Algorithmic pricing: Implications for marketing strategy and regulation
Bibliographic record
Abstract
Over the past decade, a growing number of firms have delegated pricing decisions to algorithms in consumer and business markets such as travel, entertainment, and retail, as well as in platform markets such as ride-sharing. We define algorithmic pricing as “the use of programs to automate the setting of prices.” Firms adopt algorithmic pricing to optimize their prices in response to changing market conditions and to leverage the efficiency gains from automation. Advances in information technology and the increased availability of digital data have further facilitated the use of algorithm-driven pricing strategies. Yet adopting algorithmic pricing is not merely a technical upgrade — it is a strategic decision that must align with a company’s existing and future marketing strategies. Moreover, algorithmic pricing can raise various regulatory concerns regarding potential threats to competition and the legality of price discrimination. This paper discusses the implementation of algorithmic pricing in the context of firms’ marketing strategies and regulatory frameworks, while outlining an agenda for future research in this increasingly important area
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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 | 0.016 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.024 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".