can increase efficiency and provide lower prices for consumers, they also introduce new risks. In particular, the likelihood of tacit collusion increases when rival firms use the same third-party pricing software. This emerging phenomenon, referred to as "algorithmic collusion,” enables firms to set prices above the Bertrand equilibrium without explicit communication. These algorithms effectively overcome traditional barriers to collusion by facilitating coordination and sustaining cooperative pricing behavior, posing a significant challenge for antitrust enforcement. Although the Competition Act contains provisions that could address algorithmic collusion, the mechanisms for enforcement must be strengthened. After an analysis of tacit collusion via Q-learning algorithms and Canada’s policy landscape, this paper concludes with recommendations to ensure the Competition Bureau can address the risks posed by tacit collusion via algorithms.
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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 | 0.017 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.017 |
| Scholarly communication | 0.016 | 0.018 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.016 | 0.014 |
| Insufficient payload (model declined to judge) | 0.052 | 0.010 |
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".