Securing containerized supply chain through public and private partnership
Bibliographic record
Abstract
Global trade is seafaring commerce; 90% of traded goods are carried by maritime transport, which has become vulnerable to security risks. This has led governments to initiate security programs serving tens of thousands of members worldwide. This paper studies the government's incentive design and the interaction between Customs inspection capacity and the incentives offered in security programs. Using the theory of incentives, we investigate the value of a partnership in improving the security of containerized supply chain. We developed a sequential game featuring the government, firms, and an adversary. The government selects the inspection capacity and incentives to foster the partnership, namely, an operational benefit in the form of a reduced inspection rate, and a security benefit obtained through reductions in the risks of adversarial infiltration. Firms subsequently decide on a collaboration level, followed by a strategic adversary's decision to infiltrate. Using the adversary's best response, we show that, in equilibrium, the government ranks all the firms and induces collaboration with only a subset of them. We demonstrate that, in equilibrium, while security incentives may benefit all participants, tailored operational incentives should be offered strictly to foster collaboration. The required condition to implement the inspection‐free lane for members is also characterized. Our results also inform practice to help security policymakers understand the underlying interaction between Customs inspection capacity and incentive design in forging collaboration with private firms. In particular, as firms opting for collaboration experience lower inspection rates, this further reduces overall congestion, which, in turn, creates a positive externality for nonmember firms. Therefore, having an excess inspection capacity may result in shorter wait times that could dissuade firms from collaborating.
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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 | 0.000 | 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 | 0.000 | 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".