Equilibrium anti-counterfeiting strategies with deceptive counterfeits: Proactive, reactive, or instantaneous?
Bibliographic record
Abstract
This papers studies anti-counterfeiting strategies with deceptive counterfeits. We develop a model to study the interactive anti-counterfeiting and counterfeit hiding decisions between the genuine brand company and a deceptive counterfeiter. Facing deceptive counterfeits, the genuine brand company can choose to adopt a proactive, instantaneous, or reactive anti-counterfeit strategy. We first examine these strategies and then characterize the equilibrium outcomes. Surprisingly, we reveal that the genuine brand company does not necessarily benefit from heavier penalty and the counterfeiter is not necessarily worse off with more costly counterfeit hiding efforts. Interestingly, the counterfeiter’s hiding effort decreases with heavier penalty when the genuine brand company’s anti-counterfeit and the counterfeiter’s hiding decisions are sufficiently efficient. Besides, higher levels of counterfeit imitation or penetration can either hurt or benefit the genuine brand company. Whether the counterfeiter is better off with deeper counterfeit penetration depends on the status quo. Furthermore, the counterfeiter exerts fewer counterfeit hiding efforts when the genuine brand company exerts more anti-counterfeit efforts, while the anti-counterfeit effort increases with the counterfeit hiding effort, regardless of the anti-counterfeiting strategies. Finally, for decision makers and policy makers, we discuss the implications for anti-counterfeiting strategies in practice.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".