An Agent‐Based Model of Border Enforcement for Invasive Species Management
Bibliographic record
Abstract
This paper presents a model of border enforcement in order to address trade‐related invasive species risk. An agent‐based modeling (ABM) framework was developed based on a theoretical economic model that incorporates a spatially explicit damage function. The framework was applied to a representative commodity (broccoli), invasive species (crucifer flea beetle), ports‐of‐entry (Calexico and Otay Mesa U.S./Mexico land ports), and vulnerable location (California). The ABM evaluated the economic impacts of port‐specific and importer‐specific enforcement regimes, enabling regulators to improve both the allocation of scarce enforcement resources and the effectiveness of current enforcement policies. The analysis generated several policy relevant findings concerning importing firm behavior and suggests conditions under which increasing enforcement may or may not significantly reduce invasive species risk and associated crop damages. The analysis illustrates that with a more realistic parameterization, the ABM could be used to make real‐world policy decisions concerning allocation of limited resources across ports‐of‐entry. Dans le présent article, nous présentons un modèle de renforcement des frontières pour examiner le risque d'introduction d'espèces envahissantes par le biais d'échanges commerciaux. Nous avons élaboré un modèle multiagent fondé sur un modèle économique théorique qui intègre une fonction de dommage spatiale. Nous avons appliqué ce modèle à une denrée représentative (le brocoli), à une espèce envahissante (l'altise des crucifères), à des points d'entrée (les points d'entrée terrestre de Calexico et d'Otay Mesa à la frontière des États−Unis et du Mexique) et à un endroit vulnérable (la Californie). Le modèle multiagent a évalué les répercussions économiques des régimes de renforcement spécifiques à un point d'entrée et à un importateur, permettant aux organismes de réglementation d'améliorer à la fois l'allocation des ressources de renforcement qui sont rares et l'efficacité des politiques de renforcement actuelles. L'analyse a généré plusieurs résultats quant aux politiques concernant le comportement des entreprises importatrices et indique des conditions dans lesquelles l'accroissement du renforcement pourrait diminuer considérablement ou pas le risque d'introduction d'espèces envahissantes pouvant causer des dommages aux cultures. L'analyse a montré que, à l'aide d'une paramétrisation plus réaliste, le modèle multiagent pourrait être utilisé pour prendre des décisions stratégiques du monde réel quant à l'allocation des ressources limitées dans les points d'entrée.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".