A methodology for building agent-base simulations of common-pool resources management : from a conceptual model designed with UML to its implementation in CORMAS
Bibliographic record
Abstract
Since 1995, our team has been developing a simulation platform called CORMAS (common-pool resources and multi-agent systems). It provides facilities to build and analyze agent-based models (ABMs) that represent ecosystems where various human activities compete for the use of natural resources. Few agent-based simulations can be mathematically proven, but they can be analyzed inductively. It is therefore important that simulations be replicated before they are accepted as correct. To tackle this thorny issue of ABM replication, we believe that, during the design process, a careful representation of the conceptual model is paramount. In this paper, we advocate using UML (unified modeling language), which is a formal language to describe systems using the object oriented paradigm. An archetypical agroforestry system is presented here, and serves as an example to design a very simple model dealing with common-pool resources management. Different types of UML diagrams are also introduced to describe the static structure of the model, as well as that of the dynamic processes. Adaptation of these diagrams for implementation using the CORMAS platform is detailed. Then, a simple simulation scenario is presented to illustrate how it is done in CORMAS, and a sensitivity analysis on one parameter of the model is conducted.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".