Pricing mechanisms for a durable good monopolist
Bibliographic record
Abstract
A durable good is a long-lasting good that can be consumed repeatedly over time. Theoretically less is known about durable goods than their more well-studied counterparts, perishable goods. However, on the practical side, durable goods abound and are very familiar to us. For example, many of the most important consumer items are (at least to some extent) durable, such as land, housing, cars, diamonds etc. A duropolist is a monopolist in the market of a durable good { topically, duropolists include several well-known purveyors of digital goods. In this talk, we examine the strategic issues facing duropolists. Pricing a durable good is not as simple as it may appear. Specifically, whilst durable goods are more desirable to the consumer, it is questionable whether a duropolist has additional monopoly power beyond that of an equivalent monopolist for a perishable good. Indeed, quite the opposite may be true. In 1972, Richard Coase made the startling conjecture that, in fact, a duropolist has no monopoly power at all! The argument being that a duropolist is not, in essence, a monopolist: the firm does face stiff competition { not from other firms but, rather, from future incarnations of itself. There have since been several proofs and disproofs of the conjecture for under assorted economic models and time horizons. We discuss this, and also real-world strategies that duropolists use to avoid the conundrum highlighted by Coase. Our main results are to quantify how well various price mechanisms perform. Specifically, we give tight bounds, for the finite time horizon case, on the relative profitabilities of these mechanisms in terms of the number of time periods and the total number of consumers. In doing so, we quantify the the extent to which a duropolist can generate higher profits than an equivalent monopolist for a perishable good.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| 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 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.027 | 0.003 |
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".