Bibliographic record
Abstract
Problem definition: We study a dynamic market over a finite horizon for a single product or service in which buyers with private valuations and sellers with private supply costs arrive following Poisson processes. A single market-making intermediary decides dynamically on the ask and bid prices that will be posted to buyers and sellers, respectively, and on the matching decisions after buyers and sellers agree to buy and sell. Buyers and sellers can wait strategically for better prices after they arrive. Academic/practical relevance: This problem is motivated by the emerging sharing economy and directly speaks to the core of operations management that is about matching supply with demand. Methodology: The dynamic, stochastic, and game-theoretic nature makes the problem intractable. We employ the mechanism-design methodology to establish a tractable upper bound on the optimal profit, which motivates a simple heuristic policy. Results: Our heuristic policy is: fixed ask and bid prices plus price adjustments as compensation for waiting costs, in conjunction with the greedy matching policy on a first-come-first-served basis. These fixed base prices balance demand and supply in expectation and can be computed efficiently. The waiting-compensated price processes are time-dependent and tend to have opposite trends at the beginning and end of the horizon. Under this heuristic policy, forward-looking buyers and sellers behave myopically. This policy is shown to be asymptotically optimal. Managerial implications: Our results suggest that the intermediary might not lose much optimality by maintaining stable prices unless the underlying market conditions have significantly changed, not to mention that frequent surge pricing may antagonize riders and induce riders and drivers to behave strategically in ways that are hard to account for with traditional pricing models.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".