Review: A Genetic Algorithm Approach to the Iterated Prisoner’s Dilemma
Bibliographic record
Abstract
Introduction: The use of machine learning tactics such as the Moran process and evolutionary finite state machines have the potential to outperform classic strategies for the iterated prisoner’s dilemma. Methods: Applying a genetic algorithm approach to the iterated prisoner’s dilemma while modeling strategies with finite state machines proved to be an efficient method in which the produced strategies were able to cooperate unilaterally with their opponents. Results: Varying parameters in the evolution process such as the amount of generations, population size and bottleneck size were shown to directly contribute to the success of the strategies produced. In comparing various optimization methods, the genetic algorithm utilizing finite state machines outperformed the Moran process with respect to the highest scoring strategies produced by each. Discussion: These results can be explained due to the benefits of genetic recombination that was made possible with the use of finite state machines, where crossing over of state / action pairs resembling choices to make depending on the state environment occurred from generation to generation. Due to natural selection and recombination, the genes of the strategies with the highest fitness levels were bred into the next generations. Population bottlenecks and gene mutation tactics were used to recreate the naturally occurring phenomenon of gene variation, resulting in the creation of new species (which are iterated prisoner’s dilemma finite state machine strategies) within the generations. Conclusion: Tangible tactics can be extracted from these strategies, which were evolved using standard genetic algorithm tactics utilizing gene crossover and mutation techniques.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".