Quality and price personalization under customer recognition: A dynamic monopoly model with contrasting equilibria
Bibliographic record
Abstract
We present a model of market hyper-segmentation, where a monopolist acquires within a short time all information about the preferences of consumers who purchase its vertically differentiated products. The firm offers a new price/quality schedule after each commitment period. Lower consumer types may have an incentive to delay their purchases until next period to obtain a better introductory offer. The monopolist counters this incentive by offering higher informational rents. Considering the dynamic game played by the monopolist and its customers, we find that there is always a Markov perfect equilibrium (MPE) in which the firm immediately sells the good to all customers, offering the Mussa-Rosen static equilibrium schedule to first time customers (and getting full commitment profits). However, if the commitment period between two offers is long enough, there is another MPE with gradual market expansion. Contrary to the Coasian result for a durable-good monopoly, we find that in both equilibria the profit of the monopolist increases (and the aggregate consumers surplus decreases) as the interval of commitment shrinks.The model yields policy implications for regulations on collection and storage of customers information.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".