A monopoly pricing model for diffusion maximization based on heterogeneous nodes and negative network externalities (Case study: A novel product)
Bibliographic record
Abstract
Social networks can provide sellers across the world with invaluable information about the structure of possible influences among different members of a network, whether positive or negative, and can be used to maximize diffusion in the network.Here, a novel mathematical monopoly product pricing model is introduced for maximization of market share in noncompetitive environment.In the proposed model, a customer's decision to buy a product is not only based on the price, quality and need time for the product but also on the positive and negative influences of his/her neighbors.Therefore, customers are considered heterogeneous and a referral bonus is granted to every customer whose neighbors also buy the product.Here, the degree of influence is directly related to the intensity of the customers' relationships.Finally, using the proposed model for a real case study, the optimal policy for product sales that is the ratio of product sale price in comparison with its cost and also the optimal amounts of referral bonus per customer is achieved.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".