Artificial intelligence and dynamic pricing: a systematic literature review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
With dynamic pricing becoming more widespread across various industries, artificial intelligence has made it even more sophisticated and widespread. The authors conducted a systematic literature review and analyzed a dataset of 95 peer-reviewed articles from international journals selected in Web of Science and Scopus to better understand artificial intelligence’s impact on dynamic pricing. The authors identified four clusters related to financial modeling, market dynamics, commodity markets, and behavior and decision-making. They also found that China has overtaken the USA in the number of published articles. They identified the themes of market simulation investment, crude oil commodity dependence, and behavior traders’ prices. A systematic literature review is essential to understand the impact of artificial intelligence on dynamic pricing and its implications for businesses, consumers, and society.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it