A simulation framework for design-oriented studies of interaction models in agent teamwork
Bibliographic record
Abstract
This paper introduces a new software framework for design-oriented simulation studies of interaction models used in agent teamwork. The framework provides a generic simulator that can be instantiated with concrete multiagent system (MAS) models to obtain custom simulators for specific experimental studies. The main purpose of such a custom simulator is to reduce the design decision space through feedback from experiments performed during the early stages of interaction model development. The combined emphases on design-oriented early feedback, low coupling between the MAS models and the simulation environment, openness towards external systems, extendibility, and distributed execution have resulted in a novel architecture which is the main contribution of the paper. An essential feature that facilitates early feedback is the interactive concurrent simulation of multiple teams, with immediate visualization. It enables the experimenter to control the experiment scenario in progress and simulation parameters while observing the behavior and performance of several teams that employ alternative design solutions. The framework also supports the distribution of runs of the same experiment across a potentially large number of nodes in a computing cluster.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".