Why we need to account for human behavior and decision-making to effectively model the non-linear dynamics of livestock disease
Bibliographic record
Abstract
Animal disease costs the livestock industries billions of dollars annually. These costs can be reduced using effective biosecurity. However, costs of biosecurity are steep and benefits must be weighed against the uncertain infection risks. Much effort has gone into determining efficacy of different biosecurity tactics and strategies. Unfortunately, the variability in human behavior and decision-making when confronted with risk information has largely been overlooked. Here we show that use of the human behavioral component is necessary to understand the patterns of infection incidence in livestock industries. Using an agent-based model developed with a foundation of supply chain and industry structural data, we integrate human behavioral data generated using experimental games that parameterizes communication strategies, learning, psychological discounting and categorization of human behavior along a risk aversion spectrum. The influence of risk communication strategies on human behavior can be tested with experimental gaming simulations and their impact on the system can be projected using agent-based models, delivering feedback to increase disease resiliency of production systems.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".