Bibliographic record
Abstract
A deck-based game is a modification of a game that normally permits the players to use any number of moves of any type. This freedom of choice of moves is limited by handing each player a deck of cards, each of which with a single move printed on it. The player must then play from their deck rather than simply choosing the moves. This study documents that deck-based iterated prisoner's dilemma is radically different from standard prisoner's dilemma when the entire deck must be expended during play. The restrictions imposed by the deck change the game into a coordination game or an anti-coordination game. The game is shown to transform smoothly into standard prisoner's dilemma as the fraction of the deck used in play is reduced, assuming that a constant ratio of the two types of moves are used in the deck. The size of the deck, ratio of defects to cooperates, and evolutionary algorithm parameters are all studied using a string based representation. An adaptive agent representation is also developed, based on augmented finite state machines called deck automata. Deck automata evolve to play the game more effectively than the string based agents for three different situations; experiments in which agents expend all, three-quarters, or half the available cards.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".