EVOLUTIONARY STRATEGY DYNAMICS FOR TAG-BASED COOPERATION AND DEFECTION IN THE SPATIAL AND ASPATIAL SNOWDRIFT GAME
Bibliographic record
Abstract
Cooperation is a costly behavior undertaken by one individual which benefits another individual. Since cooperators are easily exploited by defectors (those who receive the benefits of cooperation but do not cooperate themselves), the evolution and maintenance of cooperation rely on mechanisms that allow cooperators to interact with one another more frequently than would be predicted based on their relative abundance in a population. One simple mechanism is based on the recognition of "tags" — arbitrary, yet identifiable phenotypic traits. Tags allow for the existence of conditionally cooperative strategies; e.g. individuals could adopt a strategy whereby they cooperate with tag-mates but defect against non-tag-mates. Previous research has considered the tag and strategy dynamics of unconditional and conditional strategies engaged in the Prisoner's Dilemma game, the paradigmatic framework for studying the evolutionary dynamics of cooperation, in which defection against a cooperator yields the greatest fitness payoff, followed by mutual cooperation, mutual defection, and cooperation with a defector. Here, using complementary spatial and aspatial lattice models, an alternative payoff structure is considered, based on the Snowdrift game, in which the rankings of the payoffs associated with mutual defection and cooperation with a defector are reversed relative to the Prisoner's Dilemma. In the absence of mutation, it is demonstrated that the aspatial two-tag game tends to collapse into the traditional, non-tag-based Snowdrift game, with the frequency of cooperators and defectors predicted precisely by evolutionary dynamics analysis. The spatial two-tag game, on the other hand, produces a richer variety of outcomes, whose occurrence depends on the cost-benefit ratio of mutual cooperation; these outcomes include the dominance of conditional cooperators, the dominance of unconditional defectors, and the cyclic (or noncyclic) coexistence of the two. These outcomes are then shown to be modified by mutation (which softens the transition boundaries between outcomes), and by the presence of more than two tags (which promotes nepotistic conditional cooperation).
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".