Hierarchical Bayesian inference and spatial validation of socio-ecological system dynamics models: participatory modelling for Indigenous smallholder agriculture and food security in Guatemala
Bibliographic record
Abstract
Systems dynamics modelling is often used as a participatory modelling tool to model the long-term dynamics of socio-ecological systems, as well as to help in developing integrated policy decisions that take into account the unexpected and complex system behaviours that are often caused by the dynamic feedbacks between ecology and society. Actual use of these models in decision-making is, however, hindered by the frequent lack of high-quality temporal data on many key socioeconomic (and environmental) variables, which makes the application of traditional system dynamics model evaluation techniques difficult. This situation is particularly pronounced in the context of many Indigenous communities around the world, regions where improved access to decision support tools such as system dynamics modelling could be of particular use for supporting communities in their quest to make (and have implemented) their own resource management decisions. In the absence of rigorous quantification methods, however, these models are difficult to build and trust. In this research, we present a novel methodology for calibrating hard-to-quantify relationships between socioeconomic variables of systems dynamics models. Based on hierarchical Bayesian inference, the methodology allows for the use of spatially explicit (but temporally poor) datasets to infer the quantitative, numerical relationships between socioeconomic variables, even when data in the precise region of interest is very scarce. We present, as a case study, a system dynamics model of small-scale agricultural systems and food security in two different regions of Guatemala (Tz'olöj Ya' and K'iche'), and analyse the impacts of different proposed policies in the face of socioeconomic shocks and water stress due to projected climate change. The hierarchical Bayesian inference calibration method allowed for the inference of key socioeconomic parameter values in a spatially explicit context to compensate for data scarcity, while spatial validation indicated which regions of the country the model was appropriate for. Such a methodology, once incorporated into user-friendly system dynamics software, has the potential to facilitate participatory sociohydrological modelling even in quite data-scarce regions where modellers, up until now, have had to rely on educated guesses for the majority of the model's calibration.
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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 | 0.010 | 0.030 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".