Improving prediction accuracy in agent based modeling systems under dynamic environment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Considering the dynamic and complex nature of real systems, it is not easy to build an accurate artificial simulation. Agent Based Modeling Simulations used to build such simulated models are often oversimplified and not realistic enough to predict reliable results. In addition to this, the validation of such Agent Based Model (ABM) involves great difficulties thus putting a question mark on their effective usage and acceptability. One of the major problems affecting the reliability of ABM addressed in this work is the dynamic nature of the environment. An ABM initially validated at a given time stamp is bound to become invalid with the inevitable change in the environment over time. Thus, an ABM that does not learn regularly from its environment cannot sustain its validity over a longer period of time. It should therefore have the ability to absorb changes in the environment upon their detection. Thus, in this paper we present a novel approach for incorporating adaptability and learning in an ABM simulation, thereby making it capable to be consistently synchronized with the changing environment and provide reliable results. One phase of our method explores the use of Data Mining (DM) in ABM for detecting environment trends and dynamics. Another phase addresses different methods for finding similarity between the knowledge represented by two different decision trees, for detecting a change in the simulation's environment.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it