Monopoly Versioning of Information Goods When Consumers Have Group Tastes
Bibliographic record
Abstract
Large sunk costs of development, negligible costs of reproduction, and distribution resulting in economies of scale distinguish information goods from physical goods. Versioning is a way firms may take advantage of these properties. However, in a baseline model where consumers differ in their tastes for quality, an information goods monopolist only offers one version, and this differs from what we observe in practice. We explore formulations that add features to the baseline model that result in a monopolist offering multiple versions. We examine versioning where consumers differ in individual tastes for quality, and groups of consumers that share the same group taste are delineated by segments of individual tastes. We find that if groups have mutually exclusive characteristics—a horizontal dimension—that they value relative to the shared characteristics, then versioning is optimal. Consequently, any horizontal differentiation in product line design favors versioning. In addition, when group tastes are hierarchical such that higher taste groups value characteristics that lower taste groups value but not vice versa—a vertical dimension—as long as the valuations of the higher and adjacent lower taste group are sufficiently close, then versioning is also optimal. Our conditions, which also help determine how many versions are optimal, are based on exogenously defined parameters so that it is feasible to check them in practice.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".