Bibliographic record
Abstract
The recent advances of information technologies make price discrimination more prevalent and more complicated.Flooded by a large number of variables found by data mining algorithms, pricing managers are perplexed by the task of selecting variables for price discrimination to maximize profits.However, relevant literature remains scarce.This paper attempts to investigate how a monopoly seller should determine the optimal combination of pricing variables.The criterion found is similar to the selection of independent variables for linear regression; it is revenue-maximizing to select the variable that best reduces the residual variance of buyer's willingness-to-pay.When incorporating costs associated with pricing metrics into this model, I propose a linear-running-time algorithm to solve this problem with any general cost structure.As to the implications for public policy, the welfare analysis shows that the monopoly seller will use an excessive number of metrics compared with the socially optimal level.By linking this theoretical model to a probit model, I demonstrate how to use this model to solve the price discrimination problem of an electronic greeting cards website.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".