Strategic Consumer Cooperation in a Name‐Your‐Own‐Price Channel
Bibliographic record
Abstract
Supplier reluctance to openly advertise highly discounted products on the Internet has stimulated development of “opaque” name‐Your‐Own‐Price sales channels. Unfortunately (for suppliers), there is significant potential for online consumers to exploit these channels through collaboration in social networks. In this paper, we study three possible forms of consumer collaboration: exchange of bid result information, coordinated bidding, and coordinated bidding with risk pooling. We propose an egalitarian total utility maximizing mechanism for coordination and risk pooling in a bidding club and describe characteristics of consumers for whom participation in the club makes sense. We show that, in the absence of risk pooling, a plausible bidding club strategy using just information exchange gives almost the same benefits to consumers as coordinated bidding. In contrast, coordinated bidding with risk pooling can lead to significantly increased benefits for consumers. The benefits of risk pooling are highest for consumers with a low tolerance to risk. We also demonstrate that suppliers that actively adjust for such strategic consumer behavior can reduce the impact on their businesses and, under some circumstances, even increase revenues.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".