Sellers’ Peer Comparison Under Uncertainty in Online Marketplace
Bibliographic record
Abstract
How will peer pressure among sellers affect their operations in an online marketplace? Motivated by online platforms’ marketplace designs that prompt sellers to compare their performances, in this paper, we develop and study a price competition model in which sellers account for both profits and peer comparison outcomes. In our model, two sellers offer substitutable products, and each of them sets a price ex ante to maximize their expected total utility, which is the sum of one’s profit and the payoff from peer comparison. In particular, peer comparison takes place ex post based on sellers’ realized sales. It results in a penalty for one’s underperformance (i.e., sellers are behind-averse) or a reward for outperformance (i.e., sellers are ahead-seeking) relative to the other seller. Contrary to what extant research on social comparison would predict, we find that peer comparison is not always pro-competitive. Indeed, while the behind-aversion aspect of peer comparison fosters competition, the ahead-seeking aspect can be anti-competitive when the market uncertainty is sufficiently large. This is because market uncertainty causes a greater variation in sellers’ performance disparity ex post ( uncertainty effect ), which can have a more salient impact on sellers than the expectation of their performance gap ( comparison effect ); While sellers’ behind-aversion further aggravates the uncertainty effect and encourages them to take more aggressive actions, their ahead-seeking counterbalances the tension by absorbing part of it into the comparison effect and moderating the marginal disutility of lagging behind. Overall, we find that peer comparison can intensify sellers’ price competition, which lowers the expected profits and utilities for both sellers, benefits the consumers, and reduces the hosting platform’s profit. Our main insights are robust in a number of extensions, including general demand specifications, seller asymmetry, sellers’ misperceptions of market uncertainties, and consumers’ reference-dependent decision-making. They highlight the importance of sellers’ behavioral regularities in online platforms’ daily operations and shed light on marketplace designs regarding algorithmic transparency, information sharing, and so forth.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".