Application of blockchain in the secondary market with counterfeiting
Bibliographic record
Abstract
It is not uncommon for customers who intend to buy a used product in the secondary market to end up with a counterfeit because they have imperfect information about product authenticity . Blockchain is being piloted as a cutting-edge solution to this challenge. We use a two-period game to study the impact of utilizing blockchain to combat counterfeit products in the secondary market. We show that, even when the cost of implementing blockchain is negligible, the manufacturer can be better off incurring reputation damage than adopting blockchain. Further, the used goods reseller can be worse off from blockchain, even though that seller is not responsible for the implementation cost and benefits from blockchain’s signaling capability. We also demonstrate that the counterfeiter can benefit as a result of blockchain. When the quality of a fake product is sufficiently low, blockchain lowers consumer surplus . The winning situation of blockchain between the manufacturer, reseller, and customers is achieved only when the fake product is of intermediate quality. Blockchain can be powerful in situations when used products have a low perceived quality; otherwise, blockchain may not be ideal.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".