An Approach for Constructing Reliable Social Agent Based Systems Considering Dynamic Environment and Other Factors Affecting Their Progress.
Bibliographic record
Abstract
Construction of agent based model systems is often difficult considering the dynamic and complex nature of real world problems and various implicit factors affecting their behavior. This presents a problem in building accurate and valid systems for use as decision support tools. In this paper, we present an approach for handling some of these factors, specifically acceptance rate, retention rate and social influence, and enable simulated social agent models to evolve in a dynamic environment.The objective is to achieve a more reliable behavior and outcome for the agents in the simulation, despite unpredictable environmental changes. The agents’ knowledge, in terms of their observed responses to the environment and corresponding outcomes, is captured in a semantic tree. A metric is used to detect changes in the environment and the threshold of response of the agent, thereby triggering the agent to adapt its decision tree to maintain a reasonable response beyond its historical knowledge. The results reveal the ability of agents to detect changes in the environment more quickly and with better accuracy, using a case study, and as a result learn to adapt by modifying their decision tree under the influence of considered factors.
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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 | 0.005 | 0.018 |
| 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 | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".