Analyzing Decision-Making in Cognitive Agent Simulations Using Generalized Linear Mixed-Effects Models
Bibliographic record
Abstract
Enhancing model interpretability remains an ongoing challenge in predictive modelling, especially when applied to simulation data from complex systems. Investigating the influence and effects of design factors within computer simulations of complex systems requires assessing variable importance through statistical models. These models are crucial for capturing the relationships between factors and response variables. This study focuses on understanding functional patterns and their magnitudes of influence regarding designed factors affecting cognitive agent decision-making in a cellular automaton-based highway crossing simulation. We aim to identify the most influential design factors in the complex system simulation model to better understand the relationship between the decision outcomes and the designed factors. We apply Generalized Linear Mixed-Effects Models to explain the significant functional connections between designed factors and response variables, specifically quantifying variable importance. Our analysis demonstrates the practicality and effectiveness of the proposed models and methodologies for analyzing data from complex systems. The findings offer a deeper understanding of the connections between design factors and their resulting responses, facilitating a greater understanding of the underlying dynamics and contributing to the fields of applied mathematics, simulation modelling, and computation.
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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 | 0.012 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".