Agent-Based Modelling for the Cost-Benefit Analysis of Adaptation Strategies: A Case Study from Inuit Nunangat
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Bibliographic record
Abstract
This paper presents a case study of how agent-based modelling can be utilized to conduct a cost-benefit analysis of two differing adaptational strategies to resource insecurity. Using Inuit Nunangat (the Canadian Arctic) as the setting, models are developed to represent two adaptational strategies in response to the onset of the Little Ice Age: exchange with other communities via long-distance trade and intensification of local resource procurement. After determining the average kilograms of resources acquired through a model of local resource procurements, two models were then developed to determine under what scenarios long-distance journeys to procure perishable food goods would be more productive than hunting locally. Ultimately, the results showed that while there are scenarios where undertaking a trading journey would result in a higher average amount of resources acquired, those scenarios would not have been realistic for most Thule communities, leaving hunting locally as the more beneficial adaptational strategy on an economic basis.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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 it