Surge Pricing and Two-Sided Temporal Responses in Ride Hailing
Bibliographic record
Abstract
Problem definition: We investigate surge pricing in ride-hailing platforms from a temporal perspective, highlighting strategic behavior by riders and drivers and that drivers respond to surge pricing much more slowly than riders do. Academic/practical relevance: Surge pricing in ride-hailing platforms is a pivotal and controversial subject. Despite abundant anecdotal evidence, strategic behavior by riders and drivers has not been formally studied in the literature. Methodology: We adopt and analyze a classic two-period, game-theoretical model as in the strategic consumer literature. Results: We identify two types of equilibrium pricing strategies. The first consists of a short-lived, sharp price surge followed by a lower price, which we refer to as skimming surge pricing (SSP). The second consists of a low initial price followed by a higher price, which we refer to as penetration surge pricing (PSP). We find that PSP equilibria are generally superior to SSP equilibria when both exist but require platforms to share demand–supply information with drivers. Managerial implications: The SSP equilibrium rationalizes the controversial sharp surge-pricing practice: the short-lived sharp price surge causes many high-value riders to voluntarily wait out the initial surge period, which attracts additional drivers to the region to serve riders at a much lower price than the initial surge price. The theoretically superior PSP equilibrium suggests that a vastly different approach may improve surge pricing and highlights the potential value and importance for platforms to share demand–supply information with drivers.
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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 | 0.015 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".