Creating Agents and Landscapes for Multiagent Systems from Random Samples
Bibliographic record
Abstract
An important goal of modeling human-environment interactions is to provide scientific information to policymakers and stakeholders in order to better support their planning and decision-making processes.Modern technologies in the fields of GIS and data processing, together with an increasing amount of accessible information, have the potential to meet the varying information needs of policymakers and stakeholders.Multiagent modeling holds the promise of providing an enhanced collaborative framework in which planners, modelers, and stakeholders may learn and interact.The fulfillment of this promise, however, depends on the empirical parameterization of multiagent models.Although multiagent models have been widely applied in experimental and hypothetical settings, only few studies have strong linkages to empirical data and the literature on methods of empirical parameterization is still limited.This paper presents a straightforward approach to parameterize multiagent models in applied development research.The parameterization uses a common sampling frame to randomly select observation units for both biophysical measurements and socioeconomic surveys.The biophysical measurements, i.e., soil properties in this study, are then extrapolated over the landscape using multiple regressions and a digital elevation model.The socioeconomic surveys are used to estimate probability functions for key characteristics of human actors, which are then assigned to the model agents with Monte Carlo techniques.This approach generates a landscape and agent populations that are robust and statistically consistent with empirical observations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".