Impact of Network Structure on New Service Pricing
Why this work is in the frame
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Bibliographic record
Abstract
We analyze a firm’s optimal pricing of a new service when consumers interact in a network and impose positive externality on one another. The firm initially provides its service for free, leveraging network externality to promote rapid service consumption growth. The firm raises the price and starts earning revenue only when a sufficient level of consumer interactions is established. Incorporating the local network effects in a nonstationary dynamic model, we study the impact of network structure on the firm’s revenue and optimal pricing decision. We find that the firm delays the timing of service monetization when it faces a more strongly connected network despite the fact that such a network allows the firm to monetize the service sooner by resulting in faster consumption growth. We also find that the firm benefits from network imbalance; that is, the firm prefers a network of consumers with varying degrees of connections to that with similar degrees of connections. We also study the value of knowing the network structure and discuss how such knowledge impacts the firm’s profitability. Our analyses rely on the techniques from algebraic graph theory, which enable us to solve the firm’s high-dimensional dynamic pricing problem by relating it to the network’s spectral characteristics.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it