COMPARATIVE ANALYSIS OF PRICING POLICIES IN THE MARKET FOR NETWORK GOODS
Bibliographic record
Abstract
Research goal: To develop a mathematical apparatus for evaluation of the different pricing policies of the monopolist company in the market for network goods. Methodology: Combination of the neoclassical theory methodologies, investment analysis, and mathematical modeling. Results: The economic-mathematical model of dynamic pricing in the market for network goods is developed in terms of the supplier monopoly. A comparative evaluation of different pricing policies is carried out based on computer experiment. Conclusions and significance: Two important quality indicators of the investment project on the development, production and after-sales service of network goods, namely, net present value of the project (NPV) and discounted payback period (DPB), show different behavior in time. Maximization of NPV is achieved by using subscription fees as the main source of income, while the challenge of reducing DPB tips the choice in favor of a combination of acquisition cost and maintenance fee. The rationale of scientific novelty. The novelty of the approach lies in (1) consideration in the analysis of the three cost types: investment costs, including sunk costs; current costs for production and sales; and the costs of maintaining the value; (2) the transition from the concept of the “value of the good” (expressed in ruble/unit) to the concept of the “use value of the good” (expressed in ruble/unit/period); (3) taking into account the peculiarities of increasing number of consumers of a good over time (logistic curve); (4) taking into account the number of potential consumers as a factor determining the value of a good; (5) use of the discounted payback period as a supplementary project quality indicator.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".