Essays on Pricing and Promotion Policies of Digital Platforms
Bibliographic record
Abstract
This dissertation investigates the impact of cross-network effects on optimal product/service segmentation pricing and dynamic promotion within digital platforms. Specifically, it examines (1) the implications of vertical segmentation for digital platforms in terms of profitability, sellers' profitability, and buyers' utility, and (2) the optimal dynamic platform promotion alongside user-generated promotion from the embryonic stage to maturity. These topics are addressed through game theoretic models, dynamic programming, and comparative statics to derive managerial insights. The thesis draws upon the literature of economics and marketing, particularly focusing on two-sided platforms, information asymmetry, and dynamic promotion. The dissertation comprises the following inter-related chapters: (1) Introduction, (2) Literature Review on Digital Platforms and Related Businesses, (3) Vertical Segmentation Implications for Digital Platforms, (4) Optimal Dynamic Platform Promotion Policy under Evolution, and (5) Conclusion. The introduction section discusses the importance of digital platforms in the modern economy. The literature review section examines the inception of digital platforms and the differences in marketing strategies, competition, product/service categorization, and business evolution compared to traditional businesses. Gaps in the literature that the thesis aims to address are identified. Chapter 3 considers two broad situations: when the platform sets the price or when the seller sets the price. Equilibrium outcomes for integrated or segmented markets are derived, along with outcomes under perfect or imperfect information about product/service quality. Chapter 4 employs dynamic programming to derive Euler equations linking optimal promotion across periods. Three cases are considered: buyers/sellers changing over time, buyers changing but sellers fixed, and a three-period game with buyers/sellers changing over time but platform promotion limited to the first two periods. MATLAB is used to code the dynamic programming model, followed by simulations to derive steady-state outcomes and conduct comparative statics. The conclusion chapter summarizes the two papers and identifies potential areas for future research.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".