Trends in Coupled Human-Environment Systems Modelling: A Scoping Review
Bibliographic record
Abstract
Abstract Classical environmental models assume the influence of humans on environmental systems is constant. However, human and environmental systems respond to one another. As such, coupled human-environment systems (CHES) models have been developed and are becoming more widely studied. In this review, we analyze CHES modelling techniques and study systems over a decade (May 2009-April 2019). We utilized the PRISMA method to filter publications from both Web of Knowledge and PUBMED, yielding 92 relevant papers for our review. Publications more than doubled from the 5-year interval May 2009-December 2013 (28/92) to the 5-year interval January 2014-April 2019 (64/92). CHES models typically used either differential equations (DEs) (44/92) or agent-based models (ABMs) (28/92). We organized the included literature with respect to the technique used to represent human behaviour. We noticed a diversity of approaches in this respect, but primarily optimization techniques (28/92) and game theory (34/92). We noticed a substantial increase in publications using more highly structured models in the second 5-year interval. We attribute this to reduced technological barriers to developing more detailed models, and greater data availability. We discuss the realism of the models and their ability to capture real-world dynamics. Finally, we explore avenues for future research, and discuss unconventional routes such as online communities and artificial intelligence modelling to expand representation of human behaviour in CHES models.
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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 | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".