Competition and coopetition for two‐sided platforms
Why this work is in the frame
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Bibliographic record
Abstract
Two‐sided platforms have become omnipresent. In this context, firms compete not only for customers but also for flexible self‐scheduling workers who can work for multiple platforms. We consider a setting where two‐sided platforms simultaneously choose prices and wages to compete on both sides of the market. We assume that customers and workers each follow an endogenous generalized attraction model that accounts for network effects. In our model, the behavior of an agent depends not only on the price or wage set by the platforms, but also on the strategic interactions among agents on both sides of the market. We show that a unique equilibrium exists and that it can be computed using a tatônnement scheme. The proof technique for the competition between two‐sided platforms is not a simple extension of the traditional (one‐sided) setting and involves different arguments. Armed with this result, we study the impact of coopetition between two‐sided platforms, that is, the business strategy of cooperating with competitors. Motivated by recent practices in the ride‐sharing industry, we analyze a setting where two competing platforms engage in a profit sharing contract by introducing a new joint service. We show that a well‐designed profit sharing contract (e.g., under Nash bargaining) will benefit every party in the market (platforms, riders, and drivers), especially when the platforms are facing intensive competition on the demand side. However, if the platforms are facing intensive competition on the supply side, the coopetition partnership may hurt the profit of at least one platform.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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