Bibliographic record
Abstract
Marketing on random networks displays similarities to epidemiological models in the sense that "word-of-mouth" information passes between individuals and may "infect" susceptible buyers such that they end up buying the product.The difference to epidemics is that there are usually many competing products (rather than just one disease), and in addition to word-of-mouth transmission, products are also advertised by the producers, which can be thought of as external nodes connected to the network.In this paper we develop a model in which these various transmission pathways compete, and, in addition, where product fatigue and product switching are possible.This is a genuine and realistic extension of the model developed in [M.Li, R. Edwards, R. Illner, and J. Ma, Commun.Math.Sci., 13, 497-509, 2015], where a customer would never abandon a product after purchase.The model presented here is similar to and was inspired by SIS epidemiological models.We discuss the homogeneous limit for a fully connected graph, present some analytical properties of the models and conduct a number of numerical experiments, including an investigation of a modelling assumption we call "edge chaos".The validity of this assumption turns out to depend on the type of the underlying random network.
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) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".