Could feedback-based self-learning help solve networked Prisoner's Dilemma?
Bibliographic record
Abstract
We present a self-learning evolutionary Prisoner's Dilemma game model to study the evolution of cooperation in network-structured populations. During the evolutionary process, each agent updates its current strategy with a probability depending on the difference feedback between its actual score and score aspiration. Each agent's score is a weighed mean of its payoff coming from its neighbors (social partners) and the payoff of its social partners obtaining from it. Simulation results show that the cooperation level in the structured populations increases with increasing the weight of partners' obtaining payoff in the score. More interestingly, we find that very similar evolution of cooperation can respectively emerge in lattice, small-world and scale-free networks under the learningfeedback updating rule. Moreover, we provide theoretical analysis and qualitative explanations for these numerical simulations. Our work may provide an effective way to solve the dilemma of cooperation for structured populations.
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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 | 0.001 | 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 | 0.001 | 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) | 0.001 | 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".