Changing Resources Available to Game Playing Agents: Another Relevant Design Factor in Agent Experiments
Bibliographic record
Abstract
The iterated prisoner's dilemma is a simultaneous two-player game widely used in studies on cooperation and conflict. Recent research has demonstrated that a number of factors change the behavior of evolved agents in a manner not consistent with controlled studies. This study extends a preliminary exploration of the impact of changing the level of computational or informational resources available to game playing agents on their ensemble behavior. Both these categories of information are shown to have an impact on agent behavior. Four representations are studied: lookup tables, Markov chains, finite-state machines, and feed-forward neural nets. An assessment tool called the play profile is used to demonstrate that both the cooperativeness and the change in cooperativeness over evolutionary time are substantially different for different resource levels within a representational type. Lookup tables and neural nets are found to change the least when the resource levels they are presented with are varied, while Markov chains vary the most. Available internal resources are also found to change the competitive ability of agents as well as the rate at which they become cooperative as evolution proceeds.
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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 | 0.007 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".