ESS versus Nash: solving evolutionary games
Bibliographic record
Abstract
Question: Can a Nash solution concept be used for the analysis of evolutionary games? What are the advantages of ESS solutions over Nash solutions in evolutionary games? Mathematical methods: ESS and Nash equilibrium solution concepts and Darwinian dynamics for evolutionary games. Conclusions: An ESS contains the properties of Nash but not the other way around. The properties of evolutionary games make them fit poorly with the classical notion of a Nash solution. These properties include six key differences between the ESS and Nash concepts: (1) in the evolutionary game, players inherit rather than choose their strategies; (2) the focus of evolutionary games is on the strategies and not on the actual players who come and go via births and deaths; (3) the payoffs in the evolutionary game represent fitness, creating a dynamical link between payoffs and changes in the frequency of strategies; (4) in state-dependent games, players in a classical game may possess forethought and anticipate the con-sequences of their actions, whereas in the evolutionary game organisms do not; (5) unlike classical games, the actual number of players in the game can expand and contract via changes
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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 | 0.000 | 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) | 0.001 | 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".