Understanding representational sensitivity in the iterated prisoner's dilemma with fingerprints
Bibliographic record
Abstract
The iterated prisoner's dilemma is a widely used computational model of cooperation and conflict. Many studies report emergent cooperation in populations of agents trained to play prisoner's dilemma with an evolutionary algorithm. This study varies the representation of the evolving agents resulting in levels of emergent cooperation ranging from 0% to over 90%. The representations used in this study are directly encoded finite-state machines, cellularly encoded finite-state machines, feedforward neural networks, if-skip-action lists, parse trees storing two types of Boolean functions, lookup tables, Boolean function stacks, and Markov chains. An analytic tool for rapidly identifying agent strategies and comparing across representations called a fingerprint is used to compare the more complex representations. Fingerprints provide functional signatures of an agent's strategy in a manner that is independent of the agent's representation. This study demonstrates conclusively that choice of a representation dominates agent behavior in evolutionary prisoner's dilemma. This in turn suggests that any soft computing system intended to simulate behavior must be concerned with the representation issue.
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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) | 0.000 | 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".