Decisions and success of heterogeneous population of agents in learning to cross a highway
Bibliographic record
Abstract
We investigate the performance of a population of agents learning to cross a cellular automaton based highway. This performance is measured by mean values and their standard deviations of numbers of agents': (1) correct crossing decisions; (2) incorrect crossing decisions; (3) correct waiting decisions; (4) incorrect waiting decisions. Additionally, it is measured by mean values and their standard deviations of numbers of queued agents at simulation end. We study how agents' performance depends on the type of decision-making formula they use and on the presence of risk takers and of risk avoiders in the population of agents. We consider two decision-making formulas, one based on the assessment of both crossing and waiting decisions, and another one based only on the assessment of crossing decisions. We describe the simulation model focusing on the agents decision-making process and learning. The agents use an “observational social learning” strategy based on the observation of performance of other agents, mimicking what worked for them and avoiding what did not. Also, we investigate how accumulation of more information in agents' knowledge base affects agents' success in learning to cross the highway in homogeneous and heterogeneous populations of agents.
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How this classification was reachedexpand
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 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".